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This chapter 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 multicountry 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 are 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 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.

Productivity growth in Italy has been persistently anemic and lagged that of the euro area over the period 1999–2015, while the indebtedness of its corporate sector increased. Using the ORBIS firm-level database, this chapter studies the long-term impact of persistent corporate-debt accumulation on the productivity growth of Italian firms, and investigates whether total factor productivity (TFP) growth varies with the level of corporate indebtedness. The authors employ a novel estimation technique proposed by Chudik, Mohaddes, Pesaran, & Raissi (2017) to account for dynamics, bi-directional feedback effects, cross-firm heterogeneity, and cross-sectional dependence arising from unobserved common factors (e.g., oil price shocks, labor and product market frictions, and the stance of the global financial cycle). Filtering out the effects of unobserved common factors and controlling for firm-specific characteristics, the authors find significant negative effects of persistent corporate-debt build-up on firms’ TFP growth on average, and weak evidence of a threshold level of corporate debt, beyond which productivity growth drops off significantly. The results have strong policy implications, for example the design of the tax system should discourage persistent corporate-debt accumulation, and effective and timely frameworks to reduce corporate-debt overhangs are essential.

What would have been the hypothetical effect of monetary policy shocks had a country never joined the euro area, in cases where we know that the country in question actually did join the euro area? It is one thing to investigate the impact of joining a monetary union, but quite another to examine two things at once: joining the union and experiencing actual monetary policy shocks. The authors propose a methodology that combines synthetic control ideas with the impulse response functions to uncover dynamic response paths for treated and untreated units, controlling for common unobserved factors. Focusing on the largest euro area countries, Germany, France, and Italy, the authors find that an unexpected rise in interest rates depresses inflation and significantly appreciates exchange rate, whereas gross domestic product (GDP) fluctuations are less successfully controlled when a country belongs to the monetary union than would have been the case under the independent monetary policy. Importantly, Italy turns out to be the overall beneficiary, since all three channels – price, GDP, and exchange rate – deliver the desired results. The authors also find that stabilizing an economy within a union requires somewhat smaller policy changes than attempting to stabilize it individually, and therefore provides more policy space.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

The authors provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be non-stationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and the authors propose the mean group, common correlated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. The authors apply this model to the 324 local authorities of England, and show that our approach is useful for modeling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics. This exercise provides new insights on the (spatial) long-run relationship between house prices and income in the UK.