Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/68500
Authors: 
Vogelsang, Timothy J.
Wagner, Martin
Year of Publication: 
2011
Series/Report no.: 
Reihe Ökonomie, Institut für Höhere Studien 263
Abstract: 
This paper is concerned with parameter estimation and inference in a cointegrating regression, where as usual endogenous regressors as well as serially correlated errors are considered. We propose a simple, new estimation method based on an augmented partial sum (integration) transformation of the regression model. The new estimator is labeled Integrated Modified Ordinary Least Squares (IM-OLS). IM-OLS is similar in spirit to the fully modified approach of Phillips and Hansen (1990) with the key difference that IM-OLS does not require estimation of long run variance matrices and avoids the need to choose tuning parameters (kernels, bandwidths, lags). Inference does require that a long run variance be scaled out, and we propose traditional and fixed-b methods for obtaining critical values for test statistics. The properties of IM-OLS are analyzed using asymptotic theory and finite sample simulations. IM-OLS performs well relative to other approaches in the literature.
Subjects: 
bandwidth
cointegration
fixed-b asymptotics
fully modified OLS
IM-OLS
kernel
JEL: 
C31
C32
Document Type: 
Working Paper

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