About

Zhongjun Qu is a Professor and the Director of Graduate Studies in the Department of Economics at Boston University. His main research interests are in theoretical and applied econometrics, focusing on identification, estimation, inference, and model comparison for dynamic stochastic general equilibrium models. His work also addresses issues related to low frequency variation, including regime switching, structural change, long memory, and cointegration. Additionally, he proposes new methods for quantifying behavioral heterogeneity using the framework of quantile regression in a dynamic or semiparametric setting. His ongoing research projects include modeling regime switching in high-dimensional settings, estimating conditional quantile processes in partially linear models with applications such as the impact of unemployment benefits, and sieve estimation of option-implied state price density. Qu holds a PhD from Boston University and is actively involved in graduate education and research within the field of econometrics.

Research topics

• Artificial Intelligence
• Computer Science
• Geography
• Real-time computing
• Algorithm
• Distributed computing

Selected publications

• Estimating State Price Densities Implied by American Options

  Journal of Business and Economic Statistics · 2025-05-20

  article1st authorCorresponding

  We propose a new method to estimate state price densities implicit in American-style options. The method involves estimating the parameters of a Gauss-Hermite series expansion and solving a sequence of recursive equations for the early exercise premium. The resulting estimator can capture sudden shifts in density that may occur during financial crises or in response to significant policy events. It also provides an estimate of the early exercise premium that is of independent interest. We illustrate the proposed method using both calibrated simulations and empirical applications. Our findings indicate that the state price densities implied by S&P 500 ETF options can predict future returns up to a one-year horizon for the period 2009–2023. An application to individual stock options suggests that the state price densities have predictive power for future stock returns at both short (one month) and long (one year) horizons. The analysis also reveals a pattern of sign reversal when moving from short to longer horizon predictions.

  PublisherDOI

• QTE.RD: An R Package for Quantile Treatment Effects in Regression-Discontinuity Designs

  The R Journal · 2025-10-21

  articleOpen access1st authorCorresponding

  The QTE.RD package provides methods to test, estimate, and conduct uniform inference on quantile treatment effects in sharp regression discontinuity designs, allowing for covariates, and implementing robust bias correction. The package offers four main functions for estimating quantile treatment effects and uniform confidence bands, testing hypotheses related to treatment effects, selecting bandwidths using cross-validation or mean squared error criteria, and visualizing the estimated effects and confidence bands. This note includes an empirical illustration of the package's functionality using data on the impact of tracking on student achievement.

  PublisherOA PDFDOI

• Prediction Intervals for Model Averaging

  ArXiv.org · 2025-10-17

  preprintOpen access1st authorCorresponding

  A rich set of frequentist model averaging methods has been developed, but their applications have largely been limited to point prediction, as measuring prediction uncertainty in general settings remains an open problem. In this paper we propose prediction intervals for model averaging based on conformal inference. These intervals cover out-of-sample realizations of the outcome variable with a pre-specified probability, providing a way to assess predictive uncertainty beyond point prediction. The framework allows general model misspecification and applies to averaging across multiple models that can be nested, disjoint, overlapping, or any combination thereof, with weights that may depend on the estimation sample. We establish coverage guarantees under two sets of assumptions: exact finite-sample validity under exchangeability, relevant for cross-sectional data, and asymptotic validity under stationarity, relevant for time-series data. We first present a benchmark algorithm and then introduce a locally adaptive refinement and split-sample procedures that broaden applicability. The methods are illustrated with a cross-sectional application to real estate appraisal and a time-series application to equity premium forecasting.

  PublisherOA PDFDOI

• SpuriousMemory: Testing True Long Memory Against Spurious Long Memory

  2025-12-01

  datasetOpen access1st authorCorresponding

  Implements a test for distinguishing between true long memory and spurious long memory. Reference: Qu, Z. (2011). "A Test Against Spurious Long Memory." Journal of Business &amp; Economic Statistics, 29(3), 423–438. &lt;<a href="https://doi.org/10.1198%2Fjbes.2010.09153" target="_top">doi:10.1198/jbes.2010.09153</a>&gt;.

  PublisherOA PDFDOI

• QR.break: An R Package for Structural Breaks in Quantile Regression

  Journal of Econometric Methods · 2025-01-01

  article1st authorCorresponding

  Abstract The QR.break package provides methods for detecting, estimating, and conducting inference on multiple structural breaks in linear quantile regression models, based on one or multiple quantiles and applicable to both time series and repeated cross-sectional data. The main function, rq.break() , returns testing and estimation results based on user specifications of the quantiles of interest, the maximum number of breaks allowed, and the minimum length of a single regime. This note outlines the underlying methods and explains how to use the main function with two datasets: a time series dataset on U.S. real GDP growth rates and a repeated cross-sectional dataset on youth drinking and driving behavior. Both datasets are included in the package available on CRAN.

  PublisherDOI

• QR.break: Structural Breaks in Quantile Regression

  2025-04-07

  dataset1st authorCorresponding
  PublisherDOI

• Introduction to the Themed Issue: Macroeconometrics

  Journal of Econometrics · 2024-09-01

  article1st authorCorresponding
  PublisherDOI

• Fitting Dynamically Misspecified Models: An Optimal Transportation Approach

  ArXiv.org · 2024-12-28

  preprintOpen accessSenior author

  This paper considers filtering, parameter estimation, and testing for potentially dynamically misspecified state-space models. When dynamics are misspecified, filtered values of state variables often do not satisfy model restrictions, making them hard to interpret, and parameter estimates may fail to characterize the dynamics of filtered variables. To address this, a sequential optimal transportation approach is used to generate a model-consistent sample by mapping observations from a flexible reduced-form to the structural conditional distribution iteratively. Filtered series from the generated sample are model-consistent. Specializing to linear processes, a closed-form Optimal Transport Filtering algorithm is derived. Minimizing the discrepancy between generated and actual observations defines an Optimal Transport Estimator. Its large sample properties are derived. A specification test determines if the model can reproduce the sample path, or if the discrepancy is statistically significant. Empirical applications to DSGE models, affine term structure models, and trend-cycle decomposition illustrate the methodology and the results.

  PublisherOA PDFDOI

• QTE.RD: Quantile Treatment Effects under the Regression Discontinuity Design

  2024-03-18

  datasetOpen access1st authorCorresponding

  designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.

  PublisherOA PDFDOI

• Targeted Testing of Dynamic Stochastic General Equilibrium Models

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Pierre Perrón

  Boston University

  31 shared

• Denis Tkachenko

  9 shared

• Jungmo Yoon

  9 shared

• Fan Zhuo

  Hebrew University of Jerusalem

  9 shared

• Tatsushi Oka

  4 shared

• Junwen Lu

  1 shared

• Mingrui Han

  Xi'an Jiaotong University

  1 shared

• Timothy J. Vogelsang

  1 shared

Education

• Ph.D.

  Boston University