{"id":621,"date":"2014-05-01T22:10:59","date_gmt":"2014-05-02T03:10:59","guid":{"rendered":"http:\/\/homepages.uc.edu\/~yaozo\/wordpress\/?p=621"},"modified":"2014-05-01T22:10:59","modified_gmt":"2014-05-02T03:10:59","slug":"cran-task-view-computational-econometrics","status":"publish","type":"post","link":"https:\/\/zhuoyao.net\/index.php\/2014\/05\/01\/cran-task-view-computational-econometrics\/","title":{"rendered":"CRAN Task View: Computational Econometrics"},"content":{"rendered":"<h2 style=\"color: #666666;\">CRAN Task View: Computational Econometrics<\/h2>\n<table style=\"color: #000000;\" summary=\"Econometrics task view information\">\n<tbody>\n<tr>\n<td valign=\"top\"><b>Maintainer:<\/b><\/td>\n<td>Achim Zeileis<\/td>\n<\/tr>\n<tr>\n<td valign=\"top\"><b>Contact:<\/b><\/td>\n<td>Achim.Zeileis at R-project.org<\/td>\n<\/tr>\n<tr>\n<td valign=\"top\"><b>Version:<\/b><\/td>\n<td>2014-02-28<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div style=\"color: #000000;\">\n<p>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\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/Finance.html\">Finance<\/a>\u00a0view. Furthermore, the\u00a0<a style=\"color: blue;\" href=\"https:\/\/www.stat.math.ethz.ch\/mailman\/listinfo\/R-SIG-Finance\/\">Finance SIG\u00a0<\/a>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\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/SocialSciences.html\">SocialSciences<\/a>\u00a0that also covers a broad variety of tools for social sciences, e.g., including political science.<\/p>\n<p>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.<\/p>\n<p><strong>Linear regression models<\/strong><\/p>\n<ul>\n<li>Linear models can be fitted (via OLS) with\u00a0<tt>lm()<\/tt>\u00a0(from stats) and standard tests for model comparisons are available in various methods such as\u00a0<tt>summary()<\/tt>\u00a0and\u00a0<tt>anova()<\/tt>.<\/li>\n<li>Analogous functions that also support asymptotic tests (\u00a0<i>z\u00a0<\/i>instead of\u00a0<i>t\u00a0<\/i>tests, and Chi-squared instead of\u00a0<i>F\u00a0<\/i>tests) and plug-in of other covariance matrices are\u00a0<tt>coeftest()<\/tt>\u00a0and\u00a0<tt>waldtest()<\/tt>\u00a0in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/lmtest\/index.html\">lmtest<\/a>.<\/li>\n<li>Tests of more general linear hypotheses are implemented in\u00a0<tt>linear.hypothesis()<\/tt>\u00a0in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/car\/index.html\">car<\/a>.<\/li>\n<li>HC and HAC covariance matrices that can be plugged into these functions are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/sandwich\/index.html\">sandwich<\/a>.<\/li>\n<li>Diagnost checking: The packages\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/car\/index.html\">car<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/lmtest\/index.html\">lmtest<\/a>\u00a0provide a large collection of regression diagonstics and diagnostic tests.<\/li>\n<li>Instrumental variables regression (two-stage least squares) is provided by\u00a0<tt>ivreg()<\/tt>\u00a0in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/AER\/index.html\">AER<\/a>, another implementation is\u00a0<tt>tsls()<\/tt>\u00a0in package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/sem\/index.html\">sem<\/a>.<\/li>\n<\/ul>\n<p><strong>Microeconometrics<\/strong><\/p>\n<ul>\n<li>Many standard microeconometric models belong to the family of generalized linear models (GLM) and can be fitted by\u00a0<tt>glm()<\/tt>\u00a0from 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\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/effects\/index.html\">effects<\/a>. Marginal effects tables for certain GLMs can be obtained using the\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/mfx\/index.html\">mfx<\/a>\u00a0package.<\/li>\n<li>Negative binomial GLMs are available via\u00a0<tt>glm.nb()<\/tt>\u00a0in package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/MASS\/index.html\">MASS<\/a>. Another implementation of negative binomial models is provided by\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/aod\/index.html\">aod<\/a>, which also contains other models for overdispersed data.<\/li>\n<li>Zero-inflated and hurdle count models are provided in package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/pscl\/index.html\">pscl<\/a>.