Spatial Seemingly Unrelated Regression Models.

Description

spsur offers the user a collection of functions to estimate Spatial Seemingly Unrelated Regression (SUR) models by maximum likelihood or three-stage least squares, using spatial instrumental variables. Moreover, spsur obtains a collection of misspecification tests for omitted or wrongly specified spatial structure. The user will find spatial models more popular in applied research such as the SUR-SLX, SUR-SLM, SUR-SEM, SUR-SDM, SUR-SDEM SUR-SARAR and SUR-GNM plus the spatially independent SUR, or SUR-SIM.

Details

Some functionalities that have been included in spsur package are:

1. Testing for spatial effects

The function lmtestspsur provides a collection of Lagrange Multipliers, LM, for testing different forms of spatial dependence in SUR models. They are extended versions of the well-known LM tests for omitted lags of the explained variable in the right hand side of the equation, LM-SLM, the LM tests for omitted spatial errors, LM-SEM, the join test of omitted spatial lags and spatial errors, LM-SARAR, and the robust version of the firt two Lagrange Multipliers, LM*-SLM and LM*-SEM.
These tests can be applied to models always with a SUR nature. Roughly, we may distinguish two situations:

• Datasets with a single equation G=1, for different time periods Tm>1 and a certain number of spatial units in the cross-sectional dimension, N. This is what we call spatial panel datasets. In this case, the SUR structure appears in form of (intra) serial dependence in the errors of each spatial unit.

• Datasets with a several equations G>1, different time periods Tm>1 and a certain number of spatial units, N. The SUR structure appears, as usual, because the errors of the spatial units for different equations are contemporaneously correlated.

2. Estimation of the Spatial SUR models

As indicated above, spsur package may work with a list of different spatial specifications. They are the following:

• SUR-SIM: SUR model without spatial effects

  y_{tg} = X_{tg} \beta_{g} + \epsilon_{tg}

• SUR-SLX: SUR model with spatial lags of the regresors

  y_{tg} = X_{tg} \beta_{g} + WX_{tg} \theta_{g} + \epsilon_{tg}

• SUR-SLM: SUR model with spatial lags of the endogenous

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + \epsilon_{tg}

• SUR-SEM: SUR model with spatial errors

  y_{tg} = X_{tg} \beta_{g} + u_{tg}

  u_{tg} = \lambda_{g} Wu_{tg} + \epsilon_{tg}

• SUR-SDM: SUR model with spatial lags of the endogenous variable and of the regressors or Spatial Durbin model

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + WX_{tg} \theta_{g} + \epsilon_{tg}

• SUR-SDEM: SUR model with spatial errors and spatial lags of the endogenous variable and of the regressors

  y_{tg} = X_{tg} \beta_{g} + WX_{tg} \theta_{g} + u_{tg}

  u_{tg} = \lambda_{g} W u_{tg} + \epsilon_{tg}

• SUR-SARAR: Spatial lag model with spatial errors

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + u_{tg}

  u_{tg} = \lambda_{g} W u_{tg} + \epsilon_{tg}

• SUR-GNM: SUR model with spatial lags of the explained variables, regressors and spatial errors

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + WX_{tg} \theta_{g} + u_{tg}

  u_{tg} = \lambda_{g} W u_{tg} + \epsilon_{tg}

where y_{tg}, u_{tg} and \epsilon_{tg} are (Nx1) vectors; X_{tg} is a matrix of regressors of order (NxP); \rho_{g} and \lambda_{g} are parameters of spatial dependence and W is the (NxN) spatial weighting matrix.

These specifications can be estimated by maximum-likelihood methods, using the function spsurml. Moroever, the models that include spatial lags of the explained variables in the right hand side of the equations, and the errors are assumed to be spatially incorrelated (namely, the SUR-SLM and the SUR-SDM), can also be estimated using three-stage least-squares, spsur3sls, using spatial instrumental variable to correct for the problem of endogeneity present in these cases.

3. Diagnostic tests

Testing for inconsistencies or misspecifications in the results of an estimated (SUR) model should be a primary task for the user. spsur focuses, especifically, on two main question such as omitted or wrongly specified spatial structure and the existence of structural breaks or relevant restrictions in the parameters of the model. In this sense, the user will find:

1. Marginal tests
   The Marginal Multipliers test for omitted or wrongly specified spatial structure in the equations. They are routinely part of the output of the maximum-likelihood estimation, shown by spsurml. In particular, the LM(\rho|\lambda) tests for omitted spatial lags in a model specified with spatial errors (SUR-SEM; SUR-SDEM). The LM(\lambda|\rho) tests for omitted spatial error in a model specified with spatial lags of the explained variable (SUR-SLM; SUR-SDM).

2. Coefficients stability tests
   spsur includes two functions designed to test for linear restrictions on the \beta coefficients of the models and on the spatial coefficients (\rhos and \lambdas terms). The function for the first case is wald_betas and wald_deltas that of the second case. The user has ample flexibility to define different forms of linear restrictions, so that it is possible, for example, to test for their time constancy to identify structural breaks.

4. Marginal effects

In recent years, since the publication of LeSage and Pace (2009), it has become popular in spatial econometrics to evaluate the multiplier effects that a change in the value of a regressor, in a point in the space, has on the explained variable. spsur includes a function, impactspsur, that computes these effects. Specifically, impactspsur obtains the average, over the N spatial units and Tm time periods, of such a change on the contemporaneous value of the explained variable located in the same point as the modified variable. This is the so-called Average Direct effect. The Average Indirect effect measure the proportion of the impact that spills-over to other locations. The sum of the two effects is the Average Total effect.
These estimates are complemented with a measure of statistical significance, following the randomization approach suggested by LeSage and Pace (2009).

5. Additional functionalities

A particular feature of spsur is that the package allows to obtain simulated datasets with a SUR nature and the spatial structure decided by the user. This is the purpose of the function dgp_spsur. The function can be inserted into a more general code to solve, for example, Monte Carlo studies related to these type of models or, simply, to show some of the stylized characteristics of a SUR model with certain spatial structure.

Datasets

spsur includes three different datasets: spc, NCOVR and spain.covid. These sets are used to illustrate the capabilities of different functions. Briefly, their main characteristics are the following

• The spc dataset (Spatial Phillips-Curve) is a classical dataset taken from Anselin (1988, p. 203), of small dimensions.

• The NCOVR dataset comprises Homicides and a list of selected socio-economic variables for continental U.S. counties in four decennial census years: 1960, 1970, 1980 and 1990. It is freely available from https://geodacenter.github.io/data-and-lab/ncovr/. NCOVR is a typical spatial panel dataset (G=1).

• The spain.covid dataset comprises Within and Exit mobility index together with the weeklly incidence COVID-19 at Spain provinces from February 21 to May 21 2020.

Author(s)

References

• Breusch T, Pagan A (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies 47: 239-254.

• LeSage, J., and Pace, R. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC.

• Lopez, F.A., Mur, J., and Angulo, A. (2014). Spatial model selection strategies in a SUR framework. The case of regional productivity in EU. Annals of Regional Science, 53(1), 197-220. <doi:10.1007/s00168-014-0624-2>

• Lopez, F.A., Martinez-Ortiz, P.J., and Cegarra-Navarro, J.G. (2017). Spatial spillovers in public expenditure on a municipal level in Spain. Annals of Regional Science, 58(1), 39-65. <doi:10.1007/s00168-016-0780-7>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

• Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>

See Also

Useful links:

• https://CRAN.R-project.org/package=spsur

• Report bugs at https://github.com/rominsal/spsur/issues

Homicides in U.S. counties

Description

Homicides and selected socio-economic characteristics for continental U.S. counties. Data for four decennial census years: 1960, 1970, 1980 and 1990.

Usage

NCOVR.sf

Format

An spatial feature (sf) object with 3085 rows and 41 variables:

NAME

County coded as a name (factor)

STATE_NAME

state fips code (factor)

FIPS

state fips code (factor)

SOUTH

dummy variable for Southern counties (South = 1)

HR60, HR70, HR80, HR90

homicide rate per 100,000 (1960, 1970, 1980, 1990)

HC60, HC70, HC80, HC90

homicide count, three year average centered on 1960, 1970, 1980, 1990

PO60, PO70, PO80, PO90

county population, 1960, 1970, 1980, 1990

RD60, RD70, RD80, RD90

resource deprivation 1960, 1970, 1980, 1990 (principal component, see Codebook for details)

PS60, PS70, PS80, PS90

population structure 1960, 1970, 1980, 1990 (principal component, see Codebook for details)

UE60, UE70, UE80, UE90

unemployment rate 1960, 1970, 1980, 1990

DV60, DV70, DV80, DV90

divorce rate 1960, 1970, 1980, 1990 (% males over 14 divorced)

MA60, MA70, MA80, MA90

median age 1960, 1970, 1980, 1990

FP59, FP69, FP79, FP89

% families below poverty 1960, 1970, 1980, 1990 (see Codebook for details)

geometry

Multipolygon geometry of the spatial feature object

Source

S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the Spatial Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, National Consortium on Violence Research (NCOVR) https://geodacenter.github.io/data-and-lab/ncovr/

References

• Baller, R., L. Anselin, S. Messner, G. Deane and D. Hawkins (2001). Structural covariates of US county homicide rates: incorporating spatial effects. Criminology 39, 561-590.

