USC.edu
Professor M. Hashem Pesaran

Publications

Recent

Forthcoming Publications

  • With Andreas Pick and Allan Timmermann, “Forecasting with Panel Data: Estimation Uncertainty Versus Parameter Heterogeneity“, forthcoming in Quantitative Economics, 2026
    • Abstract: We provide a comprehensive examination of the predictive accuracy of panel forecasting methods based on individual, pooling, fixed effects, and empirical Bayes estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter heterogeneity, whether such heterogeneity is correlated with the regressors, the goodness of fit of the model, and the dimensions of the data. Monte Carlo simulations and empirical applications to house prices and CPI inflation show that empirical Bayes and forecast combination methods perform best overall and rarely produce the least accurate forecasts for individual series.
    • JEL Classifications: C33, C53
    • Key Words: Forecasting, Panel data, Heterogeneity, Pooled estimation; Forecast combination
    • Full Text
    • Codes & Data
    • CWPE Link
    • Arxiv Link
  • With Yimeng Xie, “How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Testy”, forthcoming in Econometric Theory, 2026.
    • Abstract: In a recent paper Juodis and Reese (2022) (JR) show that the application of the CD test proposed by Pesaran (2004) to residuals from panels with latent factors results in overrejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This paper considers the same problem but from a different perspective, and shows that the standard CD test remains valid if the latent factors are weak. In the case where latent factors are strong, we propose a bias-corrected version, CD*, which is shown to be asymptotically standard normal under the null of error cross-sectional independence and have power against network type alternatives. This result is shown to hold for pure latent factor models as well as for panel regression models with latent factors. The case where the errors are serially correlated is also considered. Small sample properties of the CD* test are investigated by Monte Carlo experiments and are shown to have the correct size for strong and weak factors as well as for Gaussian and non-Gaussian errors. In contrast, it is found that JR’s test tends to over-reject in the case of panels with non-Gaussian errors, and has low power against spatial and network alternatives. In an empirical application, using the CD* test, it is shown that there remains spatial error dependence in a panel data model for real house price changes across 377 Metropolitan Statistical Areas in the U.S., even after the effects of latent factors are filtered out.
    • JEL Classifications: C18, C23, C55
    • Key Words: Latent factor models, strong and weak factors, error cross-sectional independence, spatial and network alternatives, size and power
    • Full Text
    • CWPE Link
    • Arxiv Link
    • CESifo
    • Online Supplement
    • Data and Codes
  • With Liying Yang, “Heterogeneous Autoregressions in Short T Panel Data Models”,  Journal of Applied Econometrics, published online in August 2024

Working Papers

Contact

USC Faculty Profile

M. Hashem Paseran

Department of Economics
3620 S. Vermont Ave, KAP 324B
Los Angeles, CA 90089

 

Akiko Matsukiyo

Administrative Assistant