Optimal allocation of Monte Carlo simulations to multiple hypothesis tests

Statistics and Computing - Multiple hypothesis tests are often carried out in practice using p-value estimates obtained with bootstrap or permutation tests since the analytical p-values underlying...     

Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments

A Bayesian approach for the many instruments problem in linear instrumental variable models is presented. The new approach has two components. First, a slice sampler is developed, which leverages a decomposition of the likelihood function that is a Bayesian analogue to two-stage least squares. The new sampler permits nonconjugate shrinkage priors to be implemented easily and efficiently. The new computational approach permits a Bayesian analysis of problems that were previously infeasible due to computational demands that scaled poorly in the number of regressors. Second, a new predictor-dependent shrinkage prior is developed specifically for the many instruments setting. The prior is constructed based on a factor model decomposition of the matrix of observed instruments, allowing many instruments to be incorporated into the analysis in a robust way. Features of the new method are illustrated via a simulation study and three empirical examples.     

Comments on “Convergence Properties of the Likelihood of Computed Dynamic Models”

We show by counterexample that Proposition 2 in Fernández‐Villaverde, Rubio‐Ramírez, and Santos (Econometrica (2006), 74, 93–119) is false. We also show that even if their Proposition 2 were corrected, it would be irrelevant for parameter estimates. As a more constructive contribution, we consider the effects of approximation error on parameter estimation, and conclude that second order approximation errors in the policy function have at most second order effects on parameter estimates.     

Jackknife and Analytical Bias Reduction for Nonlinear Panel Models

Fixed effects estimators of panel models can be severely biased because of the well‐known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed effects, as well as model parameters. We find large bias reductions from using these approaches in examples. We consider asymptotics where T grows with n, as an approximation to the properties of the estimators in econometric applications. We show that if T grows at the same rate as n, the fixed effects estimator is asymptotically biased, so that asymptotic confidence intervals are incorrect, but that they are correct for the panel jackknife. We show T growing faster than n^1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife.     

Testing stochastic orders in tails of contingency tables

Testing for the difference in the strength of bivariate association in two independent contingency tables is an important issue that finds applications in various disciplines. Currently, many of the commonly used tests are based on single-index measures of association. More specifically, one obtains single-index measurements of association from two tables and compares them based on asymptotic theory. Although they are usually easy to understand and use, often much of the information contained in the data is lost with single-index measures. Accordingly, they fail to fully capture the association in the data. To remedy this shortcoming, we introduce a new summary statistic measuring various types of association in a contingency table. Based on this new summary statistic, we propose a likelihood ratio test comparing the strength of association in two independent contingency tables. The proposed test examines the stochastic order between summary statistics. We derive its asymptotic null distribution and demonstrate that the least favorable distributions are chi-bar distributions. We numerically compare the power of the proposed test to that of the tests based on single-index measures. Finally, we provide two examples illustrating the new summary statistics and the related tests.     

LM Test of Neglected Correlated Random Effects and Its Application

This article aims at achieving two distinct goals. The first is to extend the existing LM test of overdispersion to the situation where the alternative hypothesis is characterized by the correlated random effects model. We obtain a result that the test against the random effects model has a certain max-min type optimality property. We will call such a test the LM test of overdispersion. The second goal of the article is to draw a connection between panel data analysis and the analysis of multiplicity of equilibrium in games. Because such multiplicity can be viewed as a particular form of neglected heterogeneity, we propose an intuitive specification test for a class of two-step game estimators.     

Australia Farewell: Predictors of Emigration in the 2000s

The factors leading individuals to immigrate to developed contexts are widely studied, but comparatively less is known about those who emigrate from them. In this paper, we use data from a nationally representative cohort of Australian adults to develop longitudinal measures of emigration and to assess how social ties and individual economic position predict emigration. Cox proportional hazards models indicate that the propensity to emigrate is particularly pronounced for those with relatively little social connectedness in Australia. Specifically, our results show that first-generation Australians, especially those with relatively short durations in the country, have substantially higher emigration rates than later-generation Australians. Similarly, having a partner with deeper generational roots in Australia strongly reduces the likelihood to emigrate. At the same time, our analysis also shows that economic position matters, with the not employed having higher risks of emigration. Perhaps most interestingly, estimates from our models reveal that those with university degrees are much more likely to emigrate than individuals with lower levels of education, a finding that is true for both first- and later-generation Australians.