Spatial weight matrix for South-West Ohio Counties to estimate Spatial Phillips-Curve

Description

A spatial weight matrix row-standardized based on first order contiguity criterium.

Usage

Wspc

Format

A row-standardized squared matrix with 25 rows and columns. The rows and columns follow the same order than provinces included in spc data frame.

Source

Anselin (1988, p. 207)

References

• Anselin, L. (1988). Spatial Econometrics: Methods and Models. Springer Science & Business Media.

Generation of a random dataset with a spatial SUR structure.

Description

The purpose of the function dgp_spsur is to generate a random dataset with the dimensions and spatial structure decided by the user. This function may be useful in pure simulation experiments or with the aim of showing specific properties and characteristics of a spatial SUR dataset and inferential procedures related to them.

The user of dgp_spsur should think in terms of a Monte Carlo experiment. The arguments of the function specify the dimensions of the dataset to be generated, the spatial mechanism underlying the data, the intensity of the SUR structure among the equations and the values of the parameters to be used to obtain the simulated data, which includes the error terms, the regressors and the explained variables.

Usage

dgp_spsur(Sigma, Tm = 1, G, N, Betas, Thetas = NULL, 
                 rho = NULL, lambda = NULL, p = NULL, listw = NULL, 
                 X = NULL, type = "matrix", pdfU = "nvrnorm", 
                 pdfX = "nvrnorm")

Arguments

Details

The purpose of the function dgp_spsur is to generate random datasets, of a SUR nature, with the spatial structure decided by the user. The function requires certain information to be supplied externally because, in fact, dgp_spsur constitutes a Data Generation Process, DGP. The following aspects should be addressed:

• The user must define the dimensions of the dataset, that is, number of equations, G, number of time periods, Tm, and number of cross-sectional units, N.

• The user must choose the type of spatial structure desired for the model from among the list of candidates of "sim", "slx", "slm", "sem", "sdm", "sdem" or "sarar". The default is the "sim" specification which does not have spatial structure. The decision is made implicitly, just omitting the specification of the spatial parameters which are not involved in the model (i.e., in a "slm" there are no \lambda parameters but appear \rho parameters; in a "sdem" model there are \lambda and \theta parameters but no \rho coefficients).

• If the user needs a model with spatial structure, a (NxN) weighting matrix, W, should be chosen.

• The next step builds the equations of the SUR model. In this case, the user must specify the number of regressors that intervene in each equation and the coefficients, \beta parameters, associated with each regressor. The first question is solved through the argument p which, if a scalar, indicates that the same number of regressors should appear in all the equations of the model; if the user seeks for a model with different number of regressors in the G equations, the argument p must be a (1xG) row vector with the required information. It must be remembered that dgp_spsur assumes that an intercept appears in all equations of the model.

  The second part of the problem posited above is solved through the argument Betas, which is a row vector of order (1xp) with the information required for this set of coefficients.

• The user must specify, also, the values of the spatial parameters corresponding to the chosen specification; we are referring to the \rho_{g}, \lambda_{g} and \theta_{g}, for g=1, ..., G and k=1,..., K_{g} parameters. This is done thought the arguments rho, lambda and theta. The firs two, rho and lambda, work as K: if they are scalar, the same value will be used in the G equations of the SUR model; if they are (1xG) row vectors, a different value will be assigned for each equation.

  Moreover, Theta works like the argument Betas. The user must define a row vector of order 1xPTheta showing these values. It is worth to remember that in no case the intercept will appear among the lagged regressors.

• With the argument type the user take the decision of the output format. See Value section.

• Finally, the user must decide which values of the regressors and of the error terms are to be used in the simulation. The regressors can be uploaded from an external matrix generated previously by the user. This is the argument X. It is the responsibility of the user to check that the dimensions of the external matrix are consistent with the dataset required for the SUR model. A second possibility implies the regressors to be generated randomly by the function dgp_spsur. In this case, the user must select the probability distribution function from which the corresponding data (of the regressors and the error terms) are to be drawn.
  

dgp_spsur provides two multivariate distribution functions, namely, the Normal and the log-Normal for the errors (the second should be taken as a clear departure from the standard assumption of normality). In both cases, random matrices of order (TmNxG) are obtained from a multivariate normal distribution, with a mean value of zero and the covariance matrix specified in the argument Sigma; then, this matrix is exponentiated for the log-Normal case. Roughly, the same procedure applies for drawing the values of the regressor. There are two distribution functions available, the normal and the uniform in the interval U[0,1]; the regressors are always independent.

Value

The default output ("matrix") is a list with a vector Y of order (TmNGx1) with the values generated for the explained variable in the G equations of the SUR and a matrix XX of order ((TmNGxsum(p)), with the values generated for the regressors of the SUR, including an intercept for each equation.

In case of Tm = 1 or G = 1 several alternatives output can be select:

• If the user select type = "df" the output is a data frame where each column is a variable.

• If the user select type = "panel" the output is a data frame in panel format including two factors. The first factor point out the observation of the individual and the second the equation for different Tm or G.

• Finally, if type = "all" is select the output is a list including all alternatives format.

Author(s)

References

• Lopez, F. A., Minguez, R., Mur, J. (2020). ML versus IV estimates of spatial SUR models: evidence from the case of Airbnb in Madrid urban area. The Annals of Regional Science, 64(2), 313-347. <doi:10.1007/s00168-019-00914-1>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

See Also

spsurml, spsur3sls, spsurtime

Examples

## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_dgp_spsur, package="spsur")

################################################
### PANEL DATA (Tm = 1 or G = 1)              ##
################################################

################################################
#### Example 1: DGP SLM model. G equations
################################################
rm(list = ls()) # Clean memory
Tm <- 1 # Number of time periods
G <- 3 # Number of equations
N <- 200 # Number of spatial elements
p <- 3 # Number of independent variables
Sigma <- matrix(0.3, ncol = G, nrow = G)
diag(Sigma) <- 1
Betas <- c(1, 2, 3, 1, -1, 0.5, 1, -0.5, 2)
rho <- 0.5 # level of spatial dependence
lambda <- 0.0 # spatial autocorrelation error term = 0
##  random coordinates
co <- cbind(runif(N,0,1),runif(N,0,1))
lw <- spdep::nb2listw(spdep::knn2nb(spdep::knearneigh(co, k = 5,
                                                   longlat = FALSE)))
DGP <- dgp_spsur(Sigma = Sigma, Betas = Betas,
                 rho = rho, lambda = lambda, Tm = Tm,
                 G = G, N = N, p = p, listw = lw)

SLM <- spsurml(X = DGP$X, Y = DGP$Y, Tm = Tm, N = N, G = G, 
               p = c(3, 3, 3), listw = lw, type = "slm") 
summary(SLM)

################################################
### MULTI-DIMENSIONAL PANEL DATA G>1 and Tm>1 ##
################################################

rm(list = ls()) # Clean memory
Tm <- 10 # Number of time periods
G <- 3 # Number of equations
N <- 100 # Number of spatial elements
p <- 3 # Number of independent variables
Sigma <- matrix(0.5, ncol = G, nrow = G)
diag(Sigma) <- 1
Betas <- rep(1:3, G)
rho <- c(0.5, 0.1, 0.8)
lambda <- 0.0 # spatial autocorrelation error term = 0
## random coordinates
co <- cbind(runif(N,0,1),runif(N,0,1))
lw <- spdep::nb2listw(spdep::knn2nb(spdep::knearneigh(co, k = 5,
                                                   longlat = FALSE)))
DGP4 <- dgp_spsur(Sigma = Sigma, Betas = Betas, rho = rho, 
                  lambda = lambda, Tm = Tm, G = G, N = N, p = p, 
                  listw = lw)
SLM4  <- spsurml(Y = DGP4$Y, X = DGP4$X, G = G, N = N, Tm = Tm,
                 p = p, listw = lw, type = "slm")
summary(SLM4)

Direct, indirect and total effects estimated for a spatial SUR model

Description

This function is a wrapper for impacts method used in spatialreg package. Nevertheless, in this case the same method is used for both lagsarlm and lmSLX objects. For details of implementation, see the documentation of impacts function in spatialreg package.
The function obtains the multiplier effects, on the explained variable, of a change in a regressor for the model that has been estimated. For reasons given below, this function only applies to models with an autoregressive structure ("slm", "sdm", "sarar" and "gnm") or with spatial lags of the regressors ("slx", "sdem").
The measurement of the multiplier effects is a bit more complicated than in a pure time series context because, due to the spatial structure of the model, part of the impacts spills over non uniformly over the space. Using the notation introduced by LeSage and Pace (2009) we distinguish between:

• Average Direct effects: The average over the N spatial units and Tm time periods of the effect of a unitary change in the value of a explanatory variable on the contemporaneous value of the corresponding explained variable, located in the same point of the intervened regressor. This calculus is solved for all the regressors that appear in the G equations of the model.

• Average Indirect effects: The average over the N spatial units and Tm time periods of the effects of a unitary change in the value of a explanatory variable on the contemporaneous value of the corresponding explained variable, located in a different spatial unit that that of the intervened regressor. This calculus is solved for all the regressors that appear in the G equations of the model.

• Average total effects: The sum of Direct and Indirect effects.

The information on the three estimated effects is supplement with an indirect measure of statistical significance obtained from the randomization approach introduced in LeSage and Pace (2009).

