Clifford Hurvich

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

• The propagation and identification of ARMA demand under simple exponential smoothing: forecasting expertise and information sharing

  IMA Journal of Management Mathematics · 2020 · 20 citations

  Senior authorCorresponding

  Abstract It is common for firms to forecast stationary demand using simple exponential smoothing (SES) due to the ease of computation and understanding of the methodology. We consider a retailer who observes autoregressive moving average (ARMA) demand but for the sake of convenience, uses the widely available SES method to forecast its demand. This creates a potential disconnect between the true mechanism generating demand and the forecasting methodology. We show that the supplier, given a suffi…

  DOI

• Partial information sharing in supply chains with ARMA demand

  Naval Research Logistics (NRL) · 2024-09-09 · 4 citations

  articleOpen access

  Abstract In this paper we suggest a novel mechanism for information sharing that allows a retailer to control the amount of shared information, and thus to limit information leakage, while still assisting the supplier to make better‐informed decisions and improve the overall efficiency of the supply chain. The control of the amount of leaked information facilitates information sharing because, absent such control, a retailer may refrain from sharing information due to the concern of information…

  PublisherOA PDFDOI

• Performance bound for myopic order-up-to inventory policies under stationary demand processes

  Operations Research Letters · 2022 · 4 citations

  Senior authorCorresponding
  PublisherDOI

• Information Design and Sharing in Supply Chains

  Mathematics of Operations Research · 2024-08-09 · 2 citations

  article

  We study the interplay between inventory replenishment policies and information sharing in the context of a two-tier supply chain with a single supplier and a single retailer serving an independent and identically distributed Gaussian market demand. We investigate how the retailer’s inventory policy impacts the supply chain’s cumulative expected long-term average inventory costs [Formula: see text] in two extreme information-sharing cases: (a) full information sharing and (b) no information shar…

  PublisherDOI

• 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

Similar researchers at New York University

• Michael Feldmanh-73
• Anindya Ghoseh-60
• Alexander Tuzhilinh-49