cointReg - Parameter Estimation and Inference in a Cointegrating Regression
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.