Usage

impactspsur (obj, ..., tr = NULL, R = NULL, listw = NULL, 
                      evalues = NULL,tol = 1e-06, 
                      empirical = FALSE, Q = NULL)

Arguments

Details

LeSage and Pace (2009) adapt the classical notion of 'economic multiplier' to the problem of measuring the impact that a unitary change in the value of a regressor, produced in a certain point in space, has on the explained variable. The question is interesting because, due to the spatial structure of the model, the impacts of such change spill non uniformly over the space. In fact, the reaction of the explained variable depends on its relative location in relation to the point of intervention.
To simplify matters, LeSage and Pace (2009) propose to obtain aggregated multipliers for each regressor, just averaging the N^{2} impacts that results from intervening the value of each regressor on each of the N points in Space, on the explained variable, measured also in each of the N points in space. This aggregated average is the so-called Total effect.
Part of this impact will be absorved by the explained variable located in the same point of the regressor whose value has been changed (for example, the k-th regresor in the g-th equation, in the n-th spatial unit) or, in other words, we expect that [d y_{tgn}]/[d x_{ktgn}] ne 0. The aggregated average for the N points in space (n=1,2,...,N) and Tm time periods is the so-called Direct effect. The difference between the Total effect and the Direct effect measures the portion of the impact on the explained variable that leakes to other points in space, [d y_{tgn}]/[d x_{ktgm}] for n ne m; this is the Indirect effect.

impactspsur obtains the three multipliers together with an indirect measure of statistical significance, according to the randomization approach described in Lesage and Pace (2009). Briefly, they suggest to obtain a sequence of nsim random matrices of order (NTmxG) from a multivariate normal distribution N(0; Sigma), being Sigma the estimated covariance matrix of the G equations in the SUR model. These random matrices, combined with the observed values of the regressors and the estimated values of the parameters of the corresponding spatial SUR model, are used to obtain simulated values of the explained variables. Then, for each one of the nsim experiments, the SUR model is estimated, and the effects are evaluated. The function impactspsur obtains the standard deviations of the nsim estimated effects in the randomization procedure, which are used to test the significance of the estimated effects for the original data.

Finally, let us note that this is a SUR model where the G equations are connected only through the error terms. This means that if we intervene a regressor in equation g, in any point is space, only the explained variable of the same equation g should react. The impacts do not spill over equations. Moreover, the impact of a regressor, intervened in the spatial unit n, will cross the borders of this spatial unit only if in the right hand side of the equation there are spatial lags of the explained variables or of the regressors. In other words, the Indirect effect is zero for the "sim" and "sem" models. impactspsur produces no output for these two models. Lastly, it is clear that all the impacts are contemporaneous because the equations in the SUR model have no time dynamics.

Value

A list of G objects either of class lagImpact or WXImpact.

For each of the G objects of the list, if no simulation is carried out the object returned is a list with:

and a matching Qres list attribute if Q was given.

On the other hand, for each of the G objects of the list, if simulation is carried out the object returned is a list with:

Author(s)

References

• Bivand, R.S. and Piras G. (2015). Comparing Implementations of Estimation Methods for Spatial Econometrics. Journal of Statistical Software, 63(18), 1-36. <doi: 10.18637/jss.v063.i18>

• LeSage, J., and Pace, R. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC.

• Lopez, F.A., Mur, J., and Angulo, A. (2014). Spatial model selection strategies in a SUR framework. The case of regional productivity in EU. Annals of Regional Science, 53(1), 197-220. <doi:10.1007/s00168-014-0624-2>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

• Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>

See Also

impacts, spsurml, spsur3sls

Examples

## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_impactspsur, package="spsur")


###############################################
### PURE CROSS SECTIONAL DATA(G>1; Tm=1) ######
###############################################

#### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
 rm(list = ls()) # Clean memory
 data(spc)
 lwspc <- spdep::mat2listw(Wspc, style = "W")
 Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
## For SLM, SDM and SARAR models the output is a list of "lagImpact" objects
## See spatialreg::impacts for details.
 spcsur_slm <-spsurml(formula = Tformula, data = spc, 
                      type = "slm", listw = lwspc)
 summary(spcsur_slm)
 impacts_slm <- impactspsur(spcsur_slm, listw = lwspc, R = 1000)
## Impacts equation 1
 summary(impacts_slm[[1]], zstats = TRUE, short = TRUE)
## Impacts equation 2
 summary(impacts_slm[[2]], zstats = TRUE, short = TRUE)
## For SLX and SDEM models the output is a list of "WXImpact" objects
## See spatialreg::impacts for details.
## A SUR-SLX model
 spcsur_slx <-spsurml(formula = Tformula, data = spc, 
                      type = "slx", listw = lwspc)
 summary(spcsur_slx)
 impacts_slx <- impactspsur(spcsur_slx, listw = lwspc)
 summary(impacts_slx[[1]], zstats = TRUE, short = TRUE)
 summary(impacts_slx[[2]], zstats = TRUE, short = TRUE)

## A SUR-SDM model
 spcsur_sdm <-spsurml(formula = Tformula, data = spc, 
                      type = "sdm", listw = lwspc)
 impacts_sdm <- impactspsur(spcsur_sdm, listw = lwspc, R = 1000)
 summary(impacts_sdm[[1]], zstats = TRUE, short = TRUE)
 summary(impacts_sdm[[2]], zstats = TRUE, short = TRUE)
## A SUR-SDM model with different spatial lags in each equation
 TformulaD <- ~ UN83 + NMR83 + SMSA | UN80
 spcsur_sdm2 <-spsurml(formula = Tformula, data = spc, type = "sdm", 
                       listw = lwspc, Durbin = TformulaD)
 summary(spcsur_sdm2)                       
 impacts_sdm2 <- impactspsur(spcsur_sdm2, listw = lwspc, R = 1000)
 summary(impacts_sdm2[[1]], zstats = TRUE, short = TRUE)
 summary(impacts_sdm2[[2]], zstats = TRUE, short = TRUE)
 ## A SUR-SLX model with different spatial lags in each equation
 spcsur_slx2 <-spsurml(formula = Tformula, data = spc, 
                      type = "slx", listw = lwspc, Durbin = TformulaD)
 summary(spcsur_slx2)
 impacts_slx2 <- impactspsur(spcsur_slx2, listw = lwspc)
 summary(impacts_slx2[[1]], zstats = TRUE, short = TRUE)
 summary(impacts_slx2[[2]], zstats = TRUE, short = TRUE)
 
# ####################################
# ######## G=1; Tm>1               ###
# ####################################
#
 rm(list = ls()) # Clean memory
 data(NCOVR, package="spsur")
 nbncovr <- spdep::poly2nb(NCOVR.sf, queen = TRUE)
### Some regions with no links...
 lwncovr <- spdep::nb2listw(nbncovr, style = "W", zero.policy = TRUE)
 Tformula <- HR80  | HR90 ~ PS80 + UE80 | PS90 + UE90
### A SUR-SLM model
 NCOVRSUR_slm <-spsurml(formula = Tformula, data = NCOVR.sf, 
                        type = "slm", listw = lwncovr, 
                        method = "Matrix", zero.policy = TRUE, 
                        control = list(fdHess = TRUE))
 summary(NCOVRSUR_slm)
### Use of trW to compute.
 Wncovr <- Matrix::Matrix(spdep::listw2mat(lwncovr))
 trwncovr <- spatialreg::trW(Wncovr, type = "MC")
 impacts_NCOVRSUR_slm <- impactspsur(NCOVRSUR_slm, tr = trwncovr,
                                 R = 1000)
 summary(impacts_NCOVRSUR_slm[[1]], zstats = TRUE, short = TRUE)
 summary(impacts_NCOVRSUR_slm[[2]], zstats = TRUE, short = TRUE)

Testing for the presence of spatial effects in Seemingly Unrelated Regressions

Description

The function spsurml reports a collection of Lagrange Multipliers designed to test for the presence of different forms of spatial dependence in a SUR model of the "sim" type. That is, the approach of this function is from 'specific to general'. As said, the model of the null hypothesis is the "sim" model whereas the model of the alternative depends on the effect whose omission we want to test.

The collection of Lagrange Multipliers obtained by lmtestspsur are standard in the literature and take into account the multivariate nature of the SUR model. As a limitation, note that each Multiplier tests for the omission of the same spatial effects in all the cross-sections of the G equations.

Usage

lmtestspsur(...)

## S3 method for class 'formula'
lmtestspsur(
  formula,
  data,
  listw,
  na.action,
  time = NULL,
  Tm = 1,
  zero.policy = NULL,
  R = NULL,
  b = NULL,
  ...
)

## Default S3 method:
lmtestspsur(Y, X, G, N, Tm = 1, listw, p, R = NULL, b = NULL, ...)

Arguments

Details

lmtestspsur tests for the omission of spatial effects in the "sim" version of the SUR model:

y_{tg} = X_{tg} \beta_{g} + u_{tg}

E[u_{tg}u_{th}']= \sigma_{gh}I_{N} \quad E[u_{tg}u_{sh}']= 0 \mbox{ if } t ne s

where y_{tg} and u_{tg} are (Nx1) vectors, corresponding to the g-th equation and time period t; X_{tg} is the matrix of exogenous variables, of order (Nxp_{g}). Moreover, \beta_{g} is an unknown (p_{g}x1) vector of coefficients and \sigma_{gh}I_{N} the covariance between equations g and h, being \sigma_{gh} and scalar and I_{N} the identity matrix of orden N.

