Last updated on 2020-10-28
by Achim Zeileis
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, SocialSciences, 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 contact the maintainer.
Basic linear regression
- Estimation and standard inference: Ordinary least squares (OLS) estimation for linear models is provided
lm() (from stats) and standard tests for model comparisons are available in various
methods such as
- Further inference and nested model comparisons: Functions analogous to
that also support asymptotic tests (z instead of t tests, and
Chi-squared instead of F tests) and plug-in of other covariance
waldtest() in lmtest.
Tests of more general linear hypotheses are implemented in
and for nonlinear hypotheses in
deltaMethod() in car.
- Robust standard errors: HC, HAC, clustered, and bootstrap covariance matrices are
available in sandwich and can be plugged into the inference functions mentioned above.
- Nonnested model comparisons: Various tests for comparing non-nested linear
models are available in lmtest (encompassing test, J test, Cox test).
The Vuong test for comparing other non-nested models is provided by nonnest2
(and specifically for count data regression in pscl).
- Diagnostic checking: The packages
car and lmtest provide a large collection
of regression diagnostics and diagnostic tests. In addition to these two packages,
skedastic contains further diagnostics specifically for detecting
- Generalized linear models (GLMs): Many standard microeconometric models belong to the
family of generalized linear models and can be fitted by
from package stats. This includes in particular logit and probit models
for modeling choice data and Poisson models for count data.
Effects for typical
values of regressors in these models can be obtained and visualized using effects.
Marginal effects tables for certain GLMs can be obtained using the
margins and mfx packages. Interactive visualizations of both effects and marginal
effects are possible in LinRegInteractive.
- Binary responses: The standard logit and probit models (among many others) for binary
responses are GLMs that can be estimated by
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.
- Count responses: The basic Poisson regression is a GLM that can be estimated by
family = poisson as 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.
- Multinomial responses: Multinomial models
with individual-specific covariates only are available in
from package nnet. An implementation with both individual- and
choice-specific variables is mlogit and mnlogit. 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
- Ordered responses: Proportional-odds regression for ordered responses is implemented
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.
- Censored responses: Basic censored regression models (e.g., tobit models)
can be fitted by
survreg() in survival, a convenience
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.
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).
- Truncated responses: crch for truncated (and potentially heteroscedastic)
Gaussian, logistic, and t responses. Homoscedastic Gaussian responses are also available in
- Fraction and proportion responses: Fractional response models are in frm.
Beta regression for responses in (0, 1) is in betareg and gamlss.
- Duration responses: Many classical duration models can be fitted with survival,
e.g., Cox proportional hazard models with
coxph() or Weibull models with
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.
- High-dimensional fixed effects: Linear models with potentially high-dimensional
fixed effects, also for multiple groups, can be fitted by lfe.
The corresponding GLMs are covered in alpaca. Another implementation, based on
C++ code covering both OLS and GLMs is in fixest.
- Miscellaneous: Further more refined tools for microeconometrics are provided in
the micEcon family of packages: Analysis with
Cobb-Douglas, translog, and quadratic functions is in micEcon;
the constant elasticity of scale (CES) function is in micEconCES;
the symmetric normalized quadratic profit (SNQP) function is in micEconSNQP.
The almost ideal demand system (AIDS) is in micEconAids.
Stochastic frontier analysis (SFA) is in frontier and certain special cases also in sfa.
Semiparametric SFA in is available in semsfa and spatial SFA in spfrontier and ssfa.
The package bayesm implements a Bayesian approach to microeconometrics and marketing.
Inference for relative distributions is contained in package reldist.
- Basic instrumental variables (IV) regression: Two-stage least squares (2SLS)
is provided by
ivreg() in AER. Other implementations are in
in package sem, in ivpack, and lfe (with particular
focus on multiple group fixed effects).
- Binary responses: An IV probit model via GLS estimation
is available in ivprobit. The LARF package estimates
local average response functions for binary treatments and binary instruments.
- Panel data: Certain basic IV models for panel data can also be estimated
with standard 2SLS functions (see above). Dedicated IV panel data models are provided
by ivfixed (fixed effects) and ivpanel (between and random effects).
