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Working Papers


Archives: WP version of published papers


  • "Trimmed Mean Group Estimation of Average Treatment Effects in Ultra Short T Panels under Correlated Heterogeneity", by M. Hashem Pesaran and Liying Yang, October 2023, Cambridge Working Papers in Economics, CWPE2364

    Abstract: Under correlated heterogeneity, the commonly used two-way fixed effects estimator is biased and can lead to misleading inference. This paper proposes a new trimmed mean group (TMG) estimator which is consistent at the irregular rate of n1/3 even if the time dimension of the panel is as small as the number of its regressors. Extensions to panels with time effects are provided, and a Hausman-type 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 and perform better than other trimmed estimators proposed in the literature. 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.
    JEL Classifications: C21, C23
    Key Words: Correlated heterogeneity, irregular estimators, two-way fixed effects, FE-TE, tests of correlated heterogeneity, calorie demand
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  • "Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 Across U.S. States and Selected Countries", by Ida Johnsson, M. Hashem Pesaran and Cynthia Fan Yang, August 2023, Cambridge Working Papers in Economics, CWPE2360

    Abstract: This paper proposes a structural econometric approach to estimating the basic reproduction number (R0) of Covid-19. This approach identifies R0 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 R0 estimates with an average value of 4.5. Through a counterfactual analysis, we highlight a significant underestimation of the R0 when mitigating factors are not appropriately accounted for.
    JEL Classifications: C13, C33, I12, I18, J18
    Key Words: Basic reproduction number, Covid-19, panel threshold regression model
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  • "The Role of Pricing Errors in Linear Asset Pricing Models with Strong, Semi-strong, and Latent Factors", by M. Hashem Pesaran and Ron P. Smith, February 2023, Cambridge Working Papers in Economics, CWPE2317, CESifo Working Paper No. 10282

    Abstract: This paper examines the role of pricing errors in linear factor pricing models, allowing for observed strong and semi-strong factors, and latent weak factors. It focusses on the estimation of Φk = λk - μk which plays a pivotal role, not only in the estimation of risk premia but also in tests of market efficiency, where λk and μk are respectively the risk premium and the mean of the kth risk factor. It proposes a two-step estimator of Φk with Shanken type bias-correction, and derives its asymptotic distribution under a general setting that allows for idiosyncratic pricing errors, weak missing factors, as well as weak error cross-sectional dependence. The implications of semi-strong factors for the asymptotic distribution of the proposed estimator are also investigated. 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.
    JEL Classifications: C38, G10
    Key Words: Factor strength, pricing errors, risk premia, missing factors, Fama-French factors, panel R2
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    CESifo Link:


  • "Causal Effects of the Fed's Large-Scale Asset Purchases on Firms' Capital Structure", by Andrea Nocera and M. Hashem Pesaran, April 2022, revised October 2023, Cambridge Working Papers in Economics, CWPE2224

    Abstract: 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.
    JEL Classifications: G32, E44, E52, E58
    Key Words: Capital structure, identification, interactive effects, leverage, threshold panel ARDL, quantitative easing, unconventional monetary policy
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  • "Forecasting with Panel Data: Estimation Uncertainty Versus Parameter Heterogeneity", by M. Hashem Pesaran, Andreas Pick and Allan Timmermann, March 2022, revised March 2024, Cambridge Working Papers in Economics, CWPE2219

    Abstract: 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 (N) and time (T) 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.
    JEL Classifications: C33, C53
    Key Words: Forecasting, Panel data, Heterogeneity, Pooled estimation; Forecast combination
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    Arxiv Link:


  • "A Bias-Corrected CD Test for Error Cross-Sectional Dependence in Panel Data Models with Latent Factors", by M. Hashem Pesaran and Yimeng Xie, July 2021, revised August 2022, Cambridge Working Papers in Economics, CWPE2158 and and CESifo Working Paper No. 9234.

    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 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. It shows that the standard CD test remains valid if the latent factors are weak, and proposes a simple bias-corrected CD test, labelled CD*, which is shown to be asymptotically normal, irrespective of whether the latent factors are weak or strong. This result is shown to hold for pure latent factor models as well as for panel regressions 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 and satisfactory power for both 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. The use of the CD* test is illustrated with two empirical applications from the literature.
    JEL Classifications: C18, C23, C55
    Key Words: Latent factor models, strong and weak factors, error cross-sectional dependence, spatial and network alternatives, size and power
    Full Text: CDstar_Paper_Rev_August_23_2022.pdf
    Arxiv Link:
    Data and Codes:


  • "Variable Selection in High Dimensional Linear Regressions with Parameter Instability", by Alexander Chudik, M. Hashem Pesaran and Mahrad Sharifvaghe, July 2020, revised January 2023.

