| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27991 |
| 来源ID | Working Paper 27991 |
| Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints | |
| S. Borağan Aruoba; Pablo Cuba-Borda; Kenji Higa-Flores; Frank Schorfheide; Sergio Villalvazo | |
| 发表日期 | 2020-10-26 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces the persistence of the Markov chain, improves the accuracy of Monte Carlo approximations of posterior moments, and drastically speeds up computations. We use the techniques to estimate a small-scale DSGE model to assess the effects of the government spending portion of the American Recovery and Reinvestment Act in 2009 when interest rates reached the zero lower bound. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Money and Interest Rates ; Monetary Policy |
| URL | https://www.nber.org/papers/w27991 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/585664 |
| 推荐引用方式 GB/T 7714 | S. Borağan Aruoba,Pablo Cuba-Borda,Kenji Higa-Flores,et al. Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints. 2020. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w27991.pdf（822KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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