In this study, we consider a simple conditional quantile estimator in a nonparametric framework with time series data. We prove the consistency and asymptotic normality of our simple estimator for absolutely regular processes ( β -mixing). This simple estimator can get better finite sample...

Randomization inference provides exact finite sample tests of sharp nulls which precisely specify the outcomes associated with randomized treatment. A growing literature shows that when based upon studentized statistics these same tests, despite incorrectly assuming an absence of heterogeneity,...

This paper studies dynamic programming for quantile preference models, in which the agent maximizes the stream of the future τ-quantile utilities, for τ ∈ (0,1). We suggest numerical methods, based on value function iterations, for solving the quantile recursive dynamic programming, and...

It is well known that when the empirical skewness of the OLS residuals display the opposite sign of what is expected from the stochastic frontier model, maximum likelihood estimation will be equivalent to OLS and no inefficiency will be recovered. A variety of approaches to operate in this...

A moderated regression model ( MRM ) suffers from the correlations of the interaction effect with the main effects. The interaction effect is invariant, but the main effects are not invariant in the MRM and its transformed models. Thus, it is common to examine only the interaction effect. This...

This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with regime switching in the loadings. The model parameters are estimated jointly by EM algorithm, which in the current context only requires iteratively calculating regime probabilities and principal...

Many studies have documented the missing relationship between PCE inflation and the labor market slