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 for finance in the

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 let me know.

**Linear regression models**

- Linear models can be fitted (via OLS) with
`lm()`

(from stats) and standard tests for model comparisons are available in various methods such as`summary()`

and`anova()`

. - Analogous functions
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`coeftest()`

and`waldtest()`

inlmtest . - Tests of more general linear hypotheses are implemented in
`linear.hypothesis()`

incar . - HC and HAC covariance matrices that can be plugged
into these functions are available in
sandwich . - Diagnost checking: The packages
car andlmtest provide a large collection of regression diagonstics and diagnostic tests. - Instrumental variables regression (two-stage least squares) is
provided by
`ivreg()`

inAER , another implementation is`tsls()`

in packagesem .

**Microeconometrics**

- Many standard microeconometric models belong to the
family of generalized linear models (GLM) and can be fitted by
`glm()`

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 usingeffects . Marginal effects tables for certain GLMs can be obtained using themfx package. Interactive visualizations of both effects and marginal effects are possible inLinRegInteractive . - Negative binomial GLMs are available via
`glm.nb()`

in packageMASS . Another implementation of negative binomial models is provided byaod , which also contains other models for overdispersed data. - Zero-inflated and hurdle count models are provided in package
pscl . - Multinomial responses: Multinomial models
with individual-specific covariates only are available in
`multinom()`

from packagennet . Implementations with both individual- and choice-specific variables aremlogit andmnlogit . Generalized additive models (GAMs) for multinomial responses can be fitted with theVGAM package. A Bayesian approach to multinomial probit models is provided byMNP . Various Bayesian multinomial models (including logit and probit) are available inbayesm . Furthermore, the packageRSGHB fits various hierarchical Bayesian specifications based on direct specification of the likelihood function. - Ordered responses: Proportional-odds regression for ordered responses is implemented
in
`polr()`

from packageMASS . The packageordinal provides cumulative link models for ordered data which encompasses proportional odds models but also includes more general specifications. Bayesian ordered probit models are provided bybayesm . - Censored responses: Basic censored regression models (e.g., tobit models)
can be fitted by
`survreg()`

insurvival , a convenience interface`tobit()`

is in packageAER . Further censored regression models, including models for panel data, are provided incensReg . Interval regression models are inintReg . Censored regression models with conditional heteroskedasticity are incrch . Furthermore, hurdle models for left-censored data at zero can be estimated withmhurdle . Models for sample selection are available insampleSelection and semiparametric extensions of these are provided bySemiParSampleSel . - Instrumental variables for binary responses: The
LARF package estimates local average response functions for binary treatments and binary instruments. - Multivariate probit models: Estimation and marginal effect computations can be
carried out with
mvProbit . - Miscellaneous: Further more refined tools for microecnometrics are provided in
the
micEcon family of packages: Analysis with Cobb-Douglas, translog, and quadratic functions is inmicEcon ; the constant elasticity of scale (CES) function is inmicEconCES ; the symmetric normalized quadratic profit (SNQP) function is inmicEconSNQP . The almost ideal demand system (AIDS) is inmicEconAids . Stochastic frontier analysis is infrontier . The packagebayesm implements a Bayesian approach to microeconometrics and marketing. Inference for relative distributions is contained in packagereldist .

**Further regression models**

- Nonlinear least squares modeling is availble in
`nls()`

in package stats. - Quantile regression:
quantreg (including linear, nonlinear, censored, locally polynomial and additive quantile regressions). - Linear models for panel data:
plm , providing a wide range of within, between, and random-effect methods (among others) along with corrected standard errors, tests, etc. For panel-corrected standard errors in OLS and GEE models, seegeepack andpcse . Estimation of linear models with multiple group fixed effects is contained inlfe . Packagephtt offers the possibility of analyzing panel data with large dimensions n and T and can be considered when the unobserved heterogeneity effects are time-varying. - Generalized method of moments (GMM) and generalized empirical likelihood (GEL):
gmm . - 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 usingspdep andsphet (the latter using a GMM approach).splm is a package for spatial panel models. Spatial probit models are available inspatialprobit . - Linear structural equation models:
sem (including two-stage least squares). - Simultaneous equation estimation:
systemfit . - Nonparametric kernel methods:
np . - Beta regression:
betareg andgamlss . - Truncated (Gaussian) regression:
truncreg . - Nonlinear mixed-effect models:
nlme andlme4 . - Generalized additive models (GAMs):
mgcv ,gam ,gamlss andVGAM . - Mixed data sampling regression:
midasr . - Miscellaneous: The packages
VGAM ,rms andHmisc provide several tools for extended handling of (generalized) linear regression models.Zelig is a unified easy-to-use interface to a wide range of regression models.