The Lagrange Multipliers reported by this function are the followings:

• LM-SUR-LAG: Tests for the omission of a spatial lag of the explained variable in the right hand side of the "sim" equation. The model of the alternative is:
  

  y_{tg} = \rho_{g}Wy_{tg} + X_{tg} \beta_{g} + u_{tg}

  The null and alternative hypotheses are:

  H_{0}: \rho_{g}=0 (forall g) vs H_{A}: \rho_{g} ne 0 (exist g)

• LM-SUR-ERR: Tests for the omission of spatial dependence in the equation of the errors of the "sim" model. The model of the alternative is:

  y_{tg} = X_{tg} \beta_{g} + u_{tg}; u_{tg}= \lambda_{g}Wu_{tg}+\epsilon_{tg}

  The null and alternative hypotheses are:

  H_{0}: \lambda_{g}=0 (forall g) vs H_{A}: \lambda_{g} ne 0 (exist g)

• LM-SUR-SARAR: Tests for the simultaneous omission of a spatial lag of the explained variable in the right hand side of the "sim" equation and spatial dependence in the equation of the errors. The model of the alternative is:

  y_{tg} = \rho_{g}Wy_{tg}+X_{tg} \beta_{g} + u_{tg}; u_{tg}= \lambda_{g}Wu_{tg}+\epsilon_{tg}

  The null and alternative hypotheses are:

  H_{0}: \rho_{g}=\lambda_{g}=0 (forall g) vs H_{A}: \rho_{g} ne 0 or \lambda_{g} ne 0 (exist g)

• LM*-SUR-SLM and LM*-SUR-SEM: These two test are the robustifyed version of the original, raw Multipliers, LM-SUR-SLM and LM-SUR-SEM, which can be severely oversized if the respective alternative hypothesis is misspeficied (this would be the case if, for example, we are testing for omitted lags of the explained variable whereas the problem is that there is spatial dependence in the errors, or viceversa). The null and alternative hypotheses of both test are totally analogous to their twin non robust Multipliers.

Value

A list of htest objects each one including the Wald statistic, the corresponding p-value and the degrees of freedom.

Author(s)

References

• Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>

• Lopez, F.A., Mur, J., and Angulo, A. (2014). Spatial model selection strategies in a SUR framework. The case of regional productivity in EU. Annals of Regional Science, 53(1), 197-220. <doi:10.1007/s00168-014-0624-2>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>#'

• Anselin, L. (1988) A test for spatial autocorrelation in seemingly unrelated regressions Economics Letters 28(4), 335-341. <doi:10.1016/0165-1765(88)90009-2>

• Anselin, L. (1988) Spatial econometrics: methods and models Chap. 9 Dordrecht

• Anselin, L. (2016) Estimation and Testing in the Spatial Seemingly Unrelated Regression (SUR). Geoda Center for Geospatial Analysis and Computation, Arizona State University. Working Paper 2016-01. <doi:10.13140/RG.2.2.15925.40163>

See Also

spsurml, anova

Examples

#################################################
######## CROSS SECTION DATA (G>1; Tm=1) # #######
#################################################

#### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
rm(list = ls()) # Clean memory
data("spc")
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
lwspc <- spdep::mat2listw(Wspc, style = "W")
lmtestspsur(formula = Tformula, data = spc, listw = lwspc)

## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_lmtestspsur, package="spsur")


#################################################
######## PANEL DATA (G>1; Tm>1)          ########
#################################################

#### Example 2: Homicides & Socio-Economics (1960-90)
# Homicides and selected socio-economic characteristics for
# continental U.S. counties.
# Data for four decennial census years: 1960, 1970, 1980 and 1990.
# https://geodacenter.github.io/data-and-lab/ncovr/
data(NCOVR, package="spsur")
nbncovr <- spdep::poly2nb(NCOVR.sf, queen = TRUE)
### Some regions with no links...
lwncovr <- spdep::nb2listw(nbncovr, style = "W", zero.policy = TRUE)
### With different number of exogenous variables in each equation
Tformula <- HR70 | HR80  | HR90 ~ PS70 + UE70 | PS80 + UE80 +RD80 |
            PS90 + UE90 + RD90 + PO90
lmtestspsur(formula = Tformula, data = NCOVR.sf, 
            listw = lwncovr)

#################################################################
######### PANEL DATA: TEMPORAL CORRELATIONS (G=1; Tm>1) ########
#################################################################

##### Example 3: NCOVR in panel data form
Year <- as.numeric(kronecker(c(1960,1970,1980,1990), 
                   matrix(1,nrow = dim(NCOVR.sf)[1])))
HR <- c(NCOVR.sf$HR60,NCOVR.sf$HR70,NCOVR.sf$HR80,NCOVR.sf$HR90)
PS <- c(NCOVR.sf$PS60,NCOVR.sf$PS70,NCOVR.sf$PS80,NCOVR.sf$PS90)
UE <- c(NCOVR.sf$UE60,NCOVR.sf$UE70,NCOVR.sf$UE80,NCOVR.sf$UE90)
NCOVRpanel <- as.data.frame(cbind(Year,HR,PS,UE))
Tformula <- HR ~ PS + UE
lmtestspsur(formula = Tformula, data = NCOVRpanel, time = Year, 
listw = lwncovr)

Likelihood ratio for testing homogeneity constraints on beta coefficients of the SUR equations.

Description

Function lr_betas obtains a Likelihood Ratio test, LR in what follows, with the purpose of testing if some of the \beta coefficients in the G equations of the SUR model are equal. This function has a straightforward application, especially when G=1, to the case of testing for the existence of structural breaks in the \beta parameters.

The function can test for the homogeneity of only one coefficient, of a few of them or even the homogeneity of all the slope terms. The testing procedure implies, first, the estimation of both a constrained and a unconstrained model and, second, the comparison of the log-likelihoods to compute the LR statistics.

@usage lr_betas (obj, R, b)

Usage

lr_betas(obj, R, b)

Arguments

Value

Object of htest including the LR statistic, the corresponding p-value, the degrees of freedom and the values of the sample estimates.

Author(s)

References

• Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

See Also

spsurml, spsurtime, wald_betas

Examples

## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_lr_betas, package="spsur")


#' #################################################
######## CROSS SECTION DATA (G>1; Tm=1)  ########
#################################################

#### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
rm(list = ls()) # Clean memory
data(spc)
lwspc <- spdep::mat2listw(Wspc, style = "W")
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
### H0: equal beta for SMSA in both equations.
R <- matrix(c(0,0,0,1,0,0,0,-1), nrow=1)
b <- matrix(0, ncol=1)
spcsur.slm <- spsurml(formula = Tformula, data = spc, 
                      type = "slm", listw = lwspc)
summary(spcsur.slm)
lr_betas(spcsur.slm, R = R, b = b)

### Estimate restricted SUR-SLM model
spcsur.slmr <- spsurml(formula = Tformula, data = spc, 
                      type = "slm", listw = lwspc,
                      R = R, b = b)
summary(spcsur.slmr)

Methods for class spsur

Description

The anova() function provides tables of fitted spsur models including information criteria (AIC and BIC), log-likelihood and degrees of freedom of each fitted model. The argument lrtest allows to perform LR tests between nested models. The plot() function allows the user to plot both beta and spatial coefficients for all equations of the spsur model. The argument viewplot is used to choose between interactive or non-interactive plots. The print() function is used to print short tables including the values of beta and spatial coefficients as well as p-values of significance test for each coefficient. This can be used as an alternative to summary.spsur when a brief output is needed. The rest of methods works in the usual way.

Usage

## S3 method for class 'spsur'
anova(object, ..., lrtest = TRUE)

## S3 method for class 'spsur'
coef(object, ...)

## S3 method for class 'spsur'
fitted(object, ...)

## S3 method for class 'spsur'
logLik(object, ...)

## S3 method for class 'spsur'
residuals(object, ...)

## S3 method for class 'spsur'
vcov(object, ...)

## S3 method for class 'spsur'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'spsur'
plot(x, ci = 0.95, viewplot = TRUE, ...)

Arguments

Author(s)

Examples

rm(list = ls()) # Clean memory
data(spc)
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
spcsur.sim <-spsurml(formula = Tformula, data = spc, type = "sim")
## Print Table       
print(spcsur.sim)

spcsur.slm <-spsurml(formula = Tformula, data = spc, type = "slm", 
                     listw = Wspc)
# ANOVA table and LR test for nested models:
anova(spcsur.sim, spcsur.slm)
## Plot spatial and beta coefficients
# Interactive plot
plot(spcsur.slm)
# Non-interactive plot
if (require(gridExtra)) {
  pl <- plot(spcsur.slm, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}

Print method for objects of class summary.spsur.

Description

Print method for objects of class summary.spsur.

Usage

## S3 method for class 'summary.spsur'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

Author(s)

See Also

summary.spsur.

