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Research on Microeconometrics: Identification, Inference, and Applications

Periodic Reporting for period 3 - ROMIA (Research on Microeconometrics: Identification, Inference, and Applications)

Reporting period: 2019-01-01 to 2020-06-30

This research project is motivated from three observations regarding recent trends in empirical economics using micro-level data. First, researchers are increasingly aware of the trade-off between credibility and the strength of the assumptions maintained. This trend has led to recent intensive research in partial identification. Second, applied empirical research is increasingly based on data collected for study by individual researchers, quite often through laboratory or field experiments. Third, high-dimensional data are more readily available than ever before, and have received growing attention in economics.

Generally speaking, the purposes of econometrics are (i) to help empirical researchers understand under what conditions interesting features of an econometric model can be identified from the population; (ii) to develop corresponding suitable methods for estimation and inference, and (iii) to learn about parameters of interest, such as those governing mechanisms behind economic behaviours, impacts of social policy, and predicted outcomes under counterfactual exercises. Textbook econometrics implicitly assumes that (i) objects of interest are point identified, and (ii) datasets possess a small number of variables relative to sample size. In other words, textbook treatments of econometrics do not pay careful attention to identification problems, do not explicitly consider the research stage of data collection, and presume that the sample size is sufficiently large relative to the number of variables. Therefore, there is a call for research to improve standard econometric practice by facing identification problems upfront, by providing econometrically sound guidelines for data collection, and by making use of the increasing availability of high-dimensional data without sacrificing the credibility of econometric methods.

This research project aims to contribute to advances in microeconometrics by considering the issues of identification, data collection, and high-dimensional data carefully. The proposed research builds on semiparametric and nonparametric approaches to increase the credibility of proposed econometric methods. The key objectives are as follows.

(1) To develop identification results of practical value and to characterize optimal data collection for applied researchers.

(2) To make advances in estimation, inference, and testing in a variety of microeconometric models.

(3) To produce credible evidence in applied microeconometric research.

(4) To develop computer software that implements newly available microeconometric techniques.
"The PI and researchers have produced a large number of academic articles. During the reporting period, a total of 23 papers are written. Among these, 10 papers are either published or in press; 13 working papers are completed and under review for publication.

In addition, a conference entitled ""Econometrics for public policy, methods and applications"" ( took place in London, 14-16 April 2016 (jointly sponsored with Cemmap). About 50 academic participants attended this event. Another conference entitled ""Conference on optimisation and machine learning in economics"" ( took place in London, 8-9 March 2018 (jointly sponsored with Cemmap). More than 80 academic participants attended this event."
Overall, the project has been successful since it started in January 2016. Its progress has been steady and it is expected that the high productivity will continue during the second half of the project.