---
title: "Nested logit and grouped substitution"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Nested logit and grouped substitution}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
options(digits = 4)
```

Nested logit groups alternatives into *nests* of close substitutes. Within a
nest, alternatives share an unobserved component, so substitution is stronger
inside a nest than across nests. Each non-singleton nest has a positive
dissimilarity parameter λ; singleton nests have λ fixed at 1 because no
within-nest variation can identify it. The usual random-utility interpretation of
nested logit focuses on `0 < λ <= 1`: λ = 1 gives the MNL limit, and smaller λ
means tighter substitution inside the nest. choicer imposes λ > 0 rather than an
upper bound. Estimates above one are mathematically computable but imply negative
within-nest correlation and should be treated as evidence against the proposed
nesting structure, not as the usual "close substitutes" interpretation.

Think of nested logit as the **parsimonious middle ground** between the
multinomial and mixed logits. It introduces genuine within-nest correlation in
unobserved utility at the cost of just one extra parameter per nest, and it stays
globally well-behaved and cheap to estimate (a closed-form GEV likelihood, no
simulation). The price you pay is a strong prior:
*you* must specify the nesting tree in advance, and the model only permits
correlation within the nests you draw. When the right grouping is obvious from
the application (travel modes, product categories), that is a defensible
restriction; when it is not, the result can be sensitive to how you nest. A good
nested-logit application therefore treats the tree as an economic assumption,
not as a tuning parameter chosen after looking at fit statistics. See
[Choosing among choice models](choicer.html#choosing-among-choice-models) for how
this tradeoff compares with the alternatives.

```{r setup}
library(choicer)
set_num_threads(2)
```

## Simulate a nested process

`simulate_nl_data()` builds two nests of inside goods plus an outside option,
with known dissimilarity parameters.

```{r sim}
sim <- simulate_nl_data(N = 4000, seed = 1)
sim
```

## Fit

Supply the nest membership column; choicer estimates the coefficients, the
alternative-specific constants and the nest dissimilarity parameters jointly,
using an analytical gradient and Hessian.

```{r fit}
fit <- run_nestlogit(
  data                   = sim$data,
  id_col                 = "id",
  alt_col                = "j",
  choice_col             = "choice",
  covariate_cols         = c("X", "W"),
  nest_col               = "nest",
  use_asc                = TRUE,
  include_outside_option = TRUE,
  outside_opt_label      = 0L
)
summary(fit)
```

## Parameter recovery

The `lambda` rows are the nest dissimilarity parameters — the part that is
unique to nested logit.

```{r recovery}
recovery_table(fit, sim$true_params)
```

## What identifies the dissimilarity parameters?

It is worth being explicit about where each parameter block gets its
information. The coefficients on `X` and `W` are identified, as in any logit,
by how utilities respond to covariate variation. The dissimilarity parameters
are identified by *reallocation*: when an observed utility shifter moves, does
the displaced demand stay inside the nest or spill across nests?
Alternative-specific covariates that vary within nests are therefore the
variation that disciplines λ. If the covariates move whole nests together — or
the specification is mostly alternative-specific constants — λ is pinned down
mainly by functional form, and a wide confidence interval on a `lambda` row is
the model saying the data cannot see inside that nest.

The estimate's position in (0, 1] is itself informative. A λ near 1 says the
data see no extra within-nest correlation: the MNL was adequate, and the nest
costs a parameter without buying substitution structure. A λ near 0 says the
nest's alternatives are nearly perfect substitutes at the nest margin, which
carries a sharp welfare implication: the nested logsum discounts within-nest
variety by λ, so adding an alternative to a tight nest adds almost no expected
consumer surplus, while adding one to a loose nest adds a lot. Welfare
counterfactuals computed with `consumer_surplus()` inherit exactly this
structure — one more reason to treat the tree as an economic assumption rather
than a fit device.

## Nest-consistent elasticities

This is the payoff of nesting: cross-elasticities are larger for alternatives in
the *same* nest than for alternatives in different nests. choicer's
`elasticities()` respects the nest structure automatically.

```{r elast}
elasticities(fit, elast_var = "W")
diversion_ratios(fit)
```

Those substitution patterns are credible only to the extent that the nesting tree
is credible. If plausible alternative trees imply materially different diversion
or welfare conclusions, that sensitivity is part of the empirical result rather
than a nuisance to hide.

## Share inversion with BLP

`blp()` runs the Berry-Levinsohn-Pakes contraction to recover the mean utilities
that reproduce a set of target shares — useful for calibration and demand
estimation from aggregate data. For strongly-nested models a damping factor
below 1 stabilizes the iteration.

```{r blp}
target_shares <- predict(fit, type = "shares")
head(blp(fit, target_shares, damping = 0.5))
```

As always, `predict()`, `wtp()` and `consumer_surplus()` are available on the
fitted object with the same syntax used throughout choicer.

## Empirical checklist

A reportable nested-logit specification should show the proposed tree and its
economic rationale; report every estimated lambda and uncertainty interval;
identify singleton nests, whose lambda is fixed at 1 by choicer because there is
no within-nest variation to learn from; and flag estimates outside the
conventional random-utility range `0 < lambda <= 1`. Refit plausible alternative trees and compare
fit, diversion, elasticities, WTP, and welfare—not just the coefficient table.

When using `blp()`, verify that shares reconstructed from the returned mean
utilities match the target within the stated tolerance, and report sensitivity
to damping. (The current return value is the utility vector, not a convergence
diagnostics object.) Damping is a numerical aid, not evidence for the tree or
the economic validity of lambda. NL
does not search over trees or estimate cross-nested substitution, so uncertainty
about the grouping remains specification uncertainty outside the reported
standard errors.

The full derivations — the GEV likelihood, analytical gradient and Hessian,
nest-consistent elasticities and diversion, and how choicer's
utility-maximization-consistent parameterization relates to the non-normalized
nested logit found in some other software — are in the
[math companion](https://fpcordeiro.github.io/choicer/articles/nested_logit_math.html).

## References

Heiss, F. (2002). Structural choice analysis with nested logit models.
*The Stata Journal*, 2(3), 227-252.

McFadden, D. (1978). Modelling the choice of residential location. In
A. Karlqvist et al. (Eds.), *Spatial Interaction Theory and Planning Models*.
North-Holland.

Train, K. E. (2009). *Discrete Choice Methods with Simulation* (2nd ed.).
Cambridge University Press, Chapter 4.
