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Fully Modified Least Squares Estimation and Inference for Systems of Cointegrating Polynomial Regressions
[working paper]
Corporate Editor
Institut für Höhere Studien (IHS), Wien
Abstract
We consider fully modified least squares estimation for systems of cointegrating polynomial regressions, i. e., systems of regressions that include deterministic variables, integrated processes and their powers as regressors. The errors are allowed to be correlated across equations, over time and wi... view more
We consider fully modified least squares estimation for systems of cointegrating polynomial regressions, i. e., systems of regressions that include deterministic variables, integrated processes and their powers as regressors. The errors are allowed to be correlated across equations, over time and with the regressors. Whilst, of course, fully modified OLS and GLS estimation coincide - for any regular weighting matrix - without restrictions on the parameters and with the same regressors in all equations, this equivalence breaks down, in general, in case of parameter restrictions and/or different regressors across equations. Consequently, we discuss in detail restricted fully modified GLS estimators and inference based upon them.... view less
Keywords
estimation; regression; hypothesis testing
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
fully modified estimation; cointegrating polynomial regression; generalized least squares
Document language
English
Publication Year
2023
City
Wien
Page/Pages
13 p.
Series
IHS Working Paper, 44
Status
Published Version; reviewed