Ze-Yu

Ze-Yu

Econometrics PhD Candidate

Working Papers and Publications

Identification and Estimation of Structural Factor Models with External Instruments

Working Paper, 2024

We develop a new estimator for the impulse response functions structural factor models with the use of external instruments. The proposed estimator is able to allow for the number of primitive shocks to be less than the number of static factors, and via the use of a minimum distance framework, jointly utilize multiple instruments. The minimum distance framework naturally leads to an overidentification test for the joint validity of instruments, and an auto- matic moment selection procedure to select the correct instruments. Simulation results show the improvement in the estimation accuracy of impulse response functions when more than one valid instrument is used, as well as the size and consistency of the overidentification test and automatic moment selection procedures. We apply the proposed methodology to estimate the effects of a monetary policy shock using a U.S. macroeconomic dataset with the use of popular monetary policy instruments. The results show these monetary policy instruments are all jointly valid, and that their joint use can result in more accurate and reasonable estimates of the impulse response functions.


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Factor-Augmented Forecasting Subject to Structural Breaks in the Factor Structure

Working Paper, 2024

This paper investigates the impact of structural breaks in the factor structure on factor-augmented forecasting. We decompose the break in the factor loading matrix into rotational and shift components. To effectively utilize the pre-break data and maintain robustness against shift breaks, we propose a novel factor estimator that minimizes the L2 distance between pre- and post-break loading matrices through the rotation of factor estimates. We call this estimator the “rotated factors” and analyze its the asymptotic properties, along with two competing factor estimators, in the presence of different types of breaks. To leverage the respective advantages of each factor estimator in an automatic data driven way, we introduce a method that averages over sets of factor estimates using a leave-h-out cross- validation criterion. Simulations demonstrate that combining different factor estimates through the proposed cross-validation averaging approach leads to improved forecasting performance compared to existing methods. Furthermore, we evaluate the effectiveness of our methods in an empirical application with US macroeconomic data and emphasize the importance of incorporating structural breaks into factor-augmented forecasting models.


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Disentangling Structural Breaks in Factor Models for Macroeconomic Data

Working Paper, 2022

Through a routine normalization of the factor variance, standard methods for estimating factor models in macroeconomics do not distinguish between breaks of the factor variance and factor loadings. We argue that it is important to distinguish between structural breaks in the factor variance and loadings within factor models commonly employed in macroeconomics as both can lead to markedly different in- terpretations when viewed via the lens of the underlying dynamic factor model. We then develop a projection-based decomposition that leads to two standard and easy- to-implement Wald tests to disentangle structural breaks in the factor variance and factor loadings. Applying our procedure to U.S. macroeconomic data, we find evi- dence of both types of breaks associated with the Great Moderation and the Great Recession. Through our projection-based decomposition, we estimate that the Great Moderation is associated with an over 60% reduction in the total factor variance, highlighting the relevance of disentangling breaks in the factor structure.


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