REndo fits linear models with endogenous regressor using various latent instrumental variable approaches.
Panel data models
- Panel standard errors: A simple approach for panel data is
to fit the pooling (or independence) model (e.g., via
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, plm,
and geepack, respectively. The latter two require estimation of the
pooling/independence models via
the respective packages (which also provide other types of models, see below).
- Linear panel models: plm, providing a wide range of within,
between, and random-effect methods (among others) along with corrected standard
errors, tests, etc. Another implementation of several of these models is in
Paneldata. Various dynamic panel models are available in plm
and dynamic panel models with fixed effects in OrthoPanels.
feisr provides fixed effects individual slope (FEIS) models.
Panel vector autoregressions are implemented in panelvar.
- Generalized estimation equations and GLMs: GEE models for panel data (or longitudinal
data in statistical jargon) are in geepack. The pglm package provides
estimation of GLM-like models for panel data.
- Mixed effects models: Linear and nonlinear models for panel data (and more
general multi-level data) are available in lme4 and nlme.
- Instrumental variables: ivfixed and ivpanel, see also above.
Autocorrelation and heteroscedasticity correction are available in wahc and panelAR.
Threshold regression and unit root tests are in pdR.
The panel data approach method for program evaluation is available in pampe.
Dedicated fast data preprocessing for panel data econometrics is provided by collapse.
Further regression models
- Nonlinear least squares modeling:
nls() in package stats.
- Quantile regression: quantreg (including linear, nonlinear, censored,
locally polynomial and additive quantile regressions).
- Generalized method of moments (GMM) and generalized empirical likelihood (GEL):
- Spatial econometric models: The Spatial view gives details about
handling spatial data, along with information about (regression) modeling. In particular,
spatial regression models can be fitted using spatialreg and sphet (the
latter using a GMM approach). splm is a package for spatial panel
models. Spatial probit models are available in spatialprobit.
- Bayesian model averaging (BMA): A comprehensive toolbox for BMA is provided by
BMS including flexible prior selection, sampling, etc. A different implementation
is in BMA for linear models, generalizable linear models and survival models (Cox regression).
- Linear structural equation models: lavaan and sem.
See also the Psychometrics task view for more details.
- Simultaneous equation estimation: systemfit.
- Nonparametric methods: np using kernel smoothing and NNS using partial moments.
- Linear and nonlinear mixed-effect models: nlme and lme4.
- Generalized additive models (GAMs): mgcv, gam, gamlss
- Design-based inference: estimatr contains fast procedures for several
design-appropriate estimators with robust standard errors and confidence intervals including
linear regression, instrumental variables regression, difference-in-means, among others.
- Extreme bounds analysis: ExtremeBounds.
- Miscellaneous: The packages VGAM, rms and Hmisc provide several tools for extended
handling of (generalized) linear regression models.
Time series data and models
- The TimeSeries task view provides much more detailed
information about both basic time series infrastructure and time series models.
Here, only the most important aspects relating to econometrics are briefly mentioned.
Time series models for financial econometrics (e.g., GARCH, stochastic volatility models, or
stochastic differential equations, etc.) are described in the Finance task view.
- Infrastructure for regularly spaced time series: The class
"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
from package zoo.
- Infrastructure for irregularly spaced time series: zoo provides infrastructure for
both regularly and irregularly spaced time series (the latter via the class
"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.
- Classical time series models: Simple autoregressive models can be estimated
ar() and ARIMA modeling and Box-Jenkins-type analysis can be
carried out with
arima() (both in the stats package). An enhanced
arima() is in forecast.
- Linear regression models: A convenience interface to
for estimating OLS and 2SLS models based on time series data is dynlm.
Linear regression models with AR error terms via GLS is possible
gls() from nlme.
- Structural time series models: Standard models can be fitted with
StructTS() in stats.
Further packages are discussed in the TimeSeries task view.
- Filtering and decomposition:
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.
- Vector autoregression: Simple models can be fitted by
ar() in stats, more
elaborate models are provided in package vars along with suitable diagnostics,
visualizations etc. Panel vector autoregressions are available in panelvar.
