Gianluca Cubadda, Francesco Giancaterini, Stefano Grassi
arXiv 7 Jan 2025 · Econometrics
arXiv:2501.03945 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a Sequential Monte Carlo approach for the Bayesian estimation of mixed causal and noncausal models. Unlike previous Bayesian estimation methods developed for these models, Sequential Monte Carlo offers extensive parallelization opportunities, significantly reducing estimation time and mitigating the risk of becoming trapped in local minima, a common issue in noncausal processes. Simulation studies demonstrate the strong ability of the algorithm to produce accurate estimates and correctly identify the process. In particular, we propose a novel identification methodology that leverages the Marginal Data Density and the Bayesian Information Criterion. Unlike previous studies, this methodology determines not only the causal and noncausal polynomial orders but also the error term distribution that best fits the data. Finally, Sequential Monte Carlo is applied to a bivariate process containing S$&$P Europe 350 ESG Index and Brent crude oil prices.
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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. and J. Luoto (2016) Noncausal Bayesian Vector Autoregression | 1.000 | 12 | 4 | 100% |
| 2 | Lanne, M. and P. Saikkonen (2013) Noncausal Vector Autoregression | 1.000 | 8 | 3 | 100% |
| 3 | Lanne, M. and P. Saikkonen (2011) Noncausal Autoregressions for Economic Time Series | 0.928 | 5 | 4 | 80% |
| 4 | Bognanni, M. and E. Herbst (2018) A Sequential Monte Carlo Approach to Inference in Multiple-Equation Markov-Switching Models | 0.894 | 7 | 3 | 71% |
| 5 | Lanne, M., A. Luoma, and J. Luoto (2012) Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models | 0.874 | 5 | 2 | 100% |
| 6 | Breidt, F. J., R. A. Davis, K.-S. Lh, and M. Rosenblatt (1991) Maximum Likelihood Estimation for Noncausal Autoregressive Processes | 0.737 | 3 | 2 | 100% |
| 7 | Gourieroux, C. and J. Jasiak (2017) Noncausal Vector Autoregressive Process: Representation, Identification and Semi-Parametric Estimation | 0.737 | 3 | 2 | 100% |
| 8 | Herbst, E. and F. Schorfheide (2014) Sequential Monte Carlo Sampling for DSGE Models | 0.737 | 3 | 2 | 100% |
| 9 | Davis, R. A. and L. Song (2020) Noncausal Vector AR Processes with Application to Economic Time Series | 0.644 | 2 | 2 | 100% |
| 10 | Gouriéroux, C. and J.-M. Zakoïan (2017) Local Explosion Modelling by Non-causal Process | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2504.18678 | 0.511 | 2 | 1 |