Methods

Choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects.

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series.

We introduce a class of quantile regression estimators for short panels.

We analyze identication of nonseparable models under three kinds of exogeneity assumptions weaker than full statistical independence. The first is b

This paper develops and implements a nonparametric test of Random Utility Models. The motivating application is to test the null hypothesis that a sample of cross-sectional demand distributions was generated by a population of rational consumers.

Researchers often rely on the t-statistic to make inference on parameters in statistical models. It is common practice to obtain critical values by si

This paper considers two-sided tests for the parameter of an endogenous variable in an instrumental variable (IV) model with heteroskedastic and autocorrelated errors. We develop the finite-sample theory of weighted-average power (WAP) tests with normal errors and a known long-run variance.

This paper first aims to reinvestigate the issue of US fiscal sustainability by using the quantile cointegration approach proposed by Xiao (2009 and 2012). Our empirical evidence indicates a quantile-dependent cointegrating relationship between government expenditures and revenues. In addition, this paper examines the long-run causality relationship between expenditures and revenues by using the vector error-correction (VEC) model with coefficients based on the different quantiles. Findings from the long-run Granger-causality analyses support the spend-and-tax hypothesis. Our investigation suggests that the government should show more discretion in increasing expenditures in the long run. Moreover, budget deficit reduction can only be achieved through reductions in government expenditures.

We propose a notion of conditional vector quantile function and a vector quantile regression.

The goal of this article is to develop formal tests to evaluate the relative in-sample performance of two competing, misspecified, nonnested models in the presence of possible data instability.

The paper considers nonparametric estimation of absolutely continuous distribution functions of independent lifetimes of non-identical components in k-out-of-n systems, 2 ≤ k ≤ n, from the observed “autopsy” data.

We consider a Kronecker product structure for large covariance matrices, which has the feature that the number of free parameters increases logarithmically with the dimensions of the matrix. We propose an estimation method of the free parameters based on the log linear property of this structure, and also a Quasi-Likelihood method. We establish the rate of convergence of the estimated parameters when the size of the matrix diverges. We also establish a CLT for our method. We apply the method to portfolio choice for S&P500 daily returns and compare with sample covariance based methods and with the recent Fan et al. (2013) method.

Individual heterogeneity is an important source of variation in demand. Allowing for general heterogeneity is needed for correct welfare comparisons. We consider general heterogeneous demand where preferences and linear budget sets are statistically independent.

This paper studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve "balance" within each stratum. Such schemes include, for example, Efron's biased-coin design and stratied block randomization. When testing the null hypothesis that the average treatment effect equals a pre-specified value in such settings, we first show that the usual two-sample t-test is conservative in the sense that it has limiting rejection probability under the null hypothesis no greater than and typically strictly less than the nominal level. In a simulation study, we find that the rejection probability may in fact be dramatically less than the nominal level. We show further that these same conclusions remain true for a naïve permutation test, but that a modified version of the permutation test yields a test that is non-conservative in the sense that its limiting rejection probability under the null hypothesis equals the nominal level for a wide variety of randomization schemes. The modified version of the permutation test has the additional advantage that it has rejection probability exactly equal to the nominal level for some distributions satisfying the null hypothesis and some randomization schemes. Finally, we show that the usual t-test (on the coefficient on treatment assignment) in a linear regression of outcomes on treatment assignment and indicators for each of the strata yields a non-conservative test as well under even weaker assumptions on the randomization scheme. In a simulation study, we find that the non-conservative tests have substantially greater power than the usual two-sample t-test.

In this paper we propose a novel method to construct confidence intervals in a class of linear inverse problems.

We present a novel experimental design to study social learning in the laboratory.

This paper examines the asymptotic behavior of the posterior distribution of a possibly nondifferentiable function g(theta), where theta is a finite dimensional parameter.

We derive fixed effects estimators of parameters and average partial effects in (possibly dynamic) nonlinear panel data models with individual and time effects.

We derive strong approximations to the supremum of the non-centered empirical process indexed by a possibly unbounded VC-type class of functions by the suprema of the Gaussian and bootstrap processes.

This paper considers identifcation of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Because treated and control units drop out at different rates, randomization only ensures the comparability of treatment and controls at the time of randomization, so that long run average treatment effects are not point identified. Instead we derive informative bounds on these average treatment effects. Our bounds do not impose (semi)parametric restrictions, as e.g. proportional hazards. We also explore various assumptions such as monotone treatment response, common shocks and positively correlated outcomes that tighten the bounds.