Faculty & Research

Drew D. Creal

Associate Professor of Econometrics and Statistics

Phone :
773 834-5249
Address :
5807 South Woodlawn Avenue
Chicago, IL 60637

Drew Creal studies time series econometrics and statistics with particular emphasis on state space and time-varying parameter models. His research interests include applications in macroeconomics and finance.

Creal earned a bachelor’s degree from Cornell University in 1999 and graduated with his PhD in economics from the University of Washington in 2007. He joined Chicago Booth in 2009. With experience teaching at both the Vrije Universiteit in Amsterdam and the University of Washington, he was awarded the Langton Award for Outstanding Undergraduate Teaching and the Graduate Student Teaching Award both in the Department of Economics at the University of Washington.

Professionally, Creal is a member of the Econometric Society and is a referee for Annals of Statistics, Annals of Applied Statistics, Computational Statistics and Data Analysis, Econometric Reviews, Econometric Theory, Journal of the American Statistical Association, Journal of Applied Econometrics, Journal of Business and Economic Statistics, Journal of Econometrics, , Journal of Money, Credit and Banking, and Macroeconomic Dynamics.

He enjoys golf, running, tennis, and platform tennis in his spare time.

 

2013 - 2014 Course Schedule

Number Name Quarter
41000 Business Statistics 2014 (Winter)
41600 Econometrics and Statistics Colloquium 2013 (Fall)

2014 - 2015 Course Schedule

Number Name Quarter
41000 Business Statistics 2014 (Fall)

Other Interests

Golf, running, tennis and platform tennis.

 

Research Activities

State space and time-varying parameter models.

With Kum Hwa Oh and Eric Zivot, "The Relationship Between the Beveridge-Nelson Decomposition and Other Popular Permanent-Transitory Decompositions in Economics," Journal of Econometrics (2008).

With Siem Jan Koopman and Neil Shephard, "Testing the Assumptions Behind Importance Sampling," Journal of Econometrics (2009).

"A survey of sequential Monte Carlo methods for economics and finance," Econometric Reviews (forthcoming).

With Siem Jan Koopman and Andre Lucas "A dynamic multivariate heavy tailed model for time-varying volatilities and correlations," Journal of Business and Economic Statistics (2011).

With Siem Jan Koopman and Andre Lucas "Generalized autoregressive score models with applications," Journal of Applied Econometrics (forthcoming).

REVISION: The Multinational Advantage
Date Posted: Sep  29, 2012
We investigate whether foreign operations provide U.S. multinational corporations (MNCs) with a competitive advantage by comparing actual firm value to an imputed value. An innovation of our study is the use of foreign firms as benchmarks to estimate the imputed value of foreign MNC operations, which controls for differences in discount rates and expected growth rates across countries. We find robust evidence that multinational networks trade at a premium relative to local firms. This result rec

New: Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
Date Posted: Feb  22, 2011
We propose a dynamic factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be observed at different time frequencies, may have missing observations, and may exhibit common dynamics and cross-sectional dependence due to shared exposure to dynamic latent factors. The distinguishing feature of our model is that the likelihood function is known in closed form and need not be obt

REVISION: A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
Date Posted: Oct  14, 2010
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student's t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations and volatilities. When we account for he

New: A General Framework for Observation Driven Time-Varying Parameter Models
Date Posted: Nov  11, 2008
We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the likelihood function. This approach provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, the autoregressive conditional

New: The Effect of the Great Moderation on the U.S. Business Cycle in a Time-Varying Multivariate Trend-C
Date Posted: Jul  31, 2008
In this paper we investigate whether the dynamic properties of the U.S. business cycle have changed in the last fifty years. For this purpose we develop a flexible business cycle indicator that is constructed from a moderate set of macroeconomic time series. The coincident economic indicator is based on a multivariate trend-cycle decomposition model that accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an unobserved components tim


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