About

The page provides information about the New York University Stern Center for Research Computing (SCRC), which is dedicated to providing world-class computational facilities and services to researchers at the Stern School of Business. The center offers a variety of services including a moderately sized Slurm HPC cluster, Cloud Computing (Virtual Machines), data acquisition and storage, research software, and access to WRDS (Wharton Research Data System). The research software suite is designed to facilitate advanced computational research and data analysis, while the datasets are sourced from diverse disciplines through collaborations with data repositories, platforms, and academic institutions. The compute services and storage systems support faculty and researchers' projects by providing high-speed, robust, and scalable solutions to meet diverse computational and storage needs.

Research topics

• Mathematics
• Statistics
• Econometrics
• Applied mathematics
• Computer science

Selected publications

• The Slow Convergence of Ordinary Least Squares Estimators of <i>α</i>, <i>β</i> and Portfolio Weights under Long‐Memory Stochastic Volatility

  Journal of Time Series Analysis · 2019-01-07 · 1 citations

  articleCorresponding

  We consider inference for the market model coefficients based on simple linear regression under a long memory stochastic volatility generating mechanism for the returns. We obtain limit theorems for the ordinary least squares (OLS) estimators of α and β in this framework. These theorems imply that the convergence rate of the OLS estimators is typically slower than if both the regressor and the predictor have long memory in volatility, where T is the sample size. The traditional standard errors of the OLS‐estimated intercept ( ) and slope ( ), which disregard long memory in volatility, are typically too optimistic, and therefore the traditional t ‐statistic for testing, say, α = 0 or β = 1, will diverge under the null hypothesis. We also obtain limit theorems (which imply slow convergence) for the estimated weights of the minimum variance portfolio and the optimal portfolio in the same framework. In addition, we propose and study the performance of a subsampling‐based approach to hypothesis testing for α and β . We conclude by noting that analogous results hold under more general conditions on long‐memory volatility models and state these general conditions which cover certain fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models.

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• Subsampling based inference for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml31" display="inline" overflow="scroll" altimg="si31.gif"><mml:mi>U</mml:mi></mml:math>statistics under thick tails using self-normalization

  Statistics & Probability Letters · 2018-03-08

  articleSenior author
  PublisherDOI

• On the Tracy–Widom approximation of studentized extreme eigenvalues of Wishart matrices

  Journal of Multivariate Analysis · 2016-02-14 · 9 citations

  article1st authorCorresponding
  PublisherDOI

• Uniform Inference in Predictive Regression Models

  Journal of Business and Economic Statistics · 2013-10-01

  article

  The restricted likelihood has been found to provide a well-behaved likelihood ratio test in the predictive regression model even when the regressor variable exhibits almost unit root behavior. Using the weighted least squares approximation to the restricted likelihood obtained in Chen and Deo, we provide a quasi restricted likelihood ratio test (QRLRT), obtain its asymptotic distribution as the nuisance persistence parameter varies, and show that this distribution varies very slightly. Consequently, the resulting sup bound QRLRT is shown to maintain size uniformly over the parameter space without sacrificing power. In simulations, the QRLRT is found to deliver uniformly higher power than competing procedures with power gains that are substantial.

  PublisherDOI

• Improved forecasting of autoregressive series by weighted least squares approximate REML estimation

  International Journal of Forecasting · 2011-04-15 · 3 citations

  article1st authorCorresponding
  PublisherDOI

• The restricted likelihood ratio test for autoregressive processes

  Journal of Time Series Analysis · 2011-11-29 · 9 citations

  articleSenior author

  The restricted likelihood is known to produce estimates with significantly less bias in AR( p ) models with intercept and/or trend. In AR(1) models, the corresponding restricted likelihood ratio test (RLRT), unlike the t ‐statistic or the usual LRT, has been recently shown to be well approximated by the chi‐square distribution even close to the unit root, thus yielding confidence intervals with good coverage properties. In this article, we extend this result to AR( p ) processes of arbitrary order p by obtaining the expansion of the RLRT distribution around that of the limiting chi‐squared and showing that the error is bounded even as the unit root is approached. Next, we investigate the correspondence between the AR coefficients and the partial autocorrelations, which is well known in the stationary region, and extend to the more general situation of potentially multiple unit roots. In the case of one positive unit root, which is of most practical interest, the resulting parameter space is shown to be the bounded p ‐dimensional hypercube (−1, 1] × (−1, 1) p −1 . This simple parameter space, in addition with a stable algorithm that we provide for computing the restricted likelihood, allows its easy computation and optimization as well as construction of confidence intervals for the sum of the AR coefficients. In simulations, the RLRT intervals are shown to have not only near exact coverage in keeping with our theoretical results, but also shorter lengths and significantly higher power against stationary alternatives than other competing interval procedures. An application to the well‐known Nelson–Plosser data yields RLRT based intervals that can be markedly different from those in the literature.

