Francesco Giancaterini, Alain Hecq
arXiv 3 Dec 2020 · Econometrics · publishedEconometrics and Statistics (2022)
arXiv:2012.01888 · PDF · DOI · OpenAlex · Extracted main text
The properties of Maximum Likelihood estimator in mixed causal and noncausal models with a generalized Student's t error process are reviewed. Several known existing methods are typically not applicable in the heavy-tailed framework. To this end, a new approach to make inference on causal and noncausal parameters in finite sample sizes is proposed. It exploits the empirical variance of the generalized Student's-t, without the existence of population variance. Monte Carlo simulations show a good performance of the new variance construction for fat tail series. Finally, different existing approaches are compared using three empirical applications: the variation of daily COVID-19 deaths in Belgium, the monthly wheat prices, and the monthly inflation rate in Brazil.
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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 | Lanne, M., Saikkonen, P (2011) Noncausal autoregressions for economic time series | 1.000 | 7 | 3 | 100% |
| 2 | Hecq, A., Lieb, L., Telg, S (2016) Identification of mixed causal-noncausal models in finite samples self | 1.000 | 5 | 4 | 100% |
| 3 | Andrews, B., Davis, R.A., Breidt, F.J (2006) Maximum likelihood estimation for all-pass time series models | 0.585 | 3 | 1 | 100% |
| 4 | Rousseeuw, P.J., Croux, C (1993) Alternatives to the median absolute deviation | 0.585 | 3 | 1 | 100% |
| 5 | Fries, S., Zakoian, J.M (2019) Mixed causal-noncausal ar processes and the modelling of explosive bubbles | 0.511 | 2 | 1 | 100% |
| 6 | Alessi, L., Barigozzi, M., Capasso, M (2011) Non-fundamentalness in structural econometric models: A review | 0.405 | 1 | 1 | 100% |
| 7 | Andrews, B., Davis, R.A (2013) Model identification for infinite variance autoregressive processes | 0.405 | 1 | 1 | 100% |
| 8 | Bec, F., Nielsen, H.B., Sadi, S (2020) Mixed causal–noncausal autoregressions: Bimodality issues in estimation and unit root testing 1 | 0.405 | 1 | 1 | 100% |
| 9 | Breidt, F.J., Davis, R.A (1992) Time-reversibility, identifiability and independence of innovations for stationary time series | 0.405 | 1 | 1 | 100% |
| 10 | Cavaliere, G., Nielsen, H.B., Rahbek, A (2020) Bootstrapping noncausal autoregressions: with applications to explosive bubble modeling | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Is climate change time-reversible? | 0.511 | 2 | 1 |
| 2 | 2509.13492 | 0.405 | 1 | 1 |
| 3 | Seasonality in Mixed Causal-Noncausal Processes | 0.405 | 1 | 1 |