 Semiparametrically optimal cointegration testBo Zhou Journal of Econometrics, 2024, vol. 242, issue 2 Abstract: This paper aims to address the issue of semiparametric efficiency for cointegration rank testing in finite-order vector autoregressive models, where the innovation distribution is considered an infinite-dimensional nuisance parameter. Our asymptotic analysis relies on Le Cam’s theory of limit experiment, which in this context is of the Locally Asymptotically Brownian Functional (LABF) type likelihood ratios. By exploiting the structural representation of LABF, an Ornstein–Uhlenbeck experiment, we develop the asymptotic power envelopes of asymptotically invariant tests for both cases with and without time trends. We propose feasible tests based on a nonparametrically estimated density and demonstrate that their power can achieve the semiparametric power envelopes, making them semiparametrically optimal. We validate the theoretical results through large-sample simulations and illustrate satisfactory size control and excellent power performance of our tests under small samples. In both cases with and without time trends, we show that a remarkable amount of additional power can be obtained from non-Gaussian distributions. Keywords: Cointegration; Semiparametric efficiency; Limit experiment; LABF (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:242:y:2024:i:2:s0304407624001611 DOI: 10.1016/j.jeconom.2024.105816 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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