{ "_id": "6a1eeee2b401979e73412a9b", "Package": "cointReg", "Type": "Package", "Title": "Parameter Estimation and Inference in a Cointegrating Regression", "Date": "2016-06-14", "Version": "0.2.0.9000", "Authors@R": "c(\nperson(\"Philipp\", \"Aschersleben\", role = c(\"aut\", \"cre\"),\nemail = \"aschersleben@statistik.tu-dortmund.de\"),\nperson(\"Martin\", \"Wagner\", role = \"aut\",\nemail = \"wagner@statistik.tu-dortmund.de\",\ncomment = \"Author of underlying MATLAB code.\"))", "Description": "Cointegration methods are widely used in empirical\nmacroeconomics and empirical finance. It is well known that in\na cointegrating regression the ordinary least squares (OLS)\nestimator of the parameters is super-consistent, i.e. converges\nat rate equal to the sample size T. When the regressors are\nendogenous, the limiting distribution of the OLS estimator is\ncontaminated by so-called second order bias terms, see e.g.\nPhillips and Hansen (1990) . The presence\nof these bias terms renders inference difficult. Consequently,\nseveral modifications to OLS that lead to zero mean Gaussian\nmixture limiting distributions have been proposed, which in\nturn make standard asymptotic inference feasible. These methods\ninclude the fully modified OLS (FM-OLS) approach of Phillips\nand Hansen (1990) , the dynamic OLS\n(D-OLS) approach of Phillips and Loretan (1991)\n, Saikkonen (1991)\n and Stock and Watson (1993)\n and the new estimation approach called\nintegrated modified OLS (IM-OLS) of Vogelsang and Wagner (2014)\n. The latter is based on an\naugmented partial sum (integration) transformation of the\nregression model. IM-OLS is similar in spirit to the FM- and\nD-OLS approaches, with the key difference that it does not\nrequire estimation of long run variance matrices and avoids the\nneed to choose tuning parameters (kernels, bandwidths, lags).\nHowever, inference does require that a long run variance be\nscaled out. This package provides functions for the parameter\nestimation and inference with all three modified OLS\napproaches. That includes the automatic bandwidth selection\napproaches of Andrews (1991) and of Newey\nand West (1994) as well as the\ncalculation of the long run variance.", "URL": "https://github.com/aschersleben/cointReg", "BugReports": "https://github.com/aschersleben/cointReg/issues", "License": "GPL-3", "RoxygenNote": "5.0.1", "VignetteBuilder": "knitr", "Repository": "https://aschersleben.r-universe.dev", "Date/Publication": "2017-01-03 14:50:21 UTC", "RemoteUrl": "https://github.com/aschersleben/cointreg", "RemoteRef": "HEAD", "RemoteSha": "38d35fcb1a8bdf3993d3fcf820290ee3eea671e4", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 08:44:28 UTC", "User": "root" }, "Author": "Philipp Aschersleben [aut, cre],\nMartin Wagner [aut] (Author of underlying MATLAB code.)", "Maintainer": "Philipp Aschersleben ", "MD5sum": "3e1b92114d2cc0bbaaa2ee96135d1f8c", "_user": "aschersleben", "_type": "src", "_file": "cointReg_0.2.0.9000.tar.gz", "_fileid": 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"page": "cointRegFM", "title": "Fully Modified OLS", "topics": [ "cointRegFM" ] }, { "page": "cointRegIM", "title": "Integrated Modified OLS", "topics": [ "cointRegIM" ] }, { "page": "getBandwidth", "title": "Automatic Bandwidth Selection", "topics": [ "getBandwidth", "getBandwidthAnd", "getBandwidthNW" ] }, { "page": "getLeadLag", "title": "Leads and Lags", "topics": [ "getLeadLag" ] }, { "page": "getLongRunVar", "title": "Long-Run Variance", "topics": [ "getLongRunVar" ] }, { "page": "getLongRunWeights", "title": "Weights for Long-Run Variance", "topics": [ "getLongRunWeights" ] }, { "page": "getModD", "title": "Get D OLS model.", "topics": [ "getModD" ] }, { "page": "makeLeadLagMatrix", "title": "Leads-and-Lags Matrix", "topics": [ "makeLeadLagMatrix" ] }, { "page": "plot.cointReg", "title": "Plot Method for Cointegration Models (Modified OLS).", "topics": [ "plot.cointReg" ] }, { "page": "print.cointReg", "title": "Print Method for Cointegration Models (Modified OLS).", "topics": [ 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