Jean-Jacques Forneron, Zhongjun Qu
arXiv 28 Dec 2024 · Econometrics
arXiv:2412.20204 · PDF · DOI · OpenAlex · Extracted main text
This paper considers filtering, parameter estimation, and testing for potentially dynamically misspecified state-space models. When dynamics are misspecified, filtered values of state variables often do not satisfy model restrictions, making them hard to interpret, and parameter estimates may fail to characterize the dynamics of filtered variables. To address this, a sequential optimal transportation approach is used to generate a model-consistent sample by mapping observations from a flexible reduced-form to the structural conditional distribution iteratively. Filtered series from the generated sample are model-consistent. Specializing to linear processes, a closed-form Optimal Transport Filtering algorithm is derived. Minimizing the discrepancy between generated and actual observations defines an Optimal Transport Estimator. Its large sample properties are derived. A specification test determines if the model can reproduce the sample path, or if the discrepancy is statistically significant. Empirical applications to trend-cycle decomposition, DSGE models, and affine term structure models illustrate the methodology and the results.
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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 | Smets, Frank and Wouters, Rafael (2007) Shocks and frictions in US business cycles: A Bayesian DSGE approach | 0.941 | 6 | 5 | 83% |
| 2 | Hamilton, James D and Wu, Jing Cynthia (2012) Identification and estimation of Gaussian affine term structure models | 0.874 | 6 | 2 | 100% |
| 3 | Lubik, Thomas A and Schorfheide, Frank (2004) Testing for indeterminacy: An application to US monetary policy | 0.843 | 4 | 4 | 75% |
| 4 | Hannan, Edward James and Deistler, Manfred (2012) The statistical theory of linear systems | 0.737 | 5 | 3 | 40% |
| 5 | Peyré, Gabriel and Cuturi, Marco (2019) Computational optimal transport: With applications to data science | 0.737 | 3 | 3 | 67% |
| 6 | Anderson, Brian and Moore, John B (1979) Optimal filtering | 0.737 | 3 | 2 | 100% |
| 7 | Qu, Zhongjun (2018) A composite likelihood framework for analyzing singular DSGE models self | 0.737 | 3 | 2 | 100% |
| 8 | Lewis, Richard and Reinsel, Gregory C (1985) Prediction of multivariate time series by autoregressive model fitting | 0.644 | 5 | 2 | 40% |
| 9 | Ang, Andrew and Piazzesi, Monika (2003) A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables | 0.644 | 3 | 2 | 67% |
| 10 | Kuersteiner, Guido M (2005) Automatic inference for infinite order vector autoregressions | 0.585 | 3 | 3 | 33% |
Showing the top 10 of 64 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | An econometrician's guide to optimal transport | 0.585 | 3 | 1 |