arXiv 14 Mar 2025 · Econometrics
arXiv:2503.11416 · PDF · DOI · OpenAlex · Extracted main text
A novel approach to Forecast Error Variance Decompositions (FEVD) in nonlinear Structural Vector Autoregressive models with Gaussian innovations is proposed, called the Hermite FEVD (HFEVD). This method employs a Hermite polynomial expansion to approximate the future trajectory of a nonlinear process. The orthogonality of Hermite polynomials under the Gaussian density facilitates the construction of the decomposition, providing a separation of shock effects by time horizon, by components of the structural innovation and by degree of nonlinearity. A link between the HFEVD and nonlinear Impulse Response Functions is established and distinguishes between marginal and interaction contributions of shocks. Simulation results from standard nonlinear models are provided as illustrations and an application to fiscal policy shocks is examined.
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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 | Ferraresi, T., Roventini, A., and Fagiolo, G (2015) Fiscal policies and credit regimes: A tvar approach | 0.928 | 4 | 3 | 100% |
| 2 | Kilian, L. and Lütkepohl, H (2017) Structural vector autoregressive analysis | 0.843 | 4 | 4 | 75% |
| 3 | Isakin, M. and Ngo, P. V (2020) Variance decomposition analysis for nonlinear economic models 1 | 0.811 | 4 | 2 | 100% |
| 4 | Lanne, M. and Nyberg, H (2016) Generalized forecast error variance decomposition for linear and nonlinear multivariate models | 0.737 | 4 | 4 | 50% |
| 5 | Gouriéroux, C., Monfort, A., and Renne, J.-P (2017) Statistical inference for independent component analysis: Application to structural var models | 0.737 | 3 | 3 | 67% |
| 6 | Koop, G., Pesaran, M. H., and Potter, S. M (1996) Impulse response analysis in nonlinear multivariate models | 0.737 | 3 | 3 | 67% |
| 7 | Gourieroux, C. and Jasiak, J (2005) Nonlinear innovations and impulse responses with application to var sensitivity | 0.511 | 2 | 2 | 50% |
| 8 | Gourieroux, C. and Jasiak, J (2023) Generalized covariance estimator | 0.511 | 2 | 2 | 50% |
| 9 | Gouriéroux, C. and Lee, Q (2024) Forecast relative error decomposition self | 0.511 | 2 | 2 | 50% |
| 10 | Rahman, S (2017) Wiener–hermite polynomial expansion for multivariate gaussian probability measures | 0.511 | 2 | 1 | 100% |
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