<\/li>\n<li>Multinomial responses: Multinomial models with individual-specific covariates only are available in\u00a0<tt>multinom()<\/tt>\u00a0from package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/nnet\/index.html\">nnet<\/a>. Implementations with both individual- and choice-specific variables are\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/mlogit\/index.html\">mlogit<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/mnlogit\/index.html\">mnlogit<\/a>. Generalized additive models (GAMs) for multinomial responses can be fitted with the\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/VGAM\/index.html\">VGAM<\/a>\u00a0package. A Bayesian approach to multinomial probit models is provided by\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/MNP\/index.html\">MNP<\/a>. Various Bayesian multinomial models (including logit and probit) are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/bayesm\/index.html\">bayesm<\/a>. Furthermore, the package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/RSGHB\/index.html\">RSGHB<\/a>\u00a0fits various hierarchical Bayesian specifications based on direct specification of the likelihood function.<\/li>\n<li>Ordered responses: Proportional-odds regression for ordered responses is implemented in\u00a0<tt>polr()<\/tt>\u00a0from package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/MASS\/index.html\">MASS<\/a>. The package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/ordinal\/index.html\">ordinal<\/a>\u00a0provides cumulative link models for ordered data which encompasses proportional odds models but also includes more general specifications. Bayesian ordered probit models are provided by\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/bayesm\/index.html\">bayesm<\/a>.<\/li>\n<li>Censored responses: Basic censored regression models (e.g., tobit models) can be fitted by\u00a0<tt>survreg()<\/tt>\u00a0in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/survival\/index.html\">survival<\/a>, a convenience interface\u00a0<tt>tobit()<\/tt>\u00a0is in package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/AER\/index.html\">AER<\/a>. Further censored regression models, including models for panel data, are provided in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/censReg\/index.html\">censReg<\/a>. Interval regression models are in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/intReg\/index.html\">intReg<\/a>. Censored regression models with conditional heteroskedasticity are in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/crch\/index.html\">crch<\/a>. Furthermore, hurdle models for left-censored data at zero can be estimated with\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/mhurdle\/index.html\">mhurdle<\/a>. Models for sample selection are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/sampleSelection\/index.html\">sampleSelection<\/a>\u00a0and semiparametric extensions of these are provided by<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/SemiParSampleSel\/index.html\">SemiParSampleSel<\/a>.<\/li>\n<li>Instrumental variables for binary responses: The\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/LARF\/index.html\">LARF<\/a>\u00a0package estimates local average response functions for binary treatments and binary instruments.<\/li>\n<li>Multivariate probit models: Estimation and marginal effect computations can be carried out with\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/mvProbit\/index.html\">mvProbit<\/a>.<\/li>\n<li>Miscellaneous: Further more refined tools for microecnometrics are provided in the\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/micEcon\/index.html\">micEcon<\/a>\u00a0family of packages: Analysis with Cobb-Douglas, translog, and quadratic functions is in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/micEcon\/index.html\">micEcon<\/a>; the constant elasticity of scale (CES) function is in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/micEconCES\/index.html\">micEconCES<\/a>; the symmetric normalized quadratic profit (SNQP) function is in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/micEconSNQP\/index.html\">micEconSNQP<\/a>. The almost ideal demand system (AIDS) is in<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/micEconAids\/index.html\">micEconAids<\/a>. Stochastic frontier analysis is in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/frontier\/index.html\">frontier<\/a>. The package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/bayesm\/index.html\">bayesm<\/a>\u00a0implements a Bayesian approach to microeconometrics and marketing. Inference for relative distributions is contained in package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/reldist\/index.html\">reldist<\/a>.<\/li>\n<\/ul>\n<p><strong>Further regression models<\/strong><\/p>\n<ul>\n<li>Nonlinear least squares modeling is availble in\u00a0<tt>nls()<\/tt>\u00a0in package stats.<\/li>\n<li>Quantile regression:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/quantreg\/index.html\">quantreg<\/a>\u00a0(including linear, nonlinear, censored, locally polynomial and additive quantile regressions).