About

Clifford M. Hurvich is a Professor of Statistics and Research Professor of Information, Operations and Management Sciences at the Stern School of Business, New York University. He earned a B.A. in Mathematics from Amherst College in 1980 and a Ph.D. in Statistics from Princeton University in 1985. Professor Hurvich is a Fellow of the American Statistical Association and serves as an Associate Editor of the Journal of Time Series Analysis. His work spans the areas of statistical modeling, time series econometrics, and forecasting, with notable contributions to the selection of statistical models, which resulted in a research grant from the National Science Foundation and implementation in widely available software packages. He is a co-author of a foundational paper on determining the strength of mean reversion of a time series, a methodology useful for assessing how quickly financial series revert to equilibrium. His recent research focuses on measuring the forecastability of stock returns and volatility. Professor Hurvich has published extensively in journals across Statistics, Econometrics, and Finance, including the Journal of Econometrics, Econometric Theory, and the Journal of Financial and Quantitative Analysis. He teaches Statistics across undergraduate, M.B.A., and Ph.D. programs at NYU Stern.

Research topics

• Computer Science
• Mathematics
• Mathematical optimization
• Econometrics
• Economics
• Statistics
• Artificial Intelligence
• Machine Learning
• Operations management
• Operations research

Selected publications

• Designing Information Delays in Supply Chains

  arXiv (Cornell University) · 2026-01-01

  preprintOpen access

  This paper studies how a downstream retailer in a decentralized two-tier supply chain can implicitly transmit demand information to an upstream supplier through the structure of its order stream in the absence of an explicit information-sharing mechanism. We distinguish our work from prior work by introducing the notion of information delay and by linking optimal implicit information sharing to the group delay of the retailer's ordering transfer function. We show that pure delay is strictly suboptimal, while fractional-delay mechanisms can reshape the order autocorrelation to improve supplier forecastability and reduce system-wide inventory costs. Using Hardy-space factorization, we develop a tractable family of invertible ARMA policies that approximates the theoretically optimal (but non-rational) limiting filter derived by Caldentey et al. (2025) and preserves its informational delay properties. This construction yields sharp guidance on how policy complexity, as measured by the degrees of the ARMA policies, impacts supply chain costs. We further extend the analysis to memory-constrained suppliers and characterize how the complexity of the retailer's policy should scale with the supplier's finite forecasting window, highlighting when, perhaps counterintuitively, increasing policy complexity can become counterproductive.

  PublisherDOI

• Seasonal demand forecasting and incentivizing information sharing

  Journal of the Operational Research Society · 2026-04-29

  articleSenior author
  PublisherDOI

• Designing Information Delays in Supply Chains

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Designing Information Delays in Supply Chains

  ArXiv.org · 2026-01-01

  articleOpen access

  This paper studies how a downstream retailer in a decentralized two-tier supply chain can implicitly transmit demand information to an upstream supplier through the structure of its order stream in the absence of an explicit information-sharing mechanism. We distinguish our work from prior work by introducing the notion of information delay and by linking optimal implicit information sharing to the group delay of the retailer's ordering transfer function. We show that pure delay is strictly suboptimal, while fractional-delay mechanisms can reshape the order autocorrelation to improve supplier forecastability and reduce system-wide inventory costs. Using Hardy-space factorization, we develop a tractable family of invertible ARMA policies that approximates the theoretically optimal (but non-rational) limiting filter derived by Caldentey et al. (2025) and preserves its informational delay properties. This construction yields sharp guidance on how policy complexity, as measured by the degrees of the ARMA policies, impacts supply chain costs. We further extend the analysis to memory-constrained suppliers and characterize how the complexity of the retailer's policy should scale with the supplier's finite forecasting window, highlighting when, perhaps counterintuitively, increasing policy complexity can become counterproductive.

  PublisherOA PDF

• A High-Low Ratio Test for Geometric Brownian Motion

  SSRN Electronic Journal · 2025-01-01

  articleOpen accessSenior author
  PublisherDOI

• Long-horizon return predictability from realized volatility in pure-jump point processes

  Econometrics and Statistics · 2025-11-01

  articleSenior author
  PublisherDOI

• Managing Inventory and Information in Supply Chains

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• A High-Low Ratio Test for Geometric Brownian Motion

  SSRN Electronic Journal · 2025-01-01

  articleOpen accessSenior author
  PublisherDOI

• Automatic Order, Bandwidth Selection and Flaws of Eigen Adjustment in HAC Estimation

  ArXiv.org · 2025-09-27

  preprintOpen accessSenior author

  In this paper, we propose a new heteroskedasticity and autocorrelation consistent covariance matrix estimator based on the prewhitened kernel estimator and a localized leave-one-out frequency domain cross-validation (FDCV). We adapt the cross-validated log likelihood (CVLL) function to simultaneously select the order of the prewhitening vector autoregression (VAR) and the bandwidth. The prewhitening VAR is estimated by the Burg method without eigen adjustment as we find the eigen adjustment rule of Andrews and Monahan (1992) can be triggered unnecessarily and harmfully when regressors have nonzero mean. Through Monte Carlo simulations and three empirical examples, we illustrate the flaws of eigen adjustment and the reliability of our method.

  PublisherOA PDFDOI

• Partial Information Sharing in Supply Chains with ARMA Demand

  SSRN Electronic Journal · 2024-01-01 · 2 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Philippe Soulier

  36 shared

• Chih‐Ling Tsai

  25 shared

• Avi Giloni

  24 shared

• Rohit Deo

  New York University

  22 shared

• Willa W. Chen

  Texas A&M University

  16 shared

• Éric Moulines

  École Polytechnique

  14 shared

• Jeffrey S. Simonoff

  13 shared

• Vladimir Kovtun

  Yeshiva University

  13 shared

Awards & honors

• Fellow of the American Statistical Association