**Basic time series infrastructure**

- The
TimeSeries task view provides much more detailed information. Here, only the most important aspects are briefly mentioned. - The class
`"ts"`

in package stats is R's standard class for regularly spaced time series (especially annual, quarterly, and monthly data). - Time series in
`"ts"`

format can be coerced back and forth without loss of information to`"zooreg"`

from packagezoo .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). - Several
other implementations of irregular time series building on the
`"POSIXct"`

time-date class are available inits ,tseries andtimeSeries (previously: fSeries) which are all aimed particularly at finance applications. See theFinance task view for more information.

**Time series modeling**

- The
TimeSeries task view contains detailed information about time series analysis in R. Time series models for financial econometrics (e.g., GARCH, stochastic volatility models, or stochastic differential equations, etc.) are described in theFinance . Here, only a brief overview of the most important methods for econometrics is given. - Classical time series modeling tools are
contained in the stats package and include
`arima()`

for ARIMA modeling and Box-Jenkins-type analysis. - Fitting linear regression models with AR error terms via OLS is possible
using
`gls()`

fromnlme . - Structural time series models are provided by
`StructTS()`

in stats. - Filtering and decomposition for time series is available in
`decompose()`

and`HoltWinters()`

in stats. - Extensions to these
methods, in particular for forecasting and model selection, are provided in
the
forecast package. - Miscellaneous time series filters are available in
mFilter . - For estimating VAR models, several
methods are available: simple models can be fitted by
`ar()`

in stats, more elaborate models are provided in packagevars and`estVARXls()`

indse . A convenient interface for fitting dynamic regression models via OLS is available indynlm ; a different approach that also works with other regression functions is implemented indyn . - More advanced dynamic system equations can be fitted using
dse . - Various linear and nonlinear autoregressive time series models are provided by
tsDyn . - Periodic autoregressive models are provided by
partsm . - Gaussian linear state space models can be fitted using
dlm (via maximum likelihood, Kalman filtering/smoothing and Bayesian methods). - Unit root and cointegration techniques are available in
urca ,tseries ,CADFtest . - Time series factor analysis is available in
tsfa . - Asymmetric price transmission modeling is available in
apt .

**Data sets**

- Packages
AER andEcdat contain a comprehensive collections of data sets from various standard econometric textbooks as well as several data sets from the Journal of Applied Econometrics and the Journal of Business & Economic Statistics data archives. AER additionally provides an extensive set of examples reproducing analyses from the textbooks/papers, illustrating various econometric methods.FinTS is the R companion to Tsay's 'Analysis of Financial Time Series' (2nd ed., 2005, Wiley) containing data sets, functions and script files required to work some of the examples.CDNmoney provides Canadian monetary aggregates.pwt provides the Penn World Table from versions 5.6, 6.x, 7.x. The version 8.x data are available inpwt8 .- The packages
expsmooth ,fma , andMcomp 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, respectively. - Package
erer contains functions and datasets for the book of 'Empirical Research in Economics: Growing up with R' (Sun, forthcoming). - The package psidR available from GitHub can build panel data sets from the Panel Study of Income Dynamics (PSID).

**Miscellaneous**

*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 packagesMatrix andSparseM .*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 theOptimization task view.*Bootstrap*: In addition to the recommendedboot package, there are some other general bootstrapping techniques available inbootstrap orsimpleboot as well some bootstrap techniques designed for time-series data, such as the maximum entropy bootstrap inmeboot or the`tsbootstrap()`

fromtseries .*Inequality*: For measuring inequality, concentration and poverty the packageineq 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, seestrucchange andsegmented .*Exchange rate regimes*: Methods for inference about exchange rate regimes, in particular in a structural change setting, are provided byfxregime .