<\/li>\n<li>Linear models for panel data:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/plm\/index.html\">plm<\/a>, 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, see\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/geepack\/index.html\">geepack<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/pcse\/index.html\">pcse<\/a>. Estimation of linear models with multiple group fixed effects is contained in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/lfe\/index.html\">lfe<\/a>.<\/li>\n<li>Generalized method of moments (GMM) and generalized empirical likelihood (GEL):\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/gmm\/index.html\">gmm<\/a>.<\/li>\n<li>Spatial econometric models: The\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/Spatial.html\">Spatial<\/a>\u00a0view gives details about handling spatial data, along with information about (regression) modeling. In particular, spatial regression models can be fitted using<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/spdep\/index.html\">spdep<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/sphet\/index.html\">sphet<\/a>\u00a0(the latter using a GMM approach).\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/splm\/index.html\">splm<\/a>\u00a0is a package for spatial panel models. Spatial probit models are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/spatialprobit\/index.html\">spatialprobit<\/a>.<\/li>\n<li>Linear structural equation models:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/sem\/index.html\">sem<\/a>\u00a0(including two-stage least squares).<\/li>\n<li>Simultaneous equation estimation:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/systemfit\/index.html\">systemfit<\/a>.<\/li>\n<li>Nonparametric kernel methods:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/np\/index.html\">np<\/a>.<\/li>\n<li>Beta regression:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/betareg\/index.html\">betareg<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/gamlss\/index.html\">gamlss<\/a>.<\/li>\n<li>Truncated (Gaussian) regression:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/truncreg\/index.html\">truncreg<\/a>.<\/li>\n<li>Nonlinear mixed-effect models:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/nlme\/index.html\">nlme<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/lme4\/index.html\">lme4<\/a>.<\/li>\n<li>Generalized additive models (GAMs):\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/mgcv\/index.html\">mgcv<\/a>,\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/gam\/index.html\">gam<\/a>,\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/gamlss\/index.html\">gamlss<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/VGAM\/index.html\">VGAM<\/a>.<\/li>\n<li>Mixed data sampling regression:\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/midasr\/index.html\">midasr<\/a>.<\/li>\n<li>Miscellaneous: The packages\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/VGAM\/index.html\">VGAM<\/a>,\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/rms\/index.html\">rms<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/Hmisc\/index.html\">Hmisc<\/a>\u00a0provide several tools for extended handling of (generalized) linear regression models.\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/Zelig\/index.html\">Zelig<\/a>\u00a0is a unified easy-to-use interface to a wide range of regression models.<\/li>\n<\/ul>\n<p><strong>Basic time series infrastructure<\/strong><\/p>\n<ul>\n<li>The\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/TimeSeries.html\">TimeSeries<\/a>\u00a0task view provides much more detailed information. Here, only the most important aspects are briefly mentioned.<\/li>\n<li>The class\u00a0<tt>\"ts\"<\/tt>\u00a0in package stats is R&#8217;s standard class for regularly spaced time series (especially annual, quarterly, and monthly data).<\/li>\n<li>Time series in\u00a0<tt>\"ts\"<\/tt>\u00a0format can be coerced back and forth without loss of information to\u00a0<tt>\"zooreg\"<\/tt>\u00a0from package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/zoo\/index.html\">zoo<\/a>.\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/zoo\/index.html\">zoo<\/a>\u00a0provides infrastructure for both regularly and irregularly spaced time series (the latter via the class\u00a0<tt>\"zoo\"<\/tt>) where the time information can be of arbitrary class. This includes daily series (typically with\u00a0<tt>\"Date\"<\/tt>\u00a0time index) or intra-day series (e.g., with\u00a0<tt>\"POSIXct\"<\/tt>\u00a0time index).