Is there a debt-threshold effect on output growth?
This paper studies the long-run impact of public debt expansion on economic growth and investigates whether the debt-growth relation varies with the level of indebtedness. Our contribution is both theoretical and empirical. On the theoretical side, we develop tests for threshold effects in the context of dynamic heterogeneous panel data models with crosssectionally dependent errors and illustrate, by means of Monte Carlo experiments, that they perform well in small samples. On the empirical side, using data on a sample of 40 countries (grouped into advanced and developing) over the 1965-2010 period, we find no evidence for a universally applicable threshold effect in the relationship between public debt and economic growth, once we account for the impact of global factors and their spillover effects. Regardless of the threshold, however, we find significant negative long-run effects of public debt build-up on output growth. Provided that public debt is on a downward trajectory, a country with a high level of debt can grow just as fast as its peers.
AUTHORS: Pesaran, M. Hashem; Mohaddes, Kamiar; Raissi, Mehdi; Chudik, Alexander
A one-covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models
Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised regression has become the de facto benchmark technique used to trade off parsimony and fit when the number of possible covariates is large, often much larger than the number of available observations. However, issues such as the choice of a penalty function and tuning parameters associated with the use of penalized regressions remain contentious. In this paper, we provide an alternative approach that considers the statistical significance of the individual covariates one at a time, whilst taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time Multiple Testing (OCMT) procedure. The OCMT provides an alternative to penalised regression methods: It is based on statistical inference and is therefore easier to interpret and relate to the classical statistical analysis, it allows working under more general assumptions, it is faster, and performs well in small samples for almost all of the different sets of experiments considered in this paper. We provide extensive theoretical and Monte Carlo results in support of adding the proposed OCMT model selection procedure to the toolbox of applied researchers. The usefulness of OCMT is also illustrated by an empirical application to forecasting U.S. output growth and inflation.
AUTHORS: Kapetanios, George; Pesaran, M. Hashem; Chudik, Alexander
Modelling global trade flows: results from a GVAR model
This paper uses a Global Vector Auto-Regression (GVAR) model featuring 21 emerging market and advanced economies to investigate the factors behind the dynamics of global trade flows, with a particular view on the issue of global trade imbalances and on the conditions of their unwinding. The GVAR approach enables us to make two key contributions: first, to model international linkages among a large number of countries, which is a key asset given the diversity of countries and regions involved in global imbalances, and second, to model exports and imports jointly. The latter proves to be very important due to the internationalization of production chains. The model can be used to gauge the effect on trade flows of various scenarios, such as an output shock in the United States, a shock to the US real effective exchange rate and shocks to foreign (e.g., German and Chinese) variables. Results indicate that changes in domestic and foreign demand have a much stronger effect on trade flows than changes in relative trade prices. In addition, we show how the model can be used to monitor trade developments, with an application to the Great Trade Collapse.
AUTHORS: Chudik, Alexander; Sestieri, Giulia; Bussiere, Matthieu
A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels
This paper contributes to the GMM literature by introducing the idea of self-instrumenting target variables instead of searching for instruments that are uncorrelated with the errors, in cases where the correlation between the target variables and the errors can be derived. The advantage of the proposed approach lies in the fact that, by construction, the instruments have maximum correlation with the target variables and the problem of weak instrument is thus avoided. The proposed approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. In this paper we focus on the latter and consider both univariate and multivariate panel data models with short time dimension. Simple Bias-corrected Methods of Moments (BMM) estimators are proposed and shown to be consistent and asymptotically normal, under very general conditions on the initialization of the processes, individual-specific effects, and error variances allowing for heteroscedasticity over time as well as cross-sectionally. Monte Carlo evidence document BMM?s good small sample performance across different experimental designs and sample sizes, including in the case of experiments where the system GMM estimators are inconsistent. We also find that the proposed estimator does not suffer size distortions and has satisfactory power performance as compared to other estimators.