- Unit root and cointegration tests: urca,
tseries, CADFtest. See also pco for panel cointegration tests.
- tsDyn - Threshold and smooth transition models.
- midasr - MIDAS regression and other econometric methods for mixed frequency time series data analysis.
- gets - GEneral-To-Specific (GETS) model selection for either ARX models with log-ARCH-X errors, or a log-ARCH-X model of the log variance.
- tsfa - Time series factor analysis.
- bimets - Econometric modeling of time series data using flexible specifications of simultaneous equation models.
- dlsem - Distributed-lag linear structural equation models.
- lpirfs - Local projections impulse response functions.
- apt - Asymmetric price transmission models.
- Textbooks and journals: Packages AER, Ecdat, and wooldridge
contain a comprehensive collections of data sets from various standard econometric
textbooks (including Greene, Stock & Watson, Wooldridge, Baltagi, among others) as well as
several data sets from the Journal of Applied Econometrics and the Journal of Business & Economic Statistics
data archives. AER and wooldridge additionally provide extensive sets of
examples reproducing analyses from the textbooks/papers, illustrating
various econometric methods. In pder a wide collection of data sets for
"Panel Data Econometrics with R" (Croissant & Millo 2018) is available.
The ccolonescu/PoEdata package on GitHub provides
the data sets from "Principles of Econometrics" (4th ed, by Hill, Griffiths, and Lim 2011).
- Canadian monetary aggregates: CDNmoney.
- Penn World Table: pwt provides versions 5.6, 6.x, 7.x. Version 8.x and 9.x
data are available in pwt8 and pwt9, respectively.
- Time series and forecasting data: The packages expsmooth, fma, and Mcomp are
data packages with time series data
from the books 'Forecasting with Exponential Smoothing: The State Space Approach'
(Hyndman, Koehler, Ord, Snyder, 2008, Springer) and 'Forecasting: Methods and Applications'
(Makridakis, Wheelwright, Hyndman, 3rd ed., 1998, Wiley) and the M-competitions,
- Empirical Research in Economics: Package erer contains functions and datasets for the book of
'Empirical Research in Economics: Growing up with R' (Sun, forthcoming).
- Panel Study of Income Dynamics (PSID): psidR can build panel data sets
from the Panel Study of Income Dynamics (PSID).
- US state- and county-level panel data: rUnemploymentData.
- Matrix manipulations: As a vector- and matrix-based language, base R
ships with many powerful tools for doing matrix manipulations, which are
complemented by the packages Matrix and SparseM.
- Optimization and mathematical programming: R and many of its contributed
packages provide many specialized functions for solving particular optimization
problems, e.g., in regression as discussed above. Further functionality for
solving more general optimization problems, e.g., likelihood maximization, is
discussed in the the Optimization task view.
- Bootstrap: In addition to the recommended boot package,
there are some other general bootstrapping techniques available in
bootstrap or simpleboot as well some bootstrap techniques
designed for time-series data, such as the maximum entropy bootstrap in
meboot or the
tsbootstrap() from tseries.
- Inequality: For measuring inequality, concentration and poverty the
package ineq provides some basic tools such as Lorenz curves,
Pen's parade, the Gini coefficient and many more.
- Structural change: R is particularly strong when dealing with
structural changes and changepoints in parametric models, see
strucchange and segmented.
- Exchange rate regimes: Methods for inference about exchange
rate regimes, in particular in a structural change setting, are provided
- Global value chains: Tools and decompositions for global value
chains are in gvc and decompr.
- Regression discontinuity design: A variety of methods are provided in
the rdd, rddtools, rdrobust, and rdlocrand packages.
The rdpower package offers power calculations for regression discontinuity designs.
And rdmulti implements analysis with multiple cutoffs or scores.
- Gravity models: Estimation of log-log and multiplicative gravity models
is available in gravity.
- z-Tree: zTree can import data from the z-Tree software for
developing and carrying out economic experiments.
- Numerical standard errors: nse implements various numerical standard
errors for time series data, especially in simulation experiments with correlated