|Maintainer:||Achim Zeileis, Grant McDermott, Kevin Tappe|
|Contact:||Achim.Zeileis at R-project.org|
|Contributions:||Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.|
|Citation:||Achim Zeileis, Grant McDermott, Kevin Tappe (2022). CRAN Task View: Econometrics. Version 2022-04-11. URL https://CRAN.R-project.org/view=Econometrics.|
|Installation:||The packages from this task view can be installed automatically using the ctv package. For example, |
Base R ships with a lot of functionality useful for computational econometrics, in particular in the stats package. This functionality is complemented by many packages on CRAN, a brief overview is given below. There is also a considerable overlap between the tools for econometrics in this view and those in the task views on Finance and TimeSeries.
The packages in this view can be roughly structured into the following topics. If you think that some package is missing from the list, please file an issue in the GitHub repository or contact the maintainer.
lm()(from stats) and standard tests for model comparisons are available in various methods such as
anova()methods that also support asymptotic tests (z instead of t tests, and Chi-squared instead of F tests) and plug-in of other covariance matrices are
waldtest()in lmtest. Tests of more general linear hypotheses are implemented in
linearHypothesis()and for nonlinear hypotheses in
glm()from package stats. This includes in particular logit and probit models for modeling choice data and Poisson models for count data.
family = binomial. Bias-reduced GLMs that are robust to complete and quasi-complete separation are provided by brglm. Discrete choice models estimated by simulated maximum likelihood are implemented in Rchoice. bife provides binary choice models with fixed effects. Heteroscedastic probit models (and other heteroscedastic GLMs) are implemented in glmx along with parametric link functions and goodness-of-link tests for GLMs.
family = poissonas explained above. Negative binomial GLMs are available via
glm.nb()in package MASS. Another implementation of negative binomial models is provided by aod, which also contains other models for overdispersed data. Zero-inflated and hurdle count models are provided in package pscl. A reimplementation by the same authors is currently under development in countreg on R-Forge which also encompasses separate functions for zero-truncated regression, finite mixture models etc.
multinom()from package nnet. An implementation with both individual- and choice-specific variables is mlogit. Generalized multinomial logit models (e.g., with random effects etc.) are in gmnl. A flexible framework of various customizable choice models (including multinomial logit and nested logit among many others) is implemented in the apollo package. Generalized additive models (GAMs) for multinomial responses can be fitted with the VGAM package. A Bayesian approach to multinomial probit models is provided by MNP. Various Bayesian multinomial models (including logit and probit) are available in bayesm. Furthermore, the package RSGHB fits various hierarchical Bayesian specifications based on direct specification of the likelihood function.
polr()from package MASS. The package ordinal provides cumulative link models for ordered data which encompasses proportional odds models but also includes more general specifications. Bayesian ordered probit models are provided by bayesm.
survreg()in survival, a convenience interface
tobit()is in package AER. Further censored regression models, including models for panel data, are provided in censReg. Censored regression models with conditional heteroscedasticity are in crch. Furthermore, hurdle models for left-censored data at zero can be estimated with mhurdle. Models for sample selection are available in sampleSelection and ssmrob using classical and robust inference, respectively. Package matchingMarkets corrects for selection bias when the sample is the result of a stable matching process (e.g., a group formation or college admissions problem).
coxph()or Weibull models with
survreg(). Many more refined models can be found in the Survival task view. The Heckman and Singer mixed proportional hazard competing risk model is available in durmod (archived).
tsls()in package sem and ivpack,
glm()) and only correct the standard errors. Different types of clustered, panel, and panel-corrected standard errors are available in sandwich (incorporating prior work from multiwayvcov), clusterSEs, pcse, clubSandwich, plm, and geepack, respectively. The latter two require estimation of the pooling/independence models via
geeglm()from the respective packages (which also provide other types of models, see below).
nls()in package stats.
"ts"in package stats is R’s standard class for regularly spaced time series (especially annual, quarterly, and monthly data). It can be coerced back and forth without loss of information to
"zooreg"from package zoo.
"zoo") where the time information can be of arbitrary class. This includes daily series (typically with
"Date"time index) or intra-day series (e.g., with
"POSIXct"time index). An extension based on zoo geared towards time series with different kinds of time index is xts. Further packages aimed particularly at finance applications are discussed in the Finance task view.
ar()and ARIMA modeling and Box-Jenkins-type analysis can be carried out with
arima()(both in the stats package). An enhanced version of
arima()is in forecast.
lm()for estimating OLS and 2SLS models based on time series data is dynlm. Linear regression models with AR error terms via GLS is possible using
StructTS()in stats. Further packages are discussed in the TimeSeries task view.
HoltWinters()in stats. The basic function for computing filters (both rolling and autoregressive) is
filter()in stats. Many extensions to these methods, in particular for forecasting and model selection, are provided in the forecast package.
ar()in stats, more elaborate models are provided in package vars along with suitable diagnostics, visualizations etc. Panel vector autoregressions are available in panelvar.
tsbootstrap()from tseries. The fwildclusterboot package provides a fast wild cluster bootstrap implementation for linear regression models, especially when the number of clusters is low.
|Core:||AER, car, fixest, forecast, ivreg, lmtest, marginaleffects, plm, sandwich, tseries, urca, zoo.|
|Regular:||alpaca, aod, apollo, apt, bayesm, betareg, bife, bimets, BMA, boot, bootstrap, brglm, CADFtest, CDNmoney, censReg, clubSandwich, clusterSEs, collapse, crch, decompr, dlsem, dynlm, Ecdat, effects, erer, estimatr, expsmooth, ExtremeBounds, feisr, fma, frm, frontier, fwildclusterboot, fxregime, gam, gamlss, geepack, gets, glmx, gmm, gmnl, gravity, gvc, Hmisc, ineq, ivfixed, ivpack, ivpanel, ivprobit, LARF, lavaan, lfe, LinRegInteractive, lme4, lpirfs, margins, MASS, matchingMarkets, Matrix, Mcomp, meboot, mfx, mgcv, mhurdle, micEcon, micEconAids, micEconCES, micEconSNQP, midasr, mlogit, MNP, modelsummary, multiwayvcov, nlme, nnet, NNS, nonnest2, np, nse, ordinal, OrthoPanels, pampe, Paneldata, panelvar, pco, pcse, pder, pdR, pdynmc, pglm, pscl, psidR, pwt, pwt8, pwt9, quantreg, Rchoice, rdd, rdlocrand, rdmulti, rdpower, rdrobust, reldist, REndo, rms, RSGHB, sampleSelection, segmented, sem, semsfa, sfa, simpleboot, SparseM, spatialprobit, spatialreg, sphet, splm, spsur, ssfa, ssmrob, SteinIV, strucchange, survival, systemfit, truncreg, tsDyn, vars, VGAM, wahc, wbstats, wooldridge, xts, zTree.|