AUTHORS: Pesaran, M. Hashem; Chudik, Alexander
Half-panel jackknife fixed effects estimation of panels with weakly exogenous regressor
This paper considers estimation and inference in fixed effects (FE) panel regression models with lagged dependent variables and/or other weakly exogenous (or predetermined) regressors when NN (the cross section dimension) is large relative to TT (the time series dimension). The paper first derives a general formula for the bias of the FE estimator which is a generalization of the Nickell type bias derived in the literature for the pure dynamic panel data models. It shows that in the presence of weakly exogenous regressors, inference based on the FE estimator will result in size distortions unless NN/TT is sufficiently small. To deal with the bias and size distortion of FE estimator when NN is large relative to TT, the use of half-panel Jackknife FE estimator is proposed and its asymptotic distribution is derived. It is shown that the bias of the proposed estimator is of order TT ?2, and for valid inference it is only required that NN/TT3 --> 0, as NN, TT --> 00 jointly. Extensions to panel data models with time effects (TE), for balanced as well as unbalanced panels, are also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE estimator can suffer from large size distortions when NN > TT, with the proposed estimator showing little size distortions. The use of half-panel jackknife FE-TE estimator is illustrated with two empirical applications from the literature.
AUTHORS: Chudik, Alexander; Pesaran, M. Hashem; Yang, Jui-Chung
The GVAR approach and the dominance of the U.S. economy
This paper extends the recent literature about global macroeconomic modelling by allowing the presence of a globally dominant economy. Our contribution is both theoretical and empirical. From a theoretical standpoint, we follow Chudik and Pesaran (2011 and 2012) to derive the GVAR approach as an approximation to two Infinite-Dimensional VAR (IVAR) models featuring nonstationary variables: one corresponding to the world consisting of several small open economies (benchmark model), and one corresponding to the world featuring a dominant economy (extended model). ; It is established that in the presence of a dominant economy, restrictions implied by the asymptotic analysis of a system without a dominant economy are no longer valid. The theoretical framework is then brought to the data by estimating both versions of the GVAR model featuring 33 countries for the period 1979(Q2)?2003(Q4). We found some support for the extended version of the GVAR model, allowing the US to be the dominant player in the world economy.
AUTHORS: Smith, Vanessa; Chudik, Alexander
Big data analytics: a new perspective
Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised regression has become the de facto benchmark technique used to trade off parsimony and fit when the number of possible covariates is large, often much larger than the number of available observations. However, issues such as the choice of a penalty function and tuning parameters associated with the use of penalised regressions remain contentious. In this paper, we provide an alternative approach that considers the statistical significance of the individual covariates one at a time, whilst taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time Multiple Testing (OCMT) procedure. The OCMT has a number of advantages over the penalised regression methods: It is based on statistical inference and is therefore easier to interpret and relate to the classical statistical analysis, it allows working under more general assumptions, it is computationally simple and considerably faster, and it performs better in small samples for almost all of the five different sets of experiments considered in this paper. Despite its simplicity, the theory behind the proposed approach is quite complicated. We provide extensive theoretical and Monte Carlo results in support of adding the proposed OCMT model selection procedure to the toolbox of applied researchers.
AUTHORS: Kapetanios, George; Chudik, Alexander; Pesaran, M. Hashem
Estimating Impulse Response Functions When the Shock Series Is Observed
We compare the finite sample performance of a variety of consistent approaches to estimating Impulse Response Functions (IRFs) in a linear setup when the shock of interest is observed. Although there is no uniformly superior approach, iterated approaches turn out to perform well in terms of root mean-squared error (RMSE) in diverse environments and sample sizes. For smaller sample sizes, parsimonious specifications are preferred over full specifications with all ?relevant? variables.
AUTHORS: Chudik, Alexander; Choi, Chi-Young
Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors
This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variable and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section averages must be at least as large as the number of unobserved common factors. We establish consistency rates, derive the asymptotic distribution, suggest using co-variates to deal with the effects of multiple unobserved common factors, and consider jackknife and recursive de-meaning bias correction procedures to mitigate the small sample time series bias. Theoretical findings are accompanied by extensive Monte Carlo experiments, which show that the proposed estimators perform well so long as the time series dimension of the panel is sufficiently large.
AUTHORS: Chudik, Alexander; Pesaran, M. Hashem
Identifying Global and National Output and Fiscal Policy Shocks Using a GVAR
The paper contributes to the growing Global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multi-country setting, and the results are compared to those obtained from standard single-country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations is exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.58 when the rise in output is due to a fiscal expansion as compared to 0.75 when the rise in output follows from a favorable output shock.
AUTHORS: Pesaran, M. Hashem; Chudik, Alexander; Mohaddes, Kamiar