cointReg

Cointegration methods are widely used in empirical macroeconomics and empirical finance. It is well known that in a cointegrating regression the ordinary least squares (OLS) estimator of the parameters is super-consistent, i.e. converges at rate equal to the sample size T. When the regressors are endogenous, the limiting distribution of the OLS estimator is contaminated by so-called second order bias terms, see e.g. Phillips and Hansen (1990) doi:10.2307/2297545. The presence of these bias terms renders inference difficult. Consequently, several modifications to OLS that lead to zero mean Gaussian mixture limiting distributions have been proposed, which in turn make standard asymptotic inference feasible. These methods include the fully modified OLS (FM-OLS) approach of Phillips and Hansen (1990) doi:10.2307/2297545, the dynamic OLS (D-OLS) approach of Phillips and Loretan (1991) doi:10.2307/2298004, Saikkonen (1991) doi:10.1017/S0266466600004217 and Stock and Watson (1993) doi:10.2307/2951763 and the new estimation approach called integrated modified OLS (IM-OLS) of Vogelsang and Wagner (2014) doi:10.1016/j.jeconom.2013.10.015. The latter is based on an augmented partial sum (integration) transformation of the regression model. IM-OLS is similar in spirit to the FM- and D-OLS approaches, with the key difference that it does not require estimation of long run variance matrices and avoids the need to choose tuning parameters (kernels, bandwidths, lags). However, inference does require that a long run variance be scaled out. This package provides functions for the parameter estimation and inference with all three modified OLS approaches. That includes the automatic bandwidth selection approaches of Andrews (1991) doi:10.2307/2938229 and of Newey and West (1994) doi:10.2307/2297912 as well as the calculation of the long run variance.

Last 30 days

This package has been downloaded 386 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 19 times.

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Last 365 days

This package has been downloaded 5,091 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Oct 31, 2024 with 84 downloads.

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