CRAN Task View: Computational Econometrics

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 Finance view. Furthermore, the finance SIG is a suitable mailing list for obtaining help and discussing questions about both computational finance and econometrics. Finally, there is also some overlap with the SocialSciences that also covers a broad variety of tools for social sciences, e.g., including political science. 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() in lmtest. Tests of more general linear hypotheses are implemented in linear.hypothesis() in car. HC and HAC covariance matrices that can be plugged into these functions are available in sandwich. The packages car and lmtest also provide a large collection of further methods for diagnost checking in linear regression models.
• 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 modelling choice data and poisson models for count data. Negative binomial GLMs are available via glm.nb() in package MASS from the VR bundle. Zero-inflated and hurdle count models are provided in in package pscl. Bivariate poisson regression models are implemented in bivpois. Basic censored regression models (e.g., tobit models) can be fitted by survreg() in survival. Further more refined tools for microecnometrics are provided in micEcon. The package bayesm implements a Bayesian approach to microeconometrics and marketing. Inference for relative distributions is contained in package reldist.
• Further regression models : Various extensions of the linear regression model and other model fitting techniques are available in base R and several CRAN packages. Nonlinear least squares modelling is availble in nls() in package stats. Relevant packages include quantreg (quantile regression), crq (censored quantile regression), plm (linear models for panel data), sem (linear structural equation models, including two-stage least squares), systemfit (simultaneous equation estimation), np (nonparametric kernel methods), betareg (beta regression), nlme (nonlinear mixed-effect models), VR (multinomial logit models in package nnet) and MNP (Bayesian multinomial probit models). The packages Design and Hmisc provide several tools for extended handling of (generalized) linear regression models.
• Basic time series infrastructure : The class "ts" in package stats is R's standard class for regularly spaced time series which can be coerced back and forth without loss of information to "zooreg" from package zoo. 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. Several other implementations of irregular time series building on the "POSIXt" time-date classes are available in its, tseries and fCalendar which are all aimed particularly at finance applications (see the Finance view).
• Time series modelling : Classical time series modelling tools are contained in the stats package and include arima() for ARIMA modelling and Box-Jenkins-type analysis. Furthermore, stats provides StructTS() for fitting structural time series and decompose() and HoltWinters() for time series filtering and decomposition. Some extensions to these methods, in particular for forecasting and model selection, are provided in the forecasting bundle. 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 package vars, estVARXls() in dse and a Bayesian approach is available in MSBVAR. A convenient interface for fitting dynamic regression models via OLS is available in dynlm; a different approach that also works with other regression functions is implemented in dyn. More advanced dynamic system equations can be fitted using dse. 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, uroot and tseries. Time series factor analysis is available in tsfa. Package sde provides simulation and inference for stochastic differential equations.
• 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.
• 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.
• Data sets : Many of the packages in this view contain collections of data sets from the econometric literature and the package Ecdat contains a complete collection 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. 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. Package CDNmoney provides Canadian monetary aggregates and pwt provides (several releases of) the Penn world table.

CRAN packages:

• bayesm
• betareg
• bivpois
• boot
• bootstrap
• car (core)
• CDNmoney
• crq
• Design
• dlm
• dse
• dyn
• dynlm
• Ecdat
• fCalendar
• FinTS
• forecasting
• Hmisc
• ineq
• its
• lmtest (core)
• Matrix
• meboot
• mFilter
• micEcon
• MNP
• MSBVAR
• nlme
• np
• plm
• pscl
• pwt
• quantreg
• reldist
• sandwich (core)
• sde
• segmented
• sem
• simpleboot
• SparseM
• strucchange
• systemfit
• tseries (core)
• tsfa
• urca (core)
• uroot
• vars
• VR
• zoo (core)

Related links:

• CRAN Task View: Finance
• CRAN Task View: SocialSciences
• Mailing list: R Special Interest Group Finance
• A Brief Guide to R for Beginners in Econometrics
• R for Economists