Examples

 # See examples for \code{\link{spsurml}} or
 # \code{\link{spsur3sls}} functions.

Within/Exit mobility index and incidence COVID-19 at Spain provinces

Description

Weekly within/exit mobility indices and COVID-19 incidence in Spain at provincial level. A total of 17 weeks from February 21 to May 21. Every week starts on a Friday and ends on the following Thursday. All travels are expressed with respect to the pre-COVID week of February 14th-20th, 2020 (week0). A value lower than 1 indicate a reduction of the mobility. Upper values than 1 indicate an increase in the mobility with respect to the reference week (week0).

Usage

spain.covid

Format

A data frame with 850 rows and 11 variables:

province

Province name coded as a factor

indiv

Province coded as a number

time

Number of week afther pre-COVID (week0: February 14th-20th, 2020). time=1 (week1, February 21th-27th); time=2 (week2,February 28th-March 5th);....

Within

Mobility index within of the province. See details for a formal definition

Exits

Mobility index of exits of the province. See details for a formal definition

Emergence

Dummy variable. 1 if the Emergence State ("Estado de Alarma") is active in the week "time" but economic activity of essential services are allowed. 0 in anoter case. The Emergence State was active in Spain from February 14th, 2020 to May 21th, 2020

EmergenceTotal

Dummy variable. 1 if the Emergence State ("Estado de Alarma") is active in the week "time" and economic activity of essential services are not allowed. 0 in anoter case

Old65

Percentage of population aged 65 and older in the province

Density

Inhabitants (in thousands) per km^2 in the province

Essential

Percentage of firms in the province "indiv" with essential activities (food, health) over the total of firms in the province "indiv" in the province (e.g., food, heath and some economic subsectors of industry and construction)

Incidence

Weekly incidence in the week "time-1" in logs

Details

• Mobility
  
  The mobility indices Within and Exits has been obtain as ratio of the total number of weekly travels in reference to the total number of travels in the week of reference (week0).
  In particular,
  
  Within = \frac{\textnormal{Number of travels within the province 'indiv' in the week 'time'}} {\textnormal{Number of travels within the province 'indiv' in the reference week (week0)}}

Exits = \frac{ \textnormal{Number of travels with origin in the province 'indiv' and arrival to another province in the week 'time'} } { \textnormal{Number of travels with origin in the province 'indiv' and arrival to another province in the reference week (week0)} }
  
  A ‘travel’ is a displacement from an origin to a destination of at least 500m. A travel can have several stages. These stages of the same travel are calculated based on the duration of the intermediate stop. For example , if I move from origin A to destination B with a stop at a point C of long duration, it is considered two travels, but if the stop is short, it is considered a single travel. For example, I can go by train from Madrid to Alicante, and there takes a bus to Benidorm. The travel will be one (Madrid-Benidorm). If I do the same but the stop in Alicante is long (or an overnight stay, for example), two travels will be considered (Madrid-Alicante, and Alicante-Benidorm). Similarly, if I go by car from Madrid to Alicante and stop for 15 minutes to take a coffee, it is also considered only one travel and not two. The travels considered in this study are always from 500m, due to the limitation of source data that is based on mobile telephony and its antennas. But one travel can be 600 meters or 600km.
  
  

• Incidence
  
  Incidence = \log \left( \frac{ \textnormal{total diagnostic cases of COVID-19, PCR test in the week 'time' at the province 'indiv'} } {\textnormal{total population in the province 'indiv'}} \right)
  
  

• Essential activities
  
  Essential activities. Economic activities whose activities are essential for the population. By example, essential activities are healthcare, food supply, State security, media and communication, refuse collection, management and public transport, etc. Non essential activities are by example, restaurants, hotels, hairdressers, etc. A full list in the Spanish official bulletin

Source

The National Statistics Institute
https://www.ine.es/en/index.htm
Instituto de Salud Carlos III
https://cnecovid.isciii.es/covid19/

Spain geometry

Description

A sf object with the Spanish geometry

Usage

spain.covid.sf

Format

A sf object with the Spanish geometry

PROVINCIA

province name coded as a factor

ID_INE

province coded as a number

A classical Spatial Phillips-Curve

Description

A data set from Anselin (1988, p. 203-2011) used to estimate a Spatial Phillips-Curve for 25 counties in South-West Ohio for two time periods (1981 and 1983).

Usage

spc

Format

A data frame with 25 rows and 10 variables:

COUNTY

County coded as a name.

WAGE83

Changes in wage rates for 1983.

UN83

Inverse unemployment rate in 1983.

NMR83

Net migration rate 1983.

SMSA

Dummy variable to identify counties defined as Standard Metropolitan Statistical Areas (SMSA = 1).

WAGE82

Changes in wage rates for 1982.

WAGE81

Changes in wage rates for 1981.

UN80

Inverse unemployment rate in 1980.

NMR80

Net migration rate 1983.

WAGE80

changes in wage rates.

geometry

geometry of sf object.

Source

Anselin (1988, p. 203-211)

References

• Anselin, L. (1988). Spatial Econometrics: Methods and Models. Springer Science & Business Media.

Three Stages Least Squares estimation,3sls, of spatial SUR models.

Description

The function estimates spatial SUR models using three stages least squares, where the instruments are obtained from the spatial lags of the X variables, assumed to be exogenous. The number of equations, time periods and spatial units is not restricted. The user can choose between a Spatial Durbin Model or a Spatial Lag Model, as described below. The estimation procedure allows for the introduction of linear restrictions on the \beta parameters associated to the regressors.

Usage

spsur3sls (formula = NULL, data = NULL, na.action,
                  R = NULL, b = NULL, listw = NULL, 
                  zero.policy = NULL, X= NULL, Y = NULL, G = NULL, 
                  N = NULL, Tm = NULL, p = NULL,  
                  type = "slm", Durbin = NULL, maxlagW = NULL,
                  trace = TRUE)

Arguments

Details

spsur3sls can be used to estimate two groups of spatial models:

• "slm": SUR model with spatial lags of the endogenous in the right hand side of the equations

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + \epsilon_{tg}

• "sdm": SUR model of the Spatial Durbin type

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + WX_{tg} \theta_{g} + \epsilon_{tg}

where y_{tg} and \epsilon_{tg} are (Nx1) vectors, corresponding to the g-th equation and time period t; X_{tg} is the matrix of regressors, of order N \times p_g. Moreover, \rho_{g} is a spatial coefficient and W is a (NxN) spatial weighting matrix.

By default, the input of this function is an object created with Formula and a data frame. However, spsur3sls also allows for the direct specification of vector Y and matrix X, with the explained variables and regressors respectively, as inputs (these terms may be the result, for example, of dgp_spsur).

spsur3sls is a Least-Squares procedure in three-stages designed to circumvent the endogeneity problems due to the presence of spatial lags of the explained variable in the right hand side of the equations do the SUR. The instruments are produced internally by spsur3sls using a sequence of spatial lags of the X variables, which are assumed to be exogenous. The user must define the number of (spatial) instruments to be used in the procedure, through the argument maxlagW (i.e. maxlagW = 3). Then, the collection of instruments generated is [WX_{tg}; W*WX_{tg}; W*W*WX_{tg}]. In the case of a SDM, the first lag of the X matrix already is in the equation and cannot be used as instrument. In the example above, the list of instruments for a SDM model would be [W^{2}X_{tg}; W^{3}X_{tg}].

The first stage of the procedure consists in the least squares of the Y variables on the set of instruments. From this estimation, the procedure retains the estimates of Y in the so-called Yls variables. In the second stage, the Y variables that appear in the right hand side of the equation are substituted by Yls and the SUR model is estimated by Least Squares. The third stage improves the estimates of the second stage through a Feasible Generalized Least Squares estimation of the parameters of the model, using the residuals of the second stage to estimate the Sigma matrix.

The arguments R and b allows to introduce linear restrictions on the beta coefficients of the G equations. spsur3sls, first, introduces the linear restrictions in the SUR model and builds, internally, the corresponding constrained SUR model. Then, the function estimates the restricted model which is shown in the output. The function does not compute the unconstrained model nor test for the linear restrictions. The user may ask for the unconstrained estimation using another spsurml estimation. Moreover, the function wald_betas obtains the Wald test of a set of linear restrictions for an object created previously by spsurml or spsur3sls.

Value

Object of spsur class with the output of the three-stages least-squares estimation of the specified spatial model. A list with:

Author(s)

References

• Anselin, L. (2016) Estimation and Testing in the Spatial Seemingly Unrelated Regression (SUR). Geoda Center for Geospatial Analysis and Computation, Arizona State University. Working Paper 2016-01. <doi:10.13140/RG.2.2.15925.40163>

• , Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht, The Netherlands (p. 146).

