arXiv 16 Sep 2025 · Econometrics
arXiv:2509.13492 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the properties of the Generalized Covariance (GCov) estimator under misspecification and constraints with application to processes with local explosive patterns, such as causal-noncausal and double autoregressive (DAR) processes. We show that GCov is consistent and has an asymptotically Normal distribution under misspecification. Then, we construct GCov-based Wald-type and score-type tests to test one specification against the other, all of which follow a $\chi^2$ distribution. Furthermore, we propose the constrained GCov (CGCov) estimator, which extends the use of the GCov estimator to a broader range of models with constraints on their parameters. We investigate the asymptotic distribution of the CGCov estimator when the true parameters are far from the boundary and on the boundary of the parameter space. We validate the finite sample performance of the proposed estimators and tests in the context of causal-noncausal and DAR models. Finally, we provide two empirical applications by applying the noncausal model to the final energy demand commodity index and also the DAR model to the US 3-month treasury bill.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jasiak, J. and A. M. Neyazi (2023) Gcov-based portmanteau test | 1.000 | 7 | 5 | 100% |
| 2 | Gourieroux, C. and J. Jasiak (2018) Misspecification of noncausal order in autoregressive processes | 0.965 | 10 | 3 | 90% |
| 3 | Gourieroux, C., A. Monfort, and A. Trognon (1983) Testing nested or non-nested hypotheses | 0.941 | 6 | 3 | 83% |
| 4 | Gourieroux, C. and A. Monfort (1995) Testing, encompassing, and simulating dynamic econometric models | 0.941 | 6 | 3 | 83% |
| 5 | Gourieroux, C. and J. Jasiak (2023) Generalized covariance estimator | 0.855 | 16 | 5 | 62% |
| 6 | Jiang, F., D. Li, and K. Zhu (2020) Non-standard inference for augmented double autoregressive models with null volatility coefficients | 0.843 | 4 | 4 | 75% |
| 7 | Francq, C. and J.-M. Zakoian (2007) Quasi-maximum likelihood estimation in garch processes when some coefficients are equal to zero | 0.737 | 3 | 3 | 67% |
| 8 | Gourieroux, C., A. Holly, and A. Monfort (1982) Likelihood ratio test, wald test, and kuhn-tucker test in linear models with inequality constraints on the regression parameters | 0.737 | 3 | 2 | 100% |
| 9 | Gourieroux, C. and J. Jasiak (2017) Noncausal vector autoregressive process: Representation, identification and semi-parametric estimation | 0.737 | 3 | 2 | 100% |
| 10 | Lanne, M. and P. Saikkonen (2013) Noncausal vector autoregression | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 56 scored citations.