Introducing shrinkage in heavy-tailed state space models to predict equity excess returns

We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space model. The shrinkage priors are complemented by heavy tailed state innovations that cater for potential large breaks in the latent states. Moreover, we allow for leptokurtic stochastic volatility in the observation equation. The empirical findings indicate that several variants of the proposed approach outperform typical competitors frequently used in the literature, both in terms of point and density forecasts

Identification of and correction for publication bias

Some empirical results are more likely to be published than others. Such selective publication leads to biased estimates and distorted inference. This paper proposes two approaches for identifying the conditional probability of publication as a function of a study's results, the first based on systematic replication studies and the second based on meta-studies. For known conditional publication probabilities, we propose median-unbiased estimators and associated confidence sets that correct for selective publication. We apply our methods to recent large-scale replication studies in experimental economics and psychology, and to meta-studies of the effects of minimum wages and de-worming programs

Semiparametric Bayesian inference in multiple equation models

This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components. The approach treats the points on each nonparametric regression line as unknown parameters and uses a prior on the degree of smoothness of each line to ensure valid posterior inference despite the fact that the number of parameters is greater than the number of observations. We develop an empirical Bayesian approach that allows us to estimate the prior smoothing hyperparameters from the data. An advantage of our semiparametric model is that it is written as a seemingly unrelated regressions model with independent normal-Wishart prior. Since this model is a common one, textbook results for posterior inference, model comparison, prediction and posterior computation are immediately available. We use this model in an application involving a two-equation structural model drawn from the labour and returns to schooling literatures

Is the Relationship Between Aid and Economic Growth Nonlinear?

In this paper, we investigate the relationship between foreign aid and growth using recently developed sample splitting methods that allow us to uncover evidence for the existence of heterogeneity and nonlinearity simultaneously. We also implement a new methodology that allows us to deal with model uncertainty in the context of these methods. We find some evidence that aid may have heterogeneous effects on growth across two growth regimes defined by ethnic fractionalization. In particular, countries that belong to a growth regime characterized by levels of ethnic fractionalization above a threshold value experience a negative partial relationship between aid and growth, while those in the regime with ethnic fractionalization below the threshold experience no growth effects from aid at all. Nevertheless, there exists substantial model uncertainty so that attempts to pin down the typology of these growth regimes as being decisively characterized by ethnic fractionalization remain inconclusive. When we account for model uncertainty, we find no evidence to suggest that the relationship between aid and growth is nonlinear. Overall, our results suggest that the partial effect of aid on growth is very likely to be negative although we cannot reject the hypothesis that aid has no effect on growth. In this sense, our findings suggest that aid is potentially counterproductive to growth with outcomes not meeting the expectations of donors.

Onward and upward? An empirical investigation of gender and promotions in Information Technology Services

The shaky ascent of women up the organizational ladder is a critical factor that may contribute to the lack of women in information technology (IT). In this study, we examine the effect of gender on the likelihood of employee promotions. We further examine whether women get an equal lift in promotion likelihood from performance improvements, work experience, and training as men. We analyze archival promotion data, as well as demographic, human capital, and administrative data for 7,004 employees at a leading IT services firm located in India for the years 2002–2007 and for multiple levels of promotion. We develop robust econometric models that consider employee heterogeneity to identify the differential effect of gender and performance on promotions. We find that, contrary to expectations, women are more likely to be promoted, on average. However, looking deeper into the heterogeneous main effects using hierarchical Bayesian modeling reveals more nuanced insights. We find that, ceteris paribus, women realize less benefit from performance gains than men, less benefit from tenure within the focal firm, but more benefit from training than men. These results suggest that despite the disparity in returns to performance and experience improvements, women can rely on signaling mechanisms such as training to restore parity in promotions. We find that the effects of gender and performance vary with the level of employee promotion; although not as much as men, women benefit more from performance gains at higher organizational levels. Our findings suggest several actionable managerial insights that can potentially make IT firms more inclusive and attractive to women

Researcher Incentives and Empirical Methods

Economists are quick to assume opportunistic behavior in almost every walk of life other than our own. Our empirical methods are based on assumptions of human behavior that would not pass muster in any of our models. The solution to this problem is not to expect a mass renunciation of data mining, selective data cleaning or opportunistic methodology selection, but rather to follow Leamer's lead in designing and using techniques that anticipate the behavior of optimizing researchers. In this essay, I make ten points about a more economic approach to empirical methods and suggest paths for methodological progress.

Is the relationship between aid and economic growth nonlinear?:

"There have been intensive debates on the role of aid in promoting economic development in developing countries by using cross-country analyses. Cross-country regression assuming linear relationship between aid and growth and without taking into heterogeneity of countries would produce biased estimates. To correct this, in this paper we investigate the relationship between foreign aid and growth using recently developed sample splitting methods that allow us to simultaneously uncover evidence for the existence of heterogeneity and nonlinearity. We also address model uncertainty in the context of these methods. We find some evidence that aid may have heterogeneous effects on growth across two growth regimes defined by ethnolinguistic fractionalization. However, when we account for model uncertainty, we find no evidence to suggest that the relationship between aid and growth is nonlinear. In fact, our results suggest that the partial effect of aid on growth is likely to be weakly negative. In this sense, our findings suggest that aid is potentially counterproductive to growth with outcomes not meeting the expectations of donors... The methodology developed in this paper can be used to identify typologies on other outcome variables, such as those included in the Millennium Development Goals." from Authors' AbstractEconomic development, Cross-country studies, Foreign aid, Public investment, Nonlinearity, Typology,

PRICE FORECASTING WITH TIME-SERIES METHODS AND NONSTATIONARY DATA: AN APPLICATION TO MONTHLY U.S. CATTLE PRICES

The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of nonstationarity. Procedures that permit model specifications consistent with the systemÂ’s dynamics provide the most accurate forecasts.Demand and Price Analysis, Livestock Production/Industries,

Marginal Likelihood Estimation with the Cross-Entropy Method

We consider an adaptive importance sampling approach to estimating the marginal likelihood, a quantity that is fundamental in Bayesian model comparison and Bayesian model averaging. This approach is motivated by the difficulty of obtaining an accurate estimate through existing algorithms that use Markov chain Monte Carlo (MCMC) draws, where the draws are typically costly to obtain and highly correlated in high-dimensional settings. In contrast, we use the cross-entropy (CE) method, a versatile adaptive Monte Carlo algorithm originally developed for rare-event simulation. The main advantage of the importance sampling approach is that random samples can be obtained from some convenient density with little additional costs. As we are generating independent draws instead of correlated MCMC draws, the increase in simulation effort is much smaller should one wish to reduce the numerical standard error of the estimator. Moreover, the importance density derived via the CE method is in a well-defined sense optimal. We demonstrate the utility of the proposed approach by two empirical applications involving women's labor market participation and U.S. macroeconomic time series. In both applications the proposed CE method compares favorably to existing estimators