• Anselin, L., Le Gallo, J., Hubert J. (2008) Spatial Panel Econometrics. In The econometrics of panel data. Fundamentals and recent developments in theory and practice. (Chap 19, p. 653)

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

• Lopez, F. A., Minguez, R., Mur, J. (2020). ML versus IV estimates of spatial SUR models: evidence from the case of Airbnb in Madrid urban area. The Annals of Regional Science, 64(2), 313-347. <doi:10.1007/s00168-019-00914-1>

See Also

spsurml, stsls, wald_betas

Examples

#################################################
######## CLASSIC PANEL DATA (G=1; Tm>1)  ########
#################################################

#### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
## A SUR model without spatial effects
rm(list = ls()) # Clean memory
data(spc)
lwspc <- spdep::mat2listw(Wspc, style = "W")
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA

## A SUR-SLM model (3SLS Estimation)
spcsur_slm_3sls <-spsur3sls(formula = Tformula, data = spc,
                            type = "slm", listw = lwspc)
summary(spcsur_slm_3sls)
print(spcsur_slm_3sls)

if (require(gridExtra)) {
  pl <- plot(spcsur_slm_3sls, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}

## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_spsur3sls, package="spsur")


## A SUR-SDM model (3SLS Estimation)
spcsur_sdm_3sls <- spsur3sls(formula = Tformula, data = spc,
                             type = "sdm", listw = lwspc)
summary(spcsur_sdm_3sls)
if (require(gridExtra)) {
  pl <- plot(spcsur_sdm_3sls, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}
rm(spcsur_sdm_3sls)

## A SUR-SDM model with different spatial lags in each equation
 TformulaD <-  ~ UN83 + NMR83 + SMSA | UN80 + NMR80  
 spcsur_sdm2_3sls <-spsur3sls(formula = Tformula, data = spc,
                             type = "sdm", listw = lwspc,
                             Durbin = TformulaD)
 summary(spcsur_sdm2_3sls)
if (require(gridExtra)) {
  pl <- plot(spcsur_sdm2_3sls, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}
rm(spcsur_sdm2_3sls)


#################################################
###  MULTI-DIMENSIONAL PANEL DATA (G>1; Tm>1) ###
#################################################

#### Example 3: Homicides + Socio-Economics (1960-90)
# Homicides and selected socio-economic characteristics for continental
# U.S. counties.
# Data for four decennial census years: 1960, 1970, 1980 and 1990.
# https://geodacenter.github.io/data-and-lab/ncovr/


rm(list = ls()) # Clean memory
data(NCOVR, package = "spsur")
nbncovr <- spdep::poly2nb(NCOVR.sf, queen = TRUE)
## Some regions with no links...
lwncovr <- spdep::nb2listw(nbncovr, style = "W", zero.policy = TRUE)
Tformula <- HR80  | HR90 ~ PS80 + UE80 | PS90 + UE90
## A SUR-SLM model
NCOVRSUR_slm_3sls <- spsur3sls(formula = Tformula, data = NCOVR.sf, 
                               type = "slm", zero.policy = TRUE,
                               listw = lwncovr, trace = FALSE)
summary(NCOVRSUR_slm_3sls)
if (require(gridExtra)) {
  pl <- plot(NCOVRSUR_slm_3sls, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}
rm(NCOVRSUR_slm_3sls)

General Spatial 3SLS for systems of spatial equations.

Description

The function estimates spatial SUR models using general spatial three stages least squares. This is a system instrumental variable procedure which also include GMM estimation when there is spatial correlations in the errors. The procedure allows for additional endogenous regressors in addition to spatial lags of the dependent variable. It could be applied to "slm", "sdm", "sem" and "sarar" spatial models. Furthermore, for non-spatial models including endogenous regressors ("iv"), it could be used to estimate using instrumental variables and Feasible Generalized Least Squares.

Usage

spsurgs3sls(formula = NULL, data = NULL, na.action,
                  listw = NULL, zero.policy = NULL, 
                  type = "slm", Durbin = FALSE,
                  endog = NULL, instruments = NULL,
                  lag.instr = FALSE, initial.value = 0.2, 
                  het = FALSE, trace = TRUE)

Arguments

Details

spsurg3sls generalize the spreg function to multiequational spatial SUR models. The methodology to estimate spatial SUR models by Generalized 3SLS follows the steps outlined in Kelejian and Piras (pp. 304-305). The summary of the algorithm is the next one:

• Estimate each equation by 2SLS and obtain the estimated residuals \hat{u}_j for each equation.

• If the model includes a spatial lag for the errors. (that is, it is a SEM/SARAR model), apply GMM to obtain the spatial parameters \hat{\lambda}_j for the residuals in each equation. In this case the spreg function is used as a wrapper for the GMM estimation. If the model does not include a spatial lag for the errors (that is, it is a "sim", "iv", "slm" or "sdm" model), then \hat{\lambda}_j = 0

• Compute

  \hat{v}_j = \hat{u}_j-\hat{\lambda}_j W \hat{u}_j

  and the covariances

  \hat{\sigma}_{i,j} = N^{-1}\hat{v}_i\hat{v}_j

  . Build \hat{Sigma}=\lbrace \hat{\sigma_{i,j}} \rbrace

• Compute

  y_j^* = y_j - \hat{\lambda}_j W y_j

  and

  X_j^* = X_j- \hat{\lambda}_j W X_j

  Compute

  \hat{X}_j^* = H_j(H_j^T H_j)^{-1} H_j^T X_j^*

  where H_j is the matrix including all the instruments and the exogenous regressors for each equation. That is, \hat{X}_j^* is the projection of X_j^* using the instruments matrix H_j.

• Compute, in a multiequational way, the Feasible Generalized Least Squares estimation using the new variables \hat{y}_j^*, \hat{X}_j^* and \hat{Sigma}. This is the 3sls step.

Value

Object of spsur class with the output of the three-stages least-squares estimation of the specified spatial model. A list with:

Author(s)

References

• Kelejian, H. H. and Piras, G. (2017). Spatial Econometrics. Academic Press.

• Kelejian, H.H. and Prucha, I.R. (2010). Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances. Journal of Econometrics, 157, pp. 53-67.

• Kelejian, H.H. and Prucha, I.R. (1999). A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model. International Economic Review, 40, pp. 509-533.

• Kelejian, H.H. and Prucha, I.R. (1998). A Generalized Spatial Two Stage Least Square Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. Journal of Real Estate Finance and Economics, 17, pp. 99–121.

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

• Piras, G. (2010). sphet: Spatial Models with Heteroskedastic Innovations in R. Journal of Statistical Software, 35(1), pp. 1-21. https://www.jstatsoft.org/v35/i01/. -

See Also

spreg, spsur3sls, stsls, spsurml

Examples

#### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
rm(list = ls()) # Clean memory
data(spc)
lwspc <- spdep::mat2listw(Wspc, style = "W")
## No endogenous regressors  
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
## Endogenous regressors and Instruments
Tformula2 <- WAGE83 | WAGE81 ~  NMR83 | NMR80 
## Endogenous regressors: UN83 , UN80
## Instrumental variable: SMSA

## A IV model with endogenous regressors only in first equation
spciv <- spsurgs3sls(formula = Tformula2, data = spc,
                      type = "iv", listw = lwspc,
                      endog = ~ UN83 | ., 
                      instruments = ~ SMSA | .)
summary(spciv)
print(spciv)
#########################################################################
## A SLM model with endogenous regressors 
spcslm <- spsurgs3sls(formula = Tformula2, data = spc,
                              endog = ~ UN83 | ., 
                              instruments = ~ SMSA |.,
                              type = "slm", 
                              listw = lwspc)
summary(spcslm)
print(spcslm)                           
impacts_spcslm <- impactspsur(spcslm, listw = lwspc, R = 1000)
summary(impacts_spcslm[[1]], zstats = TRUE, short = TRUE)
summary(impacts_spcslm[[2]], zstats = TRUE, short = TRUE)
#########################################################################
## A SDM model with endogenous regressors 
spcsdm <- spsurgs3sls(formula = Tformula2, data = spc,
                   endog = ~ UN83 | UN80, 
                   instruments = ~ SMSA | SMSA,
                   type = "sdm", listw = lwspc,
                   Durbin =  ~  NMR83 | NMR80)
summary(spcsdm)
## Durbin only in one equation
spcsdm2 <- spsurgs3sls(formula = Tformula2, data = spc,
                    endog = ~ UN83 | UN80, 
                    instruments = ~ SMSA | SMSA,
                    type = "sdm", listw = lwspc,
                    Durbin =  ~  NMR83 | .)
summary(spcsdm2)
#########################################################################
## A SEM model with endogenous regressors 
spcsem <- spsurgs3sls(formula = Tformula2, data = spc,
                      endog = ~ UN83 | UN80, 
                      instruments = ~ SMSA | SMSA,
                      type = "sem", listw = lwspc)
summary(spcsem)
print(spcsem)                           
#########################################################################
## A SARAR model with endogenous regressors 
spcsarar <- spsurgs3sls(formula = Tformula2, data = spc,
                        endog = ~ UN83 | UN80, 
                        instruments = ~ SMSA | SMSA,
                        type = "sarar", listw = lwspc)
summary(spcsarar)
print(spcsarar)                           
impacts_spcsarar <- impactspsur(spcsarar, listw = lwspc, R = 1000)
summary(impacts_spcsarar[[1]], zstats = TRUE, short = TRUE)
summary(impacts_spcsarar[[2]], zstats = TRUE, short = TRUE)

Maximum likelihood estimation of spatial SUR model.

Description

This function estimates spatial SUR models using maximum-likelihood methods.The number of equations, time periods and cross-sectional units is not restricted.The user can choose between different spatial specifications as described below. The estimation procedure allows for the introduction of linear restrictions on the \beta parameters associated to the regressors.