<\/li>\n<li>Several other implementations of irregular time series building on the\u00a0<tt>\"POSIXct\"<\/tt>\u00a0time-date class are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/its\/index.html\">its<\/a>,\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/tseries\/index.html\">tseries<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/timeSeries\/index.html\">timeSeries<\/a>\u00a0(previously: fSeries) which are all aimed particularly at finance applications. See the\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/Finance.html\">Finance<\/a>\u00a0task view for more information.<\/li>\n<\/ul>\n<p><strong>Time series modeling<\/strong><\/p>\n<ul>\n<li>The\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/TimeSeries.html\">TimeSeries<\/a>\u00a0task 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 the\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/Finance.html\">Finance<\/a>. Here, only a brief overview of the most important methods for econometrics is given.<\/li>\n<li>Classical time series modeling tools are contained in the stats package and include\u00a0<tt>arima()<\/tt>\u00a0for ARIMA modeling and Box-Jenkins-type analysis.<\/li>\n<li>Fitting linear regression models with AR error terms via OLS is possible using\u00a0<tt>gls()<\/tt>\u00a0from\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/nlme\/index.html\">nlme<\/a>.<\/li>\n<li>Structural time series models are provided by\u00a0<tt>StructTS()<\/tt>\u00a0in stats.<\/li>\n<li>Filtering and decomposition for time series is available in\u00a0<tt>decompose()<\/tt>\u00a0and\u00a0<tt>HoltWinters()<\/tt>\u00a0in stats.<\/li>\n<li>Extensions to these methods, in particular for forecasting and model selection, are provided in the\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/forecast\/index.html\">forecast<\/a>\u00a0package.<\/li>\n<li>Miscellaneous time series filters are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/mFilter\/index.html\">mFilter<\/a>.<\/li>\n<li>For estimating VAR models, several methods are available: simple models can be fitted by\u00a0<tt>ar()<\/tt>\u00a0in stats, more elaborate models are provided in package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/vars\/index.html\">vars<\/a>,\u00a0<tt>estVARXls()<\/tt>\u00a0in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/dse\/index.html\">dse<\/a>\u00a0and a Bayesian approach is available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/MSBVAR\/index.html\">MSBVAR<\/a>. A convenient interface for fitting dynamic regression models via OLS is available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/dynlm\/index.html\">dynlm<\/a>; a different approach that also works with other regression functions is implemented in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/dyn\/index.html\">dyn<\/a>.<\/li>\n<li>More advanced dynamic system equations can be fitted using\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/dse\/index.html\">dse<\/a>.<\/li>\n<li>Periodic autoregressive models are provided by\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/partsm\/index.html\">partsm<\/a>.<\/li>\n<li>Gaussian linear state space models can be fitted using\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/dlm\/index.html\">dlm<\/a>\u00a0(via maximum likelihood, Kalman filtering\/smoothing and Bayesian methods).<\/li>\n<li>Unit root and cointegration techniques are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/urca\/index.html\">urca<\/a>,\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/tseries\/index.html\">tseries<\/a>,\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/CADFtest\/index.html\">CADFtest<\/a>.<\/li>\n<li>Time series factor analysis is available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/tsfa\/index.html\">tsfa<\/a>.<\/li>\n<li>Asymmetric price transmission modeling is available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/apt\/index.html\">apt<\/a>.<\/li>\n<\/ul>\n<p><strong>Data sets<\/strong><\/p>\n<ul>\n<li>Packages\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/AER\/index.html\">AER<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/Ecdat\/index.html\">Ecdat<\/a>\u00a0contain 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 &amp; Economic Statistics data archives.<\/li>\n<li><a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/AER\/index.html\">AER<\/a>\u00a0additionally provides an extensive set of examples reproducing analyses from the textbooks\/papers, illustrating various econometric methods.<\/li>\n<li><a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/FinTS\/index.html\">FinTS<\/a>\u00a0is the R companion to Tsay&#8217;s &#8216;Analysis of Financial Time Series&#8217; (2nd ed., 2005, Wiley) containing data sets, functions and script files required to work some of the examples.