M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran
University of Southern California
H-index: 114
North America-United States
Description
M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran, With an exceptional h-index of 114 and a recent h-index of 71 (since 2020), a distinguished researcher at University of Southern California, specializes in the field of Econometrics, Macroeconomics, Applied Econometrics, Time Series Analysis, Panel Data Econometrics.
His recent articles reflect a diverse array of research interests and contributions to the field:
Testing for alpha in linear factor pricing models with a large number of securities
Forecasting with panel data: estimation uncertainty versus parameter heterogeneity
High-dimensional forecasting with known knowns and known unknowns
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Identification and estimation of categorical random coefficient models
Reflections on “Testing for unit roots in heterogeneous panels”
Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels
Climate change and economic activity: evidence from US states
Professor Information
University | University of Southern California |
---|---|
Position | John Elliot Distinguished Chair in Economics at Director Centre for Applied Financial |
Citations(all) | 166091 |
Citations(since 2020) | 80414 |
Cited By | 112498 |
hIndex(all) | 114 |
hIndex(since 2020) | 71 |
i10Index(all) | 292 |
i10Index(since 2020) | 188 |
University Profile Page | University of Southern California |
Research & Interests List
Econometrics
Macroeconomics
Applied Econometrics
Time Series Analysis
Panel Data Econometrics
Top articles of M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran
Testing for alpha in linear factor pricing models with a large number of securities
This article considers tests of alpha in linear factor pricing models when the number of securities, N, is much larger than the time dimension, T, of the individual return series. We focus on class of tests that are based on Student’s t-tests of individual securities which have a number of advantages over the existing standardized Wald type tests, and propose a test procedure that allows for non-Gaussianity and general forms of weakly cross-correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. We also show that the proposed test can account for some limited degree of pricing errors allowed under Ross’s arbitrage pricing theory condition. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5000. The test is applied to monthly returns on securities in the …
Authors
M Hashem Pesaran,Takashi Yamagata
Journal
Journal of Financial Econometrics
Published Date
2024/6/1
Forecasting with panel data: estimation uncertainty versus parameter heterogeneity
We provide a comprehensive examination of the predictive accuracy of panel forecasting methods based on individual, pooling, fixed effects, and Bayesian 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 cross-sectional () and time () dimensions. Monte Carlo simulations and empirical applications to house prices and CPI inflation show that forecast combination and Bayesian forecasting methods perform best overall and rarely produce the least accurate forecasts for individual series.
Authors
M Hashem Pesaran,Andreas Pick,Allan Timmermann
Journal
arXiv preprint arXiv:2404.11198
Published Date
2024/4/17
High-dimensional forecasting with known knowns and known unknowns
Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.
Authors
M Hashem Pesaran,Ron P Smith
Journal
arXiv preprint arXiv:2401.14582
Published Date
2024/1/26
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
This paper is concerned with the problem of variable selection in the presence of parameter instability when both the marginal effects of signals on the target variable and the correlations of the covariates in the active set could vary over time. We pose the issue of whether one should use weighted or unweighted observations at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large. We allow parameter instability to be continuous or discrete, subject to certain regularity conditions. We discuss the pros and cons of Lasso and the One Covariate at a time Multiple Testing (OCMT) method for variable selection and argue that OCMT has important advantages under parameter instability. We establish three main theorems on selection, estimation post selection, and in-sample fit. These theorems provide justification for using unweighted observations at the selection stage of OCMT and down-weighting of observations only at the forecasting stage. It is shown that OCMT delivers better forecasts, in mean squared error sense, as compared to Lasso, Adaptive Lasso and boosting both in Monte Carlo experiments as well as in 3 sets of empirical applications: forecasting monthly returns on 28 stocks from Dow Jones , forecasting quarterly output growths across 33 countries, and forecasting euro area output growth using surveys of professional forecasters.
Authors
Alexander Chudik,M Hashem Pesaran,Mahrad Sharifvaghefi
Journal
arXiv preprint arXiv:2312.15494
Published Date
2023/12/24
Identification and estimation of categorical random coefficient models
This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A generalized method of moments estimation procedure is proposed, also employed by Peter Schmidt and his coauthors to address heterogeneity in time effects in panel data models. Using Monte Carlo simulations, we find that moments of the random coefficients can be estimated reasonably accurately, but large samples are required for the estimation of the parameters of the underlying categorical distribution. The utility of the proposed estimator is illustrated by estimating the distribution of returns to education in the USA by gender and educational levels. We find that rising heterogeneity between educational groups is mainly due to the increasing …
Authors
Zhan Gao,M Hashem Pesaran
Journal
Empirical Economics
Published Date
2023/6
Reflections on “Testing for unit roots in heterogeneous panels”
This article is our personal perspective on the IPS test and the subsequent developments of unit root and cointegration tests in dynamic panels with and without cross-section dependence. In this note, we discuss the main idea behind the test and the publication process that led to Im et al. (2003).
Authors
Kyung So Im,M Hashem Pesaran,Yongcheol Shin
Journal
Journal of Econometrics
Published Date
2023/3/1
Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels
Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small sample performance of PB compared to the existing estimators in the literature, namely PMG, panel dynamic OLS (PDOLS), and panel fully-modified OLS (FMOLS). Application of two bias-correction methods and a bootstrapping of critical values to conduct inference robust to cross-sectional dependence of errors are also considered. The utility of the PB estimator is illustrated in an empirical application to the aggregate consumption function.
Authors
Alexander Chudik,M Hashem Pesaran,Ron P Smith
Journal
Econometrics and Statistics
Published Date
2023/11/3
Climate change and economic activity: evidence from US states
We investigate the long-term macroeconomic effects of climate change across 48 US states over the period 1963–2016 using a novel econometric strategy that links deviations of temperature and precipitation (weather) from their long-term moving-average historical norms (climate) to various state-specific economic performance indicators at the aggregate and sectoral levels. We show that climate change has a long-lasting adverse impact on real output in various states and economic sectors, and on labour productivity and employment in the United States. Moreover, in contrast to most cross-country results, our within US estimates tend to be asymmetrical with respect to deviations of climate variables (including precipitation) from their historical norms.
Authors
Kamiar Mohaddes,Ryan NC Ng,M Hashem Pesaran,Mehdi Raissi,Jui-Chung Yang
Journal
Oxford Open Economics
Published Date
2023/2/1
Professor FAQs
What is M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran's h-index at University of Southern California?
The h-index of M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran has been 71 since 2020 and 114 in total.
What are M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran's top articles?
The articles with the titles of
Testing for alpha in linear factor pricing models with a large number of securities
Forecasting with panel data: estimation uncertainty versus parameter heterogeneity
High-dimensional forecasting with known knowns and known unknowns
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Identification and estimation of categorical random coefficient models
Reflections on “Testing for unit roots in heterogeneous panels”
Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels
Climate change and economic activity: evidence from US states
...
are the top articles of M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran at University of Southern California.
What are M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran's research interests?
The research interests of M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran are: Econometrics, Macroeconomics, Applied Econometrics, Time Series Analysis, Panel Data Econometrics
What is M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran's total number of citations?
M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran has 166,091 citations in total.
What are the co-authors of M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran?
The co-authors of M. Hashem Pesaran, M H Pesaran, Mohammad H Pesaran, Mohammad Pesaran, M. Pesaran are Allan Timmermann, Gary Koop, Ron Smith, Cheng Hsiao, g kapetanios.