  PublisherDOI

• Weighted least squares approximate restricted likelihood estimation for vector autoregressive processes

  Biometrika · 2010-01-14 · 12 citations

  articleSenior author

  We derive a weighted least squares approximate restricted likelihood estimator for a k-dimensional pth-order autoregressive model with intercept. Exact likelihood optimization of this model is generally infeasible due to the parameter space, which is complicated and high-dimensional, involving pk2 parameters. The weighted least squares estimator has significantly reduced bias and mean squared error than the ordinary least squares estimator for both stationary and nonstationary processes. Furthermore, at the unit root, the limiting distribution of the weighted least squares approximate restricted likelihood estimator is shown to be the zero-intercept Dickey–Fuller distribution, unlike the ordinary least squares with intercept estimator that has a different distribution with significantly higher bias.

  PublisherDOI

• Long memory in intertrade durations, counts and realized volatility of NYSE stocks

  Journal of Statistical Planning and Inference · 2010-06-02 · 50 citations

  article1st authorCorresponding
  PublisherDOI

• BIAS REDUCTION AND LIKELIHOOD-BASED ALMOST EXACTLY SIZED HYPOTHESIS TESTING IN PREDICTIVE REGRESSIONS USING THE RESTRICTED LIKELIHOOD

  Econometric Theory · 2009-09-03 · 38 citations

  articleSenior authorCorresponding

  Difficulties with inference in predictive regressions are generally attributed to strong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter, and we propose basing inference on the restricted likelihood, which is free of such nuisance location parameters and also possesses small curvature, making it suitable for inference. The bias of the restricted maximum likelihood (REML) estimates is shown to be approximately 50% less than that of the ordinary least squares (OLS) estimates near the unit root, without loss of efficiency. The error in the chi-square approximation to the distribution of the REML-based likelihood ratio test (RLRT) for no predictability is shown to be $({\textstyle{3 \over 4}} - \rho ^2)n^{ - 1} (G_3 (\cdot) - G_1 (\cdot)) + O(n^{ - 2}),$ where | ρ | &lt; 1 is the correlation of the innovation series and G s (·) is the cumulative distribution function (c.d.f.) of a $\chi _s^2 $ random variable. This very small error, free of the autoregressive (AR) parameter, suggests that the RLRT for predictability has very good size properties even when the regressor has strong persistence. The Bartlett-corrected RLRT achieves an O ( n −2 ) error. Power under local alternatives is obtained, and extensions to more general univariate regressors and vector AR(1) regressors, where OLS may no longer be asymptotically efficient, are provided. In simulations the RLRT maintains size well, is robust to nonnormal errors, and has uniformly higher power than the Jansson and Moreira (2006, Econometrica 74, 681–714) test with gains that can be substantial. The Campbell and Yogo (2006, Journal of Financial Econometrics 81, 27–60) Bonferroni Q test is found to have size distortions and can be significantly oversized.

  PublisherDOI

• The Restricted Likelihood Ratio Test at the Boundary in Autoregressive Series

  The Faculty Digital Archive (New York University) · 2009-08-01

  articleOpen accessSenior author

  The restricted likelihood ratio test, RLRT, for the autoregressive coefficient in autoregressive models has recently been shown to be second order pivotal when the autoregressive coefficient is in the interior of the parameter space and so is very well approximated by the chi-square distribution. In this paper, the non-standard asymptotic distribution of the RLRT for the unit root boundary value is obtained and is found to be almost identical to that of the chi-square in the right tail. Together, the above two results imply that the chi-square distribution approximates the RLRT distribution very well even for near unit root series and transitions smoothly to the unit root distribution.

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Frequent coauthors

• Willa W. Chen

  Texas A&M University

  32 shared

• Clifford M. Hurvich

  22 shared

• Philippe Soulier

  8 shared

• Meng‐Chen Hsieh

  7 shared

• Willa Chen

  Carolinas Medical Center

  4 shared

• Matthew Richardson

  3 shared

• Yi Lu

  2 shared

• Yi Wang

  University of South Carolina

  2 shared

Awards & honors

• Multa Scripsit Award
• NYU Distinguished Teaching Award
• Great Professor Award, Executive MBA programme
• Professor of the Year Award in the full-time MBA programme