<\/li>\n<li><a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/CDNmoney\/index.html\">CDNmoney<\/a>\u00a0provides Canadian monetary aggregates.<\/li>\n<li><a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/pwt\/index.html\">pwt<\/a>\u00a0provides the Penn World Table from versions 5.6, 6.x, 7.x. The version 8.x data are available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/pwt8\/index.html\">pwt8<\/a>.<\/li>\n<li>The packages\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/expsmooth\/index.html\">expsmooth<\/a>,\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/fma\/index.html\">fma<\/a>, and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/Mcomp\/index.html\">Mcomp<\/a>\u00a0are data packages with time series data from the books &#8216;Forecasting with Exponential Smoothing: The State Space Approach&#8217; (Hyndman, Koehler, Ord, Snyder, 2008, Springer) and &#8216;Forecasting: Methods and Applications&#8217; (Makridakis, Wheelwright, Hyndman, 3rd ed., 1998, Wiley) and the M-competitions, respectively.<\/li>\n<li>Package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/erer\/index.html\">erer<\/a>\u00a0contains functions and datasets for the book of &#8216;Empirical Research in Economics: Growing up with R&#8217; (Sun, forthcoming).<\/li>\n<li>The package\u00a0<a style=\"color: blue;\" href=\"http:\/\/github.com\/floswald\/psidR\/\">psidR\u00a0<\/a>available from GitHub can build panel data sets from the Panel Study of Income Dynamics (PSID).<\/li>\n<\/ul>\n<p><strong>Miscellaneous<\/strong><\/p>\n<ul>\n<li><i>Matrix manipulations\u00a0<\/i>: As a vector- and matrix-based language, base R ships with many powerful tools for doing matrix manipulations, which are complemented by the packages\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/Matrix\/index.html\">Matrix<\/a>\u00a0and<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/SparseM\/index.html\">SparseM<\/a>.<\/li>\n<li><i>Optimization and mathematical programming\u00a0<\/i>: 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\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/views\/Optimization.html\">Optimization<\/a>\u00a0task view.<\/li>\n<li><i>Bootstrap\u00a0<\/i>: In addition to the recommended\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/boot\/index.html\">boot<\/a>\u00a0package, there are some other general bootstrapping techniques available in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/bootstrap\/index.html\">bootstrap<\/a>\u00a0or\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/simpleboot\/index.html\">simpleboot<\/a>\u00a0as well some bootstrap techniques designed for time-series data, such as the maximum entropy bootstrap in\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/meboot\/index.html\">meboot<\/a>\u00a0or the\u00a0<tt>tsbootstrap()<\/tt>\u00a0from\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/tseries\/index.html\">tseries<\/a>.<\/li>\n<li><i>Inequality\u00a0<\/i>: For measuring inequality, concentration and poverty the package\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/ineq\/index.html\">ineq<\/a>\u00a0provides some basic tools such as Lorenz curves, Pen&#8217;s parade, the Gini coefficient and many more.<\/li>\n<li><i>Structural change\u00a0<\/i>: R is particularly strong when dealing with structural changes and changepoints in parametric models, see\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/strucchange\/index.html\">strucchange<\/a>\u00a0and\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/segmented\/index.html\">segmented<\/a>.<\/li>\n<li><i>Exchange rate regimes\u00a0<\/i>: Methods for inference about exchange rate regimes, in particular in a structural change setting, are provided by\u00a0<a style=\"color: blue;\" href=\"http:\/\/cran.r-project.org\/web\/packages\/fxregime\/index.html\">fxregime<\/a>.<\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>CRAN Task View: Computational Econometrics Maintainer: Achim Zeileis Contact: Achim.Zeileis at R-project.org Version: 2014-02-28 Base R ships with a lot of functionality useful for computational&hellip; <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-621","post","type-post","status-publish","format-standard","hentry","category-r"],"_links":{"self":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/621","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/comments?post=621"}],"version-history":[{"count":0,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/621\/revisions"}],"wp:attachment":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/media?parent=621"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/categories?post=621"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/tags?post=621"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}