Research

Publications

“Estimation of a Structural Break Point in Linear Regression Models”  Journal of Business & Economic Statistics, 42(1), 95-108, (2024)

This study proposes a point estimator of the break location for a one-time structural break in linear regression models. If the break magnitude is small, the least-squares estimator of the break date has two modes at the ends of the finite sample period, regardless of the true break location. To solve this problem, I suggest an alternative estimator based on a modification of the least-squares objective function. The modified objective function incorporates estimation uncertainty that varies across potential break dates. The new break point estimator is consistent and has a unimodal finite sample distribution under small break magnitudes. A limit distribution is provided under an in-fill asymptotic framework. Monte Carlo simulation results suggest that the new estimator outperforms the least-squares estimator. I apply the method to estimate the break date in U.S. and UK stock return prediction models. 

“Forecasting in Long Horizons Using Smoothed Direct Forecast” Journal of Forecasting, 38(4), 277-292, (2019) 

This paper constructs a forecast method that obtains long horizon forecasts with improved performance through modification of the direct forecast approach. Direct forecasts are more robust to model misspecification compared to iterated forecasts, which makes them preferable in long horizons. However, direct forecast estimates tend to have jagged shapes across horizons. Our forecast method aims to "smooth out" erratic estimates across horizons while maintaining the robust aspect of direct forecasts through ridge regression, which is a restricted regression on the first differences of regression coefficients. The forecasts are compared to the conventional iterated and direct forecasts in two empirical applications: real oil prices and U.S. macroeconomic series. In both applications, our method shows improvement over direct forecasts. 

“Testing Linearity Using Power Transforms of Regressors” with Jin Seo Cho and Peter C.B. Phillips, Journal of Econometrics, 187(1), 376-384, (2015) 

We develop a method of testing linearity using power transforms of regressors, allowing for stationary processes and time trends. The linear model is a simplifying hypothesis that derives from the power transform model in three different ways, each producing its own identification problem. We call this modeling difficulty the trifold identification problem and show that it may be overcome using a test based on the quasi-likelihood ratio (QLR) statistic. More specifically, the QLR statistic may be approximated under each identification problem and the separate null approximations may be combined to produce a composite approximation that embodies the linear model hypothesis. The limit theory for the QLR test statistic depends on a Gaussian stochastic process. In the important special case of a linear time trend regressor and martingale difference errors asymptotic critical values of the test are provided. Test power is analyzed and an empirical application to crop-yield distributions is provided. The paper also considers generalizations of the Box–Cox transformation, which are associated with the QLR test statistic. 

Working Papers

“Tests for Break in Coefficients in Linear Regression when the Direction of the Break is known” with Graham Elliott

“An Analysis on the Predictors of Financial Crises Using Machine Learning”