Hypothesis testing with error correction models
| Autor: | Ellen M. Key, Matthew J. Lebo, Patrick W. Kraft |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
021110 strategic
defence & security studies Sociology and Political Science 05 social sciences Political Science and International Relations Statistics ComputingMethodologies_DOCUMENTANDTEXTPROCESSING 050602 political science & public administration 0211 other engineering and technologies Economics 02 engineering and technology Error detection and correction 0506 political science Statistical hypothesis testing |
| Zdroj: | Political Science Research and Methods. 10:870-878 |
| ISSN: | 2049-8489 2049-8470 |
| DOI: | 10.1017/psrm.2021.41 |
| Popis: | Grant and Lebo (2016) and Keele et al. (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coefficient, $\alpha _1^\ast$, to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on $\alpha _1^\ast$ is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x's. A significant $\alpha _1^\ast$ can occur when some I(1) regressors are not cointegrated and the equation is not balanced. Thus, the correct limiting distributions of the right-hand-side long-run coefficients may be unknown. We use simulations to demonstrate the problem and then discuss implications for applied examples. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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