Usage

spsurml(formula = NULL, data = NULL, na.action,
               listw = NULL, type = "sim", Durbin = NULL,
               method = "eigen", zero.policy = NULL, interval = NULL,
               trs = NULL, R = NULL, b = NULL, X = NULL, Y = NULL, 
               G = NULL, N = NULL, Tm = NULL,p = NULL, 
               control = list() )

Arguments

Details

The list of (spatial) models that can be estimated with the spsurml function are:

• "sim": SUR model with no spatial effects

  y_{tg} = X_{tg} \beta_{g} + \epsilon_{tg}

• "slx": SUR model with spatial lags of the regressors

  y_{tg} = X_{tg} \beta_{g} + WX_{tg} \theta_{g} + \epsilon_{tg}

• "slm": SUR model with spatial lags of the explained variables

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + \epsilon_{tg}

• "sem": SUR model with spatial errors

  y_{tg} = X_{tg} \beta_{g} + u_{tg}

  u_{tg} = \lambda_{g} Wu_{tg} + \epsilon_{tg}

• "sdm": SUR model of the Spatial Durbin type

  y_{tg} = \rho_{g} Wy_{tg} + X_{tt} \beta_{g} + WX_{tg} \theta_{g} + \epsilon_{tg}

• "sdem": SUR model with spatial lags of the regressors and spatial errors

  y_{tg} = X_{tg} \beta_{g} + WX_{tg} \theta_{g} + u_{tg}

  u_{tg} = \lambda_{g} W u_{tg} + \epsilon_{tg}

• "sarar": SUR model with spatial lags of the explained variables and spatial errors

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + u_{tg}

  u_{tg} = \lambda_{g} W u_{tg} + \epsilon_{tg}

• "gnm": SUR model with spatial lags of the explained variables, regressors and spatial errors

  y_{tg} = \rho_{g} Wy_{tg} + X_{tg} \beta_{g} + WX_{tg} \theta_{g} + u_{tg}

  u_{tg} = \lambda_{g} W u_{tg} + \epsilon_{tg}

Value

Object of spsur class with the output of the maximum-likelihood estimation of the specified spatial SUR model. A list with:

Control arguments

Author(s)

References

• Anselin, L. (1988). Spatial econometrics: methods and models. Dordrecht: Kluwer

• Bivand, R.S. and Piras G. (2015). Comparing Implementations of Estimation Methods for Spatial Econometrics. Journal of Statistical Software, 63(18), 1-36. <doi: 10.18637/jss.v063.i18>

• Bivand, R. S., Hauke, J., and Kossowski, T. (2013). Computing the Jacobian in Gaussian spatial autoregressive models: An illustrated comparison of available methods. Geographical Analysis, 45(2), 150-179. <doi:10.1111/gean.12008>

• Breusch T., Pagan A. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47: 239-254

• Cliff, A.D. and Ord, J.K. (1981). Spatial processes: Models and applications, Pion.

• LeSage J and Pace, R.K. (2009). Introduction to Spatial Econometrics. CRC Press, Boca Raton.

• Lopez, F.A., Mur, J., and Angulo, A. (2014). Spatial model selection strategies in a SUR framework. The case of regional productivity in EU. Annals of Regional Science, 53(1), 197-220. <doi:10.1007/s00168-014-0624-2>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

• Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>

• Ord, J.K. (1975). Estimation methods for models of spatial interaction, Journal of the American Statistical Association, 70, 120-126.

See Also

spsur3sls, lagsarlm, lmtestspsur, wald_betas, lr_betas

Examples

#################################################
######## CROSS SECTION DATA (G>1; Tm=1) ########
#################################################

#### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
rm(list = ls()) # Clean memory
data(spc)
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
spcsur.sim <- spsurml(formula = Tformula, data = spc, type = "sim")
summary(spcsur.sim)
# All the coefficients in a single table.
print(spcsur.sim)
# Plot of the coefficients of each equation in different graphs
plot(spcsur.sim) 

## A SUR-SLX model 
## (listw argument can be either a matrix or a listw object )
spcsur.slx <- spsurml(formula = Tformula, data = spc, type = "slx", 
  listw = Wspc, Durbin = TRUE)
summary(spcsur.slx)
# All the coefficients in a single table.
print(spcsur.slx)
# Plot of the coefficients in a single graph
if (require(gridExtra)) {
  pl <- plot(spcsur.slx, viewplot = FALSE)
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
                        nrow = 2)
} 

## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_spsurml, package="spsur")
  

### A SUR-SLM model
spcsur.slm <- spsurml(formula = Tformula, data = spc, type = "slm", 
                     listw = Wspc)
summary(spcsur.slm)
if (require(gridExtra)) {
  pl <- plot(spcsur.slm, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}


### A SUR-SDM model
spcsur.sdm <- spsurml(formula = Tformula, data = spc, type = "sdm", 
                     listw = Wspc)
summary(spcsur.sdm)
print(spcsur.sdm)
if (require(gridExtra)) {
  pl <- plot(spcsur.sdm, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}

## A SUR-SDM model with different spatial lags in each equation
TformulaD <- ~ UN83 + NMR83 + SMSA | UN80
spcsur.sdm2 <- spsurml(formula = Tformula, data = spc, type = "sdm", 
                      listw = Wspc, Durbin = TformulaD)
summary(spcsur.sdm2)
if (require(gridExtra)) {
  pl <- plot(spcsur.sdm2, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}


##################################################
#########  CLASSIC PANEL DATA G=1; Tm>1   ########
##################################################
#
##### Example 2: Homicides + Socio-Economics (1960-90)
## Homicides and selected socio-economic characteristics for continental
## U.S. counties.
## Data for four decennial census years: 1960, 1970, 1980 and 1990.
## \url{https://geodacenter.github.io/data-and-lab/ncovr/}


### It usually requires 1-2 minutes maximum...
rm(list = ls()) # Clean memory
### Read NCOVR.sf object
data(NCOVR, package = "spsur")
nbncovr <- spdep::poly2nb(NCOVR.sf, queen = TRUE)
### Some regions with no links...
lwncovr <- spdep::nb2listw(nbncovr, style = "W", zero.policy = TRUE)
Tformula <- HR80  | HR90 ~ PS80 + UE80 | PS90 + UE90
### A SUR-SIM model
NCOVRSUR.sim <- spsurml(formula = Tformula, data = NCOVR.sf, type = "sim")
summary(NCOVRSUR.sim)
if (require(gridExtra)) {
  pl <- plot(NCOVRSUR.sim, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], nrow = 3)
}
### A SUR-SLX model
NCOVRSUR.slx <- spsurml(formula = Tformula, data = NCOVR.sf, type = "slx", 
                       listw = lwncovr, zero.policy = TRUE)
print(NCOVRSUR.slx)
if (require(gridExtra)) {
  pl <- plot(NCOVRSUR.slx, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], nrow = 2)
}

### A SUR-SLM model
### method = "Matrix" (Cholesky) instead of "eigen"
### (fdHess = TRUE to compute numerical covariances )
NCOVRSUR.slm <- spsurml(formula = Tformula, data = NCOVR.sf, 
                       type = "slm", listw = lwncovr, method = "Matrix", 
                       zero.policy = TRUE, control = list(fdHess = TRUE))
summary(NCOVRSUR.slm)

if (require(gridExtra)) {
  pl <- plot(NCOVRSUR.slm, viewplot = FALSE) 
  grid.arrange(pl$lplbetas[[1]], pl$lplbetas[[2]], 
               pl$pldeltas, nrow = 3)
}
# LR test for nested models
anova(NCOVRSUR.sim, NCOVRSUR.slm)

Estimation of SUR models for simple spatial panels (G=1).

Description

This function estimates SUR models for simple spatial panel datasets. spsurtime is restricted, specifically, to cases where there is only one equation, G=1, and a varying number of spatial units, N, and time periods, Tm. The SUR structure appears in form of serial dependence among the error terms corresponding to the same spatial unit. Note that it is assumed that all spatial units share a common pattern of serial dependence.

The user can choose between different types of spatial specifications, as described below, and the estimation algorithms allow for the introduction of linear restrictions on the \beta parameters associated to the regressors. The spatial panels with SUR structure can be estimated by maximum-likelihood methods or three-stages least squares procedures, using spatial instrumental variables.

Usage

spsurtime (formula, data, time, na.action,
                  listw = NULL, type = "sim", Durbin = NULL, 
                  method = "eigen", fit_method = "ml", maxlagW = NULL,
                  zero.policy = NULL, interval = NULL, trs = NULL,
                  R = NULL, b = NULL, demean = FALSE, control = list() )

Arguments

Details

Function spsurtime only admits a formula, created with Formula and a dataset of class data.frame or matrix. That is, the data cannot be uploaded using data matrices Y and X provided for other functions in this package.
The argument time selects the variable, in the data.frame, associated to the time dimension in the panel dataset. Then spsurtime operates as in Anselin (1988), that is, each cross-section is treated as if it were an equation in a SUR model, which now has Tm 'equations' and N individuals.
The SUR structure appears because there is serial dependence in the errors of each individual in the panel. The serial dependence in the errors is not parameterized, but estimated non-parametrically in the Sigma covariance matrix returned by the function. An important constraint to mention is that the serial dependence assumed to be the same for all individuals in the sample. Serial dependence among individuals is excluded from Anselin approach.

Value

An spsur object with the output of the maximum-likelihood or three-stages least-squares estimation of the spatial panel SUR model.

Author(s)

References

• Anselin, L. (1988). Spatial econometrics: methods and models. Dordrecht, Kluwer Academic Publishers.

