Denis Koshelev, Alexey Ponomarenko, Sergei Seleznev
arXiv 3 Aug 2023 · Econometrics
arXiv:2308.05753 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second step, a neural network is trained to predict the future values of the variables using the history of observations. The main advantage of the proposed algorithm is its speed. This is due to the fact that, after the training procedure, it can be used to yield predictions for almost any data without additional simulations or the re-estimation of the neural network
appendix boundary found by appendix_titled_section at “Appendix A. Figures and tables” · 65% 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 | D. Delli Gatti, S. Desiderio, E. Gaffeo, P. Cirillo, and M. Gallegati (2011) Macroeconomics from the Bottom-up | 0.737 | 3 | 3 | 67% |
| 2 | R.L. Axtell and J.D. Farmer (2022) Agent Based Modeling in Economics and Finance: Past, Present and Future | 0.644 | 4 | 1 | 100% |
| 3 | C. Hommes, M. He, S. Poledna, M. Siqueira, and Y.Zhang (2022) CANVAS: A Canadian Behavioral Agent-Based Model | 0.644 | 2 | 2 | 100% |
| 4 | R. Khabibullin and S. Seleznev (2022) Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference | 0.585 | 3 | 1 | 100% |
| 5 | N.J. Gordon, D.J. Salmond, and A.F.M. Smith (1993) Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation | 0.511 | 2 | 1 | 100% |
| 6 | D. Delli Gatti and J. Grazzini (2020) Rising to the challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent Based Models | 0.511 | 2 | 1 | 100% |
| * | unmatched citation key * | 0.405 | 1 | 1 | 100% |
| 8 | C. Andrieu, A. Doucet, and R. Holenstein (2010) Particle Markov Chain Monte Carlo Methods | 0.405 | 1 | 1 | 100% |
| 9 | J. Dyer, P. Cannon, J.D. Farmer, and S.M. Schmon (2022) Black-box Bayesian Inference for Economic Agent-Based Models | 0.405 | 1 | 1 | 100% |
| 10 | J. Dyer, P. Cannon, J.D. Farmer, and S.M. Schmon (2022) Calibrating Agent-based Models to Microdata with Graph Neural Networks | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.