Estimating HANK with Micro Data
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910335282184192 |
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| author | Iao, Man Chon Selvakumar, Yatheesan J. |
| author_facet | Iao, Man Chon Selvakumar, Yatheesan J. |
| contents | We propose an indirect inference strategy for estimating heterogeneous-agent business cycle models with micro data. At its heart is a first-order vector autoregression that is grounded in linear filtering theory as the cross-section grows large. The result is a fast, simple and robust algorithm for computing an approximate likelihood that can be easily paired with standard classical or Bayesian methods. Importantly, our method is compatible with the popular sequence-space solution method, unlike existing state-of-the-art approaches. We test-drive our method by estimating a canonical HANK model with shocks in both the aggregate and cross-section. Not only do simulation results demonstrate the appeal of our method, they also emphasize the important information contained in the entire micro-level distribution over and above simple moments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11379 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating HANK with Micro Data Iao, Man Chon Selvakumar, Yatheesan J. General Economics Economics We propose an indirect inference strategy for estimating heterogeneous-agent business cycle models with micro data. At its heart is a first-order vector autoregression that is grounded in linear filtering theory as the cross-section grows large. The result is a fast, simple and robust algorithm for computing an approximate likelihood that can be easily paired with standard classical or Bayesian methods. Importantly, our method is compatible with the popular sequence-space solution method, unlike existing state-of-the-art approaches. We test-drive our method by estimating a canonical HANK model with shocks in both the aggregate and cross-section. Not only do simulation results demonstrate the appeal of our method, they also emphasize the important information contained in the entire micro-level distribution over and above simple moments. |
| title | Estimating HANK with Micro Data |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2402.11379 |