Enrique Moral-Benito

Bio: Enrique Moral-Benito is an academic researcher from Bank of Spain. The author has contributed to research in topics: Total factor productivity & Productivity. The author has an hindex of 30, co-authored 113 publications receiving 2701 citations. Previous affiliations of Enrique Moral-Benito include CEMFI & Charles III University of Madrid.

Is infrastructure capital productive ? a dynamic heterogeneous approach

Abstract: This paper offers an empirical evaluation of the output contribution of infrastructure. Drawing from a large data set on infrastructure stocks covering 88 countries and spanning the years 1960-2000, and using a panel time-series approach, the paper estimates a long-run aggregate production function relating GDP to human capital, physical capital, and a synthetic measure of infrastructure given by the first principal component of infrastructure endowments in transport, power, and telecommunications. Tests of the cointegration rank allowing it to vary across countries reveal a common rank with a single cointegrating vector, which is taken to represent the long-run production function. Estimation of its parameters is performed using the pooled mean group estimator, which allows for unrestricted short-run parameter heterogeneity across countries while imposing the (testable) restriction of long-run parameter homogeneity. The long-run elasticity of output with respect to the synthetic infrastructure index ranges between 0.07 and 0.10. The estimates are highly significant, both statistically and economically, and robust to alternative dynamic specifications and infrastructure measures. There is little evidence of long-run parameter heterogeneity across countries, whether heterogeneity is unconditional, or conditional on their level of development, population size, or infrastructure endowments.

Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects:

Abstract: Panel data make it possible both to control for unobserved confounders and allow for lagged, reciprocal causation. Trying to do both at the same time, however, leads to serious estimation difficult...

Model averaging in economics: an overview

Abstract: Standard practice in empirical research is based on two steps: first, researchers select a model from the space of all possible models; second, they proceed as if the selected model had generated the data. Therefore, uncertainty in the model selection step is typically ignored. Alternatively, model averaging accounts for this model uncertainty. In this paper, I review the literature on model averaging with special emphasis on its applications to economics. Finally, as an empirical illustration, I consider model averaging to examine the deterrent effect of capital punishment across states in the USA.

Determinants of Economic Growth: A Bayesian panel data approach

Abstract: Model uncertainty hampers consensus on the key determinants of economic growth. Some recent cross-country, cross-sectional analyses have employed Bayesian Model Averaging to address the issue of model uncertainty. This paper extends that approach to panel data models with country-specific fixed effects. The empirical results show that the most robust growth determinants are the price of investment goods, distance to major world cities, and political rights. This suggests that growth-promoting policy strategies should aim to reduce taxes and distortions that raise the prices of investment goods; improve access to international markets; and promote democracy-enhancing institutional reforms. Moreover, the empirical results are robust to different prior assumptions on expected model size.

Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors

Abstract: This article discusses the likelihood-based estimation of panel data models with individual-specific effects and both lagged dependent variable regressors and additional predetermined explanatory variables. The resulting new estimator, labeled as subsystem limited information maximum likelihood (ssLIML), is asymptotically equivalent to standard panel generalized method of moment as N → ∞ for fixed T but tends to present smaller biases in finite samples as illustrated in simulation experiments. Simulation results also indicate that the estimator is preferred to other alternatives available in the literature in terms of finite-sample performance. Finally, to provide an empirical illustration, I revisit the evidence on the relationship between income and democracy in a panel of countries using the proposed estimator.

The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics

Abstract: This essay reviews progress in empirical economics since Leamer's (1983) critique. Leamer highlighted the benefits of sensitivity analysis, a procedure in which researchers show how their results change with changes in specification or functional form. Sensitivity analysis has had a salutary but not a revolutionary effect on econometric practice. As we see it, the credibility revolution in empirical work can be traced to the rise of a design-based approach that emphasizes the identification of causal effects. Design-based studies typically feature either real or natural experiments and are distinguished by their prima facie credibility and by the attention investigators devote to making the case for a causal interpretation of the findings their designs generate. Design-based studies are most often found in the microeconomic fields of Development, Education, Environment, Labor, Health, and Public Finance, but are still rare in Industrial Organization and Macroeconomics. We explain why IO and Macro would do well to embrace a design-based approach. Finally, we respond to the charge that the design-based revolution has overreached.

Capital Allocation and Productivity in South Europe

Abstract: Following the introduction of the euro in 1999, countries in the South experienced large capital inflows and low productivity We use data for manufacturing firms in Spain to document a significant increase in the dispersion of the return to capital across firms, a stable dispersion of the return to labor across firms, and a significant increase in productivity losses from misallocation over time We develop a model of heterogeneous firms facing financial frictions and investment adjustment costs The model generates cross-sectional and time-series patterns in size, productivity, capital returns, investment, and debt consistent with those observed in production and balance sheet data We illustrate how the decline in the real interest rate, often attributed to the euro convergence process, leads to a decline in sectoral total factor productivity as capital inflows are misallocated toward firms that have higher net worth but are not necessarily more productive We conclude by showing that similar trends in dispersion and productivity losses are observed in Italy and Portugal but not in Germany, France, and Norway

Forecasting with Many Predictors

Abstract: Historically, time series forecasts of economic variables have used only a handful of predictor variables, while forecasts based on a large number of predictors have been the province of judgmental forecasts and large structural econometric models. The past decade, however, has seen considerable progress in the development of time series forecasting methods that exploit many predictors, and this chapter surveys these methods. The first group of methods considered is forecast combination (forecast pooling), in which a single forecast is produced from a panel of many forecasts. The second group of methods is based on dynamic factor models, in which the comovements among a large number of economic variables are treated as arising from a small number of unobserved sources, or factors. In a dynamic factor model, estimates of the factors (which become increasingly precise as the number of series increases) can be used to forecast individual economic variables. The third group of methods is Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model. The chapter also discusses empirical Bayes methods, in which the hyperparameters of the priors are estimated. An empirical illustration applies these different methods to the problem of forecasting the growth rate of the U.S. index of industrial production with 130 predictor variables.