• Lopez, F.A., Mur, J., and Angulo, A. (2014). Spatial model selection strategies in a SUR framework. The case of regional productivity in EU. Annals of Regional Science, 53(1), 197-220. <doi:10.1007/s00168-014-0624-2>

• Lopez, F.A., Martinez-Ortiz, P.J., and Cegarra-Navarro, J.G. (2017). Spatial spillovers in public expenditure on a municipal level in Spain. Annals of Regional Science, 58(1), 39-65. <doi:10.1007/s00168-016-0780-7>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

• Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>

See Also

spsurml, spsur3sls, wald_betas, wald_deltas, lmtestspsur, lr_betas

Examples

####################################
######## PANEL DATA (G=1; Tm>1) ###
####################################

## Example 1:
rm(list = ls()) # Clean memory
data(spc)
lwspc <- spdep::mat2listw(Wspc, style = "W")
N <- nrow(spc)
Tm <- 2
index_time <- rep(1:Tm, each = N)
index_indiv <- rep(1:N, Tm)
WAGE <- c(spc$WAGE83, spc$WAGE81)
UN <- c(spc$UN83, spc$UN80)
NMR <- c(spc$NMR83, spc$NMR80)
SMSA <- c(spc$SMSA, spc$SMSA)
pspc <- data.frame(index_indiv, index_time, WAGE, UN,
                    NMR, SMSA)
form_pspc <- WAGE ~ UN + NMR + SMSA
form2_pspc <- WAGE | NMR ~ UN  | UN + SMSA

# SLM 
pspc_slm <- spsurtime(formula = form_pspc, data = pspc, 
                      listw = lwspc,
                      time = pspc$index_time, 
                      type = "slm", fit_method = "ml")
summary(pspc_slm)
 
pspc_slm2 <- spsurtime(formula = form2_pspc, data = pspc, 
                      listw = lwspc,
                      time = pspc$index_time, 
                      type = "slm", fit_method = "ml")
summary(pspc_slm2)


## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_spsurtime, package="spsur")

 
### Example 2:
rm(list = ls()) # Clean memory
### Read NCOVR.sf object
data(NCOVR, package="spsur")
nbncovr <- spdep::poly2nb(NCOVR.sf, queen = TRUE)
### Some regions with no links...
lwncovr <- spdep::nb2listw(nbncovr, style = "W", zero.policy = TRUE)
N <- nrow(NCOVR.sf)
Tm <- 4
index_time <- rep(1:Tm, each = N)
index_indiv <- rep(1:N, Tm)
pHR <- c(NCOVR.sf$HR60, NCOVR.sf$HR70, NCOVR.sf$HR80, NCOVR.sf$HR90)
pPS <- c(NCOVR.sf$PS60, NCOVR.sf$PS70, NCOVR.sf$PS80, NCOVR.sf$PS90)
pUE <- c(NCOVR.sf$UE60, NCOVR.sf$UE70, NCOVR.sf$UE80, NCOVR.sf$UE90)
pNCOVR <- data.frame(indiv = index_indiv, time = index_time,
                     HR = pHR, PS = pPS, UE = pUE)
form_pHR <- HR ~ PS + UE

## SIM

pHR_sim <- spsurtime(formula = form_pHR, data = pNCOVR, 
                    time = pNCOVR$time, type = "sim", fit_method = "ml")
summary(pHR_sim)

## SLM by 3SLS. 

pHR_slm <- spsurtime(formula = form_pHR, data = pNCOVR, listw = lwncovr,
                     time = pNCOVR$time, type = "slm", 
                     fit_method = "3sls")
summary(pHR_slm)

############################# Wald tests about betas in spatio-temporal models
### H0: equal betas for PS in equations 1, 3 and 4.
R <- matrix(0, nrow = 2, ncol = 12) 
## nrow = number of restrictions 
## ncol = number of beta parameters
R[1, 2] <- 1; R[1, 8] <- -1 # PS beta coefficient in equations 1 equal to 3
R[2, 2] <- 1; R[2, 11] <- -1 # PS beta coefficient in equations 1 equal to 4
b <- matrix(0, nrow=2, ncol=1)
wald_betas(pHR_sim , R = R , b = b) # SIM model
wald_betas(pHR_slm , R = R , b = b) # SLM model
############################# Wald tests about spatial-parameters in
############################# spatio-temporal models
### H0: equal rhos in slm model for equations 1 and 2.
R2 <- matrix(0, nrow = 1, ncol = 4)
R2[1, 1] <- 1; R2[1, 2] <- -1
b2 <- matrix(0, nrow = 1, ncol = 1)
wald_deltas(pHR_slm, R = R2, b = b2)

Summary of estimated objects of class spsur.

Description

This function summarizes estimated spsur objects. The tables in the output include basic information for each equation. The report also shows other complementary results corresponding to the SUR model like the (GxG) covariance matrix of the residuals of the equations of the SUR, the estimated log-likelihood, the Breusch-Pagan diagonality test or the Marginal Lagrange Multiplier, LMM, tests of spatial dependence.

Usage

## S3 method for class 'spsur'
summary(object, ...)

Arguments

Value

An object of class summary.spsur

Author(s)

See Also

print.summary.spsur; spsurml; spsur3sls.

Examples

 # See examples for \code{\link{spsurml}} or
 # \code{\link{spsur3sls}} functions.

Wald tests on the beta coefficients

Description

The function wald_betas can be seen as a complement to the restricted estimation procedures included in the functions spsurml and spsur3sls. wald_betas obtains Wald tests for sets of linear restrictions on the coefficients \beta of the SUR model. The restrictions may involve coefficients of the same equation or coefficients from different equations. The function has great flexibility in this respect. Note that wald_betas is more general than lr_betas in the sense that the last function only allows to test for restrictions of homogeneity of subsets of \beta coefficients among the different equations in the SUR model, and in a maximum-likelihood framework.

In order to work with wald_betas, the model on which the linear restrictions are to be tested needs to exists as an spsur object. Using the information contained in the object, wald_betas obtains the corresponding Wald estatistic for the null hypotheses specified by the user through the R row vector and b column vector, used also in spsurml and spsur3sls. The function shows the value of the Wald test statistics and its associated p-values.

Usage

wald_betas (obj , R , b)

Arguments

Value

Object of htest class including the Wald statistic, the corresponding p-value, the degrees of freedom and the values of the sample estimates.

Author(s)

References

• Lopez, F.A., Mur, J., and Angulo, A. (2014). Spatial model selection strategies in a SUR framework. The case of regional productivity in EU. Annals of Regional Science, 53(1), 197-220. <doi:10.1007/s00168-014-0624-2>

• Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>

• Anselin, L. (2016) Estimation and Testing in the Spatial Seemingly Unrelated Regression (SUR). Geoda Center for Geospatial Analysis and Computation, Arizona State University. Working Paper 2016-01. <doi:10.13140/RG.2.2.15925.40163>

• Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>

See Also

spsurml, spsur3sls, lr_betas

Examples

## VIP: The output of the whole set of the examples can be examined 
## by executing demo(demo_wald_betas, package="spsur")

#################################################
######## CROSS SECTION DATA (G=1; Tm>1) ########
#################################################

##### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
rm(list = ls()) # Clean memory
data(spc)
lwspc <- spdep::mat2listw(Wspc, style = "W")
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
### Estimate SUR-SLM model
spcsur.slm <- spsurml(formula = Tformula, data = spc, 
                      type = "slm", listw = lwspc)
summary(spcsur.slm)
### H_0: equality between SMSA coefficients in both equations.
R1 <- matrix(c(0,0,0,1,0,0,0,-1), nrow=1)
b1 <- matrix(0, ncol=1)

wald_betas(spcsur.slm, R = R1, b = b1)

Wald tests for spatial parameters coefficients.

Description

Function wald_deltas obtains Wald tests for linear restrictions on the spatial coefficients of a SUR model that has been estimated previously through the function spsurml. The restrictions can affect to coefficients of the same equation (i.e., \lambda_{g}=\rho_{g} forall g) or can involve coefficients from different equations (i.e., \lambda_{g}=\lambda_{h}). The function has great flexibility in this respect. Note that wald_deltas only works in a maximum-likelihood framework.

In order to work with wald_betas, the model on which the linear restrictions are to be tested needs to exists as an spsur object. Using the information contained in the object, wald_deltas obtains the corresponding Wald statistic for the null hypotheses specified by the user through the R row vector and b column vector discussed, used also in spsurml. The function shows the resulting Wald test statistics and their corresponding p-values.

Usage

wald_deltas (obj , R , b)

Arguments

Value

Object of htest including the Wald statistic, the corresponding p-value, the degrees of freedom and the values of the sample estimates.

Author(s)

See Also

spsurml, spsur3sls

Examples

#################################################
######## CROSS SECTION DATA (G>1; Tm=1) ########
#################################################
rm(list = ls()) # Clean memory
data(spc, package = "spsur")
lwspc <- spdep::mat2listw(Wspc, style = "W")
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA

#################################
## Estimate SUR-SLM model
spcsur.slm <-spsurml(formula = Tformula, data = spc, 
                       type = "slm", listw = lwspc)
summary(spcsur.slm)
## H_0: equality of the lambda parameters of both equations.
R1 <- matrix(c(1,-1), nrow=1)
b1 <- matrix(0, ncol=1)
wald_deltas(spcsur.slm, R = R1, b = b1)