Inference on Counterfactual Distributions
Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist o...
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| Format: | Article |
| Language: | en_US |
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The Econometric Society
2015
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| Online Access: | http://hdl.handle.net/1721.1/95960 https://orcid.org/0000-0002-3250-6714 |
| _version_ | 1826215417554665472 |
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| author | Chernozhukov, Victor V. Melly, Blaise Fernandez-Val, Ivan |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Chernozhukov, Victor V. Melly, Blaise Fernandez-Val, Ivan |
| author_sort | Chernozhukov, Victor V. |
| collection | MIT |
| description | Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios, we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States.
As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals. |
| first_indexed | 2024-09-23T16:27:59Z |
| format | Article |
| id | mit-1721.1/95960 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:27:59Z |
| publishDate | 2015 |
| publisher | The Econometric Society |
| record_format | dspace |
| spelling | mit-1721.1/959602022-09-29T19:54:50Z Inference on Counterfactual Distributions Chernozhukov, Victor V. Melly, Blaise Fernandez-Val, Ivan Massachusetts Institute of Technology. Department of Economics Chernozhukov, Victor V. Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios, we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States. As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals. National Science Foundation (U.S.) 2015-03-11T19:23:44Z 2015-03-11T19:23:44Z 2013-11 2012-11 Article http://purl.org/eprint/type/JournalArticle 0012-9682 1468-0262 http://hdl.handle.net/1721.1/95960 Chernozhukov, Victor, Ivan Fernandez-Val and Blaise Melly. “Inference on Counterfactual Distributions.” Econometrica 81, no. 6 (2013): 2205–2268. https://orcid.org/0000-0002-3250-6714 en_US http://dx.doi.org/10.3982/ecta10582 Econometrica Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf The Econometric Society MIT web domain |
| spellingShingle | Chernozhukov, Victor V. Melly, Blaise Fernandez-Val, Ivan Inference on Counterfactual Distributions |
| title | Inference on Counterfactual Distributions |
| title_full | Inference on Counterfactual Distributions |
| title_fullStr | Inference on Counterfactual Distributions |
| title_full_unstemmed | Inference on Counterfactual Distributions |
| title_short | Inference on Counterfactual Distributions |
| title_sort | inference on counterfactual distributions |
| url | http://hdl.handle.net/1721.1/95960 https://orcid.org/0000-0002-3250-6714 |
| work_keys_str_mv | AT chernozhukovvictorv inferenceoncounterfactualdistributions AT mellyblaise inferenceoncounterfactualdistributions AT fernandezvalivan inferenceoncounterfactualdistributions |