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Fully Modified Least Squares Estimation and Inference for Systems of Cointegrating Polynomial Regressions

Martin Wagner

No 44, IHS Working Paper Series from Institute for Advanced Studies

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 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.

Keywords: Fully Modified Estimation; Cointegrating Polynomial Regression; Generalized; Least Squares; Hypothesis Testing (search for similar items in EconPapers)
JEL-codes: C12 C13 Q20 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2023-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://irihs.ihs.ac.at/id/eprint/6431 First version, 2023 (application/pdf)

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Journal Article: Fully modified least squares estimation and inference for systems of cointegrating polynomial regressions (2023) Downloads
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