arXiv subjects

M. Hashem Pesaran

Publications and source records attributed to M. Hashem Pesaran.

14 recordsLinked to original sources

arXiv2026

Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity

We provide a comprehensive examination of the predictive performance 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.

econ.EM↗

arXiv2026

How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Test

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 over-rejection. 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 sense the strength is less than half. 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 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.

econ.EM↗

arXiv2025

Analysis of Multiple Long-Run Relations in Panel Data Models

The literature on panel cointegration is extensive but does not cover data sets where the cross section dimension, $n$, is larger than the time series dimension $T$. This paper proposes a novel methodology that filters out the short run dynamics using sub-sample time averages as deviations from their full-sample counterpart, and estimates the number of long-run relations and their coefficients using eigenvalues and eigenvectors of the pooled covariance matrix of these sub-sample deviations. We refer to this procedure as pooled minimum eigenvalue (PME). We show that PME estimator is consistent and asymptotically normal as $n$ and $T \rightarrow \infty$ jointly, such that $T\approx n^{d}$, with $d>0$ for consistency and $d>1/2$ for asymptotic normality. Extensive Monte Carlo studies show that the number of long-run relations can be estimated with high precision, and the PME estimators have good size and power properties. The utility of our approach is illustrated by micro and macro applications using Compustat and Penn World Tables.

econ.EM↗

arXiv2024

Identifying and exploiting alpha in linear asset pricing models with strong, semi-strong, and latent factors

The risk premia of traded factors are the sum of factor means and a parameter vector we denote by ϕ which is identified from the cross section regression of alpha of individual securities on the vector of factor loadings. If phi is non-zero one can construct "phi-portfolios" which exploit the systematic components of non-zero alpha. We show that for known values of betas and when phi is non-zero there exist phi-portfolios that dominate mean-variance portfolios. The paper then proposes a two-step bias corrected estimator of phi and derives its asymptotic distribution allowing for idiosyncratic pricing errors, weak missing factors, and weak error cross-sectional dependence. Small sample results from extensive Monte Carlo experiments show that the proposed estimator has the correct size with good power properties. The paper also provides an empirical application to a large number of U.S. securities with risk factors selected from a large number of potential risk factors according to their strength and constructs phi-portfolios and compares their Sharpe ratios to mean variance and S&P 500 portfolio.

econ.EM↗

arXiv2024

Variable Selection in High Dimensional Linear Regressions with Parameter Instability

This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also investigated, and in-sample fit of the selected regression is shown to have the oracle property. The theoretical results support the use of unweighted observations at the selection stage of OCMT, whilst applying down-weighting of observations only at the forecasting stage. Monte Carlo and empirical applications show that OCMT without down-weighting at the selection stage yields smaller mean squared forecast errors compared to Lasso, Adaptive Lasso, and boosting.

econ.EM↗

arXiv2024

Heterogeneous Autoregressions in Short T Panel Data Models

This paper considers a first-order autoregressive panel data model with individual-specific effects and heterogeneous autoregressive coefficients defined on the interval (-1,1], thus allowing for some of the individual processes to have unit roots. It proposes estimators for the moments of the cross-sectional distribution of the autoregressive (AR) coefficients, assuming a random coefficient model for the autoregressive coefficients without imposing any restrictions on the fixed effects. It is shown the standard generalized method of moments estimators obtained under homogeneous slopes are biased. Small sample properties of the proposed estimators are investigated by Monte Carlo experiments and compared with a number of alternatives, both under homogeneous and heterogeneous slopes. It is found that a simple moment estimator of the mean of heterogeneous AR coefficients performs very well even for moderate sample sizes, but to reliably estimate the variance of AR coefficients much larger samples are required. It is also required that the true value of this variance is not too close to zero. The utility of the heterogeneous approach is illustrated in the case of earnings dynamics.

econ.EM↗

arXiv2024

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.

econ.EM↗

arXiv2023

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.

econ.EM↗

arXiv2023

Causal effects of the Fed's large-scale asset purchases on firms' capital structure

