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.
