Job Market Paper
Machine Learning using Nonstationary Data.
Abstract: Machine learning offers a promising set of tools for forecasting. However, some of the well-known properties do not apply to nonstationary data. This paper uses a simple procedure to extend machine learning methods to nonstationary data that does not require the researcher to have prior knowledge of which variables are nonstationary or the nature of the nonstationarity. I illustrate theoretically that using this procedure with LASSO or adaptive LASSO generates consistent variable selection on a mix of stationary and nonstationary explanatory variables. In an empirical exercise, I examine the success of this approach at forecasting U.S. inflation rates and the industrial production index using a number of different machine learning methods. I find that the proposed method either significantly improves prediction accuracy over traditional practices or delivers comparable performance, making it a reliable choice for obtaining stationary components of high-dimensional data.
Working Paper
Principal Component Analysis for Nonstationary Series, coauthored with James Hamilton.
This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. Applications to the term structure of interest rates and to the FRED-MD macroeconomic dataset demonstrate that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.
Publication
Strength in Numbers: Robust Mechanisms for Public Goods with Many Agents, coauthored with Haitian Xie.
Published, Social Choice and Welfare
This study examines the mechanism design problem for public goods provision in a large economy with n independent agents. We propose a class of dominant-strategy incentive compatible and ex-post individually rational mechanisms, which we call the adjusted mean-thresholding (AMT) mechanisms. We show that when the cost of provision grows slower than the square-root n rate, the AMT mechanisms are both eventually ex-ante budget balanced and asymptotically efficient. When the cost grows faster than the square-root n rate, in contrast, we show that any incentive compatible, individually rational, and eventually ex-ante budget balanced mechanism must have provision probability converging to zero and hence cannot be asymptotically efficient. The AMT mechanisms have a simple form and are more informationally robust when compared to, for example, the second-best mechanism. This is because the construction of an AMT mechanism depends only on the first moment of the valuation distribution.
Jin Xi 