We investigate the short- and long-term impacts of the Federal Reserve's large-scale asset purchases (LSAPs) on non-financial firms' capital structure using a threshold panel ARDL model. To isolate the effects of LSAPs from other macroeconomic conditions, we interact firm- and industry-specific indicators of debt capacity with measures of LSAPs. We find that LSAPs facilitated firms' access to external financing, with both Treasury and MBS purchases having positive effects. Our model also allows us to estimate the time profile of the effects of LSAPs on firm leverage providing robust evidence that they are long-lasting. These effects have a half-life of 4-5 quarters and a mean lag length of about six quarters. Nevertheless, the magnitudes are small, suggesting that LSAPs have contributed only marginally to the rise in U.S. corporate debt ratios of the past decade.

econ.GN↗

arXiv2023

Estimation of Average Effects in Short $T$ Heterogeneous Panels

The commonly used two-way fixed effects estimator is biased under correlated heterogeneity and can lead to misleading inference. The mean group estimator proposed by Pesaran and Smith (1995) is robust to correlated heterogeneity but requires the underlying individual estimates to have second-order moments that could fail if the number of estimated coefficients ($k$) is too close to the time dimension ($T$) of the panel. This paper focuses on panels where $k$ is close to $T$ (including $k=T$), and proposes a trimmed mean group (TMG) estimator that shrinks individual estimates most likely to fail the second-order moment condition. The TMG estimator is shown to be $n^{(1-\alpha )/2}$-consistent and asymptotically normally distributed, where $\alpha$ is determined by the degree to which individual estimates might not have moments. The $\sqrt{n}$ convergence rate is achieved only if all individual estimates have second-order moments. Extensions to panels with time effects are provided, and a new Hausman test of correlated heterogeneity is proposed. Small sample properties of the TMG estimator (with and without time effects) are investigated by Monte Carlo experiments and shown to be satisfactory. The proposed test of correlated heterogeneity is also shown to have the correct size and satisfactory power. The utility of the TMG approach is illustrated with an empirical application.

econ.EM↗

arXiv2023

Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 Across U.S. States and Selected Countries

This paper proposes a structural econometric approach to estimating the basic reproduction number ($\mathcal{R}_{0}$) of Covid-19. This approach identifies $\mathcal{R}_{0}$ in a panel regression model by filtering out the effects of mitigating factors on disease diffusion and is easy to implement. We apply the method to data from 48 contiguous U.S. states and a diverse set of countries. Our results reveal a notable concentration of $\mathcal{R}_{0}$ estimates with an average value of 4.5. Through a counterfactual analysis, we highlight a significant underestimation of the $\mathcal{R}_{0}$ when mitigating factors are not appropriately accounted for.

econ.EM↗

arXiv2023

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 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 U.S. by gender and educational levels. We find that rising heterogeneity between educational groups is mainly due to the increasing returns to education for those with postsecondary education, whereas within group heterogeneity has been rising mostly in the case of individuals with high school or less education.

econ.EM↗

arXiv2022

Matching Theory and Evidence on Covid-19 using a Stochastic Network SIR Model

This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under-reporting of infected cases. Empirical evidence on six European countries matches the simulated outcomes once the under-reporting of infected cases is addressed. It is estimated that the number of actual cases could be between 4 to 10 times higher than the reported numbers in October 2020 and declined to 2 to 3 times in April 2021. The calibrated models are used in the counterfactual analyses of the impact of social distancing and vaccination on the epidemic evolution, and the timing of early interventions in the UK and Germany.

econ.EM↗

arXiv2021

Identifying the Effects of Sanctions on the Iranian Economy using Newspaper Coverage

This paper considers how sanctions affected the Iranian economy using a novel measure of sanctions intensity based on daily newspaper coverage. It finds sanctions to have significant effects on exchange rates, inflation, and output growth, with the Iranian rial over-reacting to sanctions, followed up with a rise in inflation and a fall in output. In absence of sanctions, Iran's average annual growth could have been around 4-5 per cent, as compared to the 3 per cent realized. Sanctions are also found to have adverse effects on employment, labor force participation, secondary and high-school education, with such effects amplified for females.

econ.GN↗