Ramis Khabibullin, Sergei Seleznev
arXiv 13 Oct 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2210.07154 · PDF · DOI · OpenAlex · Extracted main text
This paper presents a fast algorithm for estimating hidden states of Bayesian state space models. The algorithm is a variation of amortized simulation-based inference algorithms, where a large number of artificial datasets are generated at the first stage, and then a flexible model is trained to predict the variables of interest. In contrast to those proposed earlier, the procedure described in this paper makes it possible to train estimators for hidden states by concentrating only on certain characteristics of the marginal posterior distributions and introducing inductive bias. Illustrations using the examples of the stochastic volatility model, nonlinear dynamic stochastic general equilibrium model, and seasonal adjustment procedure with breaks in seasonality show that the algorithm has sufficient accuracy for practical use. Moreover, after pretraining, which takes several hours, finding the posterior distribution for any dataset takes from hundredths to tenths of a second.
appendix boundary found by appendix_command · 71% of the source is main text. Read the extracted text to check this.
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 | Lueckmann, J.-M., Boelts, J., Greenberg, D., Goncalves, P., and Mack… (2021) Benchmarking Simulation-Based Inference | 1.000 | 5 | 4 | 100% |
| 2 | Lueckmann, J.-M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenma… (2017) Flexible Statistical Inference for Mechanistic Models of Neural Dynamics | 0.843 | 4 | 4 | 75% |
| 3 | Kim, S., Shephard, N., and Chib, S (1998) Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models | 0.843 | 5 | 3 | 60% |
| 4 | Papamakarios, G. and Murray, I (2016) Fast $$-free Inference of Simulation Models with Bayesian Conditional Density Estimation | 0.737 | 3 | 2 | 100% |
| 5 | Diebold, F. X., Schorfheide, F., and Shin, M (2017) Real-time Forecast Evaluation of DSGE Models with Stochastic Volatility | 0.644 | 4 | 2 | 50% |
| 6 | Cranmer, K., Brehmer, J., and Louppe, G (2020) The Frontier of Simulation-Based Inference | 0.644 | 2 | 2 | 100% |
| 7 | Deli Gatti, D. and Grazzini, J (2020) Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent Based Models | 0.644 | 2 | 2 | 100% |
| 8 | Tan, L. S., Bhaskaran, A., and Nott, D. J (2020) Conditionally Structured Variational Gaussian Approximation with Importance Weights | 0.585 | 3 | 1 | 100% |
| 9 | Anderson, G. and Moore, G (1985) A Linear Algebraic Procedure for Solving Linear Perfect Foresight Models | 0.511 | 2 | 2 | 50% |
| 10 | Justiniano, A. and Primiceri, G. E (2008) The Time-Varying Volatility of Macroeconomic Fluctuations | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 85 scored citations.