Information-Theoretic Methods for Econometric Estimation
infometrics is an R package implementing Information-Theoretic (IT) estimators
for econometric models, following the unified framework of Golan (2008),
Information and Entropy Econometrics — A Review and Synthesis
(doi:10.1561/0800000004). The package
focuses on the Generalized Maximum Entropy (GME) and Generalized Cross-Entropy
(GCE) family: each unknown is reparameterized as the mean of a distribution on a
bounded support, and the resulting entropy problem is solved subject to the data.
Every estimator solves the dual (concentrated) model — working in
Lagrange-multiplier space via BFGS with analytic gradients — which is
computationally efficient and needs no compiled code. The package is pure R and
depends only on stats.
infometrics is on CRAN:
install.packages("infometrics")
The development version lives on GitHub:
# install.packages("remotes")
remotes::install_github("GenMaxEnt/infometrics_R")
One function per estimator family: uniform priors give GME, user-supplied
priors (p0, w0) give GCE — there are no separate gce_* twins.
Regression
linreg() — lm-style GCE linear regression, with predict(), an
entropy-ratio (ER) coefficient test, and prior-relative normalized entropy.linreg_iv() — GME instrumental-variables regression (just- and
over-identified), with sandwich / delta / bootstrap standard errors.panel_gce() — one-way error-components panel model, with standard errors
for the coefficients, individual effects, and support weights.Inverse problems
inverse_ce() — pure-moment ME/CE inverse problem (formula interface), with
information-matrix standard errors and Fano error bounds.inverse_noise() — noisy-moment GME/GCE inverse problem, with sandwich /
delta / bootstrap standard errors and Fano bounds.Matrix balancing and Markov chains
matrix_ce() / matrix_gce() — exact and noisy-moment matrix balancing.markov_ce() / markov_gce() — exact and noisy-moment first-order Markov
transition matrices, with marginal effects and Fano bounds.Multinomial response
multinomial_gce() — nu-weighted GCE multinomial (Golan–Judge–Perloff),
with marginal effects (robust SEs) and Fano bounds.mixed_gce() — doubly-reparameterized GCE mixed model.Utilities — shannon_entropy(), make_support(), default_supports(),
normalize_data(), plus the margins() and fano_bounds() generics.
Every estimator returns an S3 object inheriting from "infometrics" with
print, summary, coef, fitted, and residuals methods (and, where
applicable, vcov, predict, margins, and fano_bounds).
vignette("inverse-problems", package = "infometrics")
vignette("gme-gce-regression", package = "infometrics")
linreg() and linreg_iv(): GME under collinearity, GCE with an informative
prior, the entropy weight nu, entropy diagnostics and hypothesis tests, and
an instrumental-variables example.Support spaces are the user's most consequential input. The dual is bounded
below only if the data are representable within the supports — if they are not,
the multipliers diverge and estimates collapse to a support boundary. The
estimators check the first-order condition at the optimum (foc_residual) and
warn when it is violated; the remedy is a wider error support v (and/or Z).
Golan, A. (2008). Information and Entropy Econometrics — A Review and Synthesis. Foundations and Trends in Econometrics, 2(1–2), 1–145. doi:10.1561/0800000004
Golan, A. (2018). Foundations of Info-Metrics: Modeling, Inference, and Imperfect Information. New York: Oxford University Press. doi:10.1093/oso/9780199349524.001.0001
Golan, A., Judge, G. G., & Miller, D. (1996). Maximum Entropy Econometrics: Robust Estimation with Limited Data. Chichester: John Wiley & Sons.
To cite infometrics in publications, run:
citation("infometrics")
GPL-3.