Giuseppe Buccheri, Fulvio Corsi, Emilija Dzuverovic
arXiv 2 Dec 2024 · Econometrics
arXiv:2412.01367 · PDF · DOI · OpenAlex · Extracted main text
We show that, for a certain class of scaling matrices including the commonly used inverse square-root of the conditional Fisher Information, score-driven factor models are identifiable up to a multiplicative scalar constant under very mild restrictions. This result has no analogue in parameter-driven models, as it exploits the different structure of the score-driven factor dynamics. Consequently, score-driven models offer a clear advantage in terms of economic interpretability compared to parameter-driven factor models, which are identifiable only up to orthogonal transformations. Our restrictions are order-invariant and can be generalized to scoredriven factor models with dynamic loadings and nonlinear factor models. We test extensively the identification strategy using simulated and real data. The empirical analysis on financial and macroeconomic data reveals a substantial increase of log-likelihood ratios and significantly improved out-of-sample forecast performance when switching from the classical restrictions adopted in the literature to our more flexible specifications.
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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 | Artemova, M (2023) An Order-Invariant Score-Driven Dynamic Factor Model | 1.000 | 6 | 3 | 100% |
| 2 | Creal, D., Schwaab, B., Koopman, S.J., Lucas, A (2014) Observation-driven mixed-measurement dynamic factor models with an application to credit risk | 0.941 | 6 | 4 | 83% |
| 3 | Bai, J., Li, K (2012) Statistical analysis of factor models of high dimension | 0.843 | 3 | 3 | 100% |
| 4 | Opschoor, A., Lucas, A., Barra, I., Van Dijk, D (2021) Closed-form multi-factor copula models with observation-driven dynamic factor loadings | 0.737 | 3 | 2 | 100% |
| 5 | Creal, D., Koopman, S.J., Lucas, A (2011) A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations | 0.644 | 2 | 2 | 100% |
| 6 | Cox, D (1981) Statistical analysis of time series: Some recent developments [with discussion and reply] | 0.405 | 1 | 1 | 100% |
| 7 | Stock, J.H., Watson, M.W (2011) 35 dynamic factor models | 0.405 | 1 | 1 | 100% |
| 8 | Adrian, T., Franzoni, F (2009) Learning about beta: Time-varying factor loadings, expected returns, and the conditional capm | 0.405 | 1 | 1 | 100% |
| 9 | Artemova, M., Blasques, F., van Brummelen, J., Koopman, S.J (2022) Score-driven models: Methods and applications | 0.405 | 1 | 1 | 100% |
| 10 | Blasques, F., van Brummelen, J., Gorgi, P., Koopman, S.J (2024) Maximum likelihood estimation for non-stationary location models with mixture of normal distributions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.