    Abstract: This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses the issue of whether weighted or unweighted observations should be used at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches, we focus on the One Covariate at a time Multiple Testing (OCMT) method. This procedure allows a natural distinction between the selection and forecasting stages. 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. The benefits of the proposed method as compared to Lasso, Adaptive Lasso and Boosting are illustrated by Monte Carlo studies and empirical applications to forecasting monthly stock market returns and quarterly output growths.
    JEL Classifications: C22, C52, C53, C55
    Key Words: Parameter instability, High-dimensionality, Variable selection, One Covariate at a time Multiple Testing (OCMT)
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  • "The Role of Factor Strength and Pricing Errors for Estimation and Inference in Asset Pricing Models", by M. Hashem Pesaran and Ron P. Smith, CESifo Working Paper No. tbc, October 2019.

    Abstract: In this paper we are concerned with the role of factor strength and pricing errors in asset pricing models, and their implications for identification and estimation of risk premia. We establish an explicit relationship between the pricing errors and the presence of weak factors that are correlated with stochastic discount factor. We introduce a measure of factor strength, and distinguish between observed factors and unobserved factors. We show that unobserved factors matter for pricing if they are correlated with the discount factor, and relate the strength of the weak factors to the strength (pervasiveness) of non-zero pricing errors. We then show, that even when the factor loadings are known, the risk premia of a factor can be consistently estimated only if it is strong and if the pricing errors are weak. Similar results hold when factor loadings are estimated, irrespective of whether individual returns or portfolio returns are used. We derive distributional results for two pass estimators of risk premia, allowing for non-zero pricing errors. We show that for inference on risk premia the pricing errors must be sufficiently weak. We consider both when n (the number of securities) is large and T (the number of time periods) is short, and the case of large n and T. Large n is required for consistent estimation of risk premia, whereas the choice of short T is intended to reduce the possibility of time variations in the factor loadings. We provide monthly rolling estimates of the factor strengths for the three Fama-French factors over the period 1989-2018.
    JEL Classifications: C38, G12
    Key Words: Arbitrage Pricing Theory, APT, factor strength, identification of risk premia, two-pass regressions, Fama-French factors.
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  • "Business Cycle Effects of Credit Shocks in a DSGE Model with Firm Defaults", by M. Hashem Pesaran and TengTeng Xu, CWPE Working paper. No. 1159, CESifo Working Paper No. 3609, IZA Discussion Paper No. 6027, October 2011, revised April 2016

    Abstract: This paper proposes a new theoretical framework for the analysis of the relationship between credit shocks, firm defaults and volatility. The key feature of the modelling approach is to allow for the possibility of default in equilibrium. The model is then used to study the impact of credit shocks on business cycle dynamics. It is assumed that firms are identical ex ante but differ ex post due to different realizations of firm-specific technology shocks, possibly leading to default by some firms. The implications of firm defaults for the balance sheets of households and banks and their subsequent impacts on business uctuations are investigated within a dynamic stochastic general equilibrium framework. Results from a calibrated version of the model suggest that, in the steady state, a firm's default probability rises with its leverage ratio and the level of uncertainty in the economy. A positive credit shock, defined as a rise in the loan-to-deposit ratio, increases output, consumption, hours and productivity, and reduces the spread between loan and deposit rates. Interestingly, the effects of the credit shock tend to be highly persistent, even without price rigidities and habit persistence in consumption behavior.
    JEL Classifications: E32, E44, E50, G21.
    Key Words: Firm Defaults; Credit Shocks; Financial Intermediation; Interest Rate Spread; Uncertainty.
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    Supplement: 5Oct2011_Supplement.pdf


  • "Optimality and Diversifiability of Mean Variance and Arbitrage Pricing Portfolios", by M. Hashem Pesaran, and Paolo Zaffaroni, CESifo Working Papers No. 2857, October, 2009

    Abstract: This paper investigates the limit properties of mean-variance (mv) and arbitrage pricing (ap) trading strategies using a general dynamic factor model, as the number of assets diverge to infinity. It extends the results obtained in the literature for the exact pricing case to two other cases of asymptotic no-arbitrage and the unconstrained pricing scenarios. The paper characterizes the asymptotic behaviour of the portfolio weights and establishes that in the non-exact pricing cases the ap and mv portfolio weights are asymptotically equivalent and, moreover, functionally independent of the factors conditional moments. By implication, the paper sheds light on a number of issues of interest such as the prevalence of short-selling, the number of dominant factors and the granularity property of the portfolio weight.
    JEL Classifications: Large Portfolios, Factor Models, Mean-Variance Portfolio, Arbitrage Pricing, Market (Beta) Neutrality, Well Diversification.
    Key Words: C32, C52, C53, G11.
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