Jointness of Growth Determinants

Model uncertainty arises from uncertainty about correct economic theories, data issues and empirical specification problems. This paper investigates mutual dependence or jointness among variables in explaining the dependent variable. Jointness departs from univariate measures of variable importance, while addressing model uncertainty and allowing for generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct, but interacting economic factors. Negative jointness implies that explanatory variables are substitutes and act as proxies for a similar underlying mechanism. In a cross-country dataset, we show that jointness among 67 determinants of growth is important, ffecting inference and economic policy

Jointness of Growth Determinants

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model space, thereby taking model uncertainty into account. By looking beyond marginal measures of variable importance, jointness reveals generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct, but mutually reinforcing effects. Negative jointness implies that explanatory variables are substitutes and capture similar underlying effects. In a cross-country dataset we show that jointness among 67 determinants of growth is important, affecting inference and informing economic policy.model uncertainty, dependence among regressors, jointness, determinants of economic growth

Robust Growth Determinants.

This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsi- monious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model averaging to growth determinants, the paper finds that eight out of eighteen variables found to be significantly related to economic growth by Sala-i-Martin et al. (2004) are sensitive to deviations from benchmark model averaging. For example, the GDP shares of mining or government consumption, are no longer robust or economically significant once deviations from the normal benchmark assumptions are allowed. The paper identifies outlying observations { most notably Botswana { in explaining economic growth in a cross-section of countries.Determinants of Economic Growth; Robust Model Averaging; Heteroscedasticity; Outliers; Mixture models.

Robust Growth Determinants

This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsimonious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model averaging to growth determinants, the paper finds that eight out of eighteen variables found to be significantly related to economic growth by Sala-i-Martin et al. (2004) are sensitive to deviations from benchmark model averaging. For example, the GDP shares of mining or government consumption, are no longer robust or economically significant once deviations from the normal benchmark assumptions are allowed. The paper identifies outlying observations - most notably Botswana - in explaining economic growth in a cross-section of countries.determinants of economic growth, robust model averaging, heteroscedasticity, outliers, mixture models

Nonlinearities in Cross-Country Growth Regressions: A Bayesian Averaging of Thresholds (BAT) Approach

We propose a Bayesian Averaging of Thresholds (BAT) approach for assessing the existence and quantifying the effect of threshold effects in cross-country growth regressions in the presence of model uncertainty. The BAT method extends the Bayesian Averaging of Classical Estimates (BACE) approach proposed by Sala-i-Martin, Doppelhofer, and Miller (2004) by allowing for uncertainty over nonlinear threshold effects. We apply our method to a set of determinants of long-term economic growth in the cross section of 88 countries. Our results suggest that when model uncertainty is taken into account there is no evidence for robust threshold effects caused by the Initial Income, measured by GDP capita in 1960, but that the Number of Years an Economy has Been Open is an important source of nonlinear effects on growth

Nonlinearities in Cross-Country Growth Regressions: A Bayesian Averaging of Thresholds (BAT) Approach

We propose a framework for assessing the existence and quantifying the effect of threshold effects in cross-country growth regressions in the presence of model uncertainty. The method is based on Bayesian model averaging tech- niques and generalizes the Bayesian Averaging of Classical Estimates (BACE) method put forward by Sala-i-Martin, Doppelhofer, and Miller (2004). We ap- ply the method presented in this paper to a set of 21 variables that have been found to be robustly related to economic growth in a cross-section of 88 coun- tries. We find no evidence of robust threshold effects generated by the initial level of GDP per capita. However, we find that the proportion of years a country has been open to trade is an important source of nonlinear effects on economic growth.

The Determinants of Economic Growth in European Regions

We use Bayesian Model Averaging (BMA) to evaluate the robustness of determinants of economic growth in a new dataset of 255 European regions in the 1995-2005 period. We use three different specifications based on (1) the cross-section of regions, (2) the cross-section of regions with country fixed effects and (3) the cross-section of regions with a spatial autoregressive (SAR) structure. We investigate the existence of parameter heterogeneity by allowing for interactions of potential explanatory variables with geographical dummies as extra regressors. We find remarkable differences between the determinants of economic growth implied by differences between regions and those within regions of a given country. In the cross-section of regions, we find evidence for conditional convergence with speed around two percent. The convergence process between countries is dominated by the catching up process of regions in Central and Eastern Europe (CEE), whereas convergence within countries is mostly a characteristic of regions in old EU member states. We also find robust evidence of positive growth of capital cities, a highly educated workforce and a negative effect of population density.model uncertainty, spatial autoregressive model, determinants of economic growth, European regions

Spillovers from US monetary policy: Evidence from a time-varying parameter global vector autoregressive model

The paper develops a global vector auto-regressive model with time varying pa- rameters and stochastic volatility to analyse whether international spillovers of US monetary policy have changed over time. The model proposed enables us to assess whether coefficients evolve gradually over time or are better characterized by infrequent, but large, breaks. Our find- ings point towards pronounced changes in the international transmission of US monetary policy throughout the sample period, especially so for the reaction of international output, equity prices and exchange rates against the US dollar. In general, the strength of spillovers has weakened in the aftermath of the global financial crisis. Using simple panel regressions, we link the vari- ation in international responses to measures of trade and financial globalization. We find that a broad trade base and a high degree of financial integration with the world economy tend to cushion risks stemming from a foreign shock such as US tightening of monetary policy, whereas a reduction in trade barriers and/or a liberalization of the capital account increase these risks

Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach

This paper examines the robustness of explanatory variables in cross-country economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible combination of included variables. The weights applied to individual regressions are justified on Bayesian grounds in a way similar to the well-known Schwarz criterion. Of 32 explanatory variables we find 11 to be robustly partially correlated with long-term growth and another five variables to be marginally related. Of all the variables considered, the strongest evidence is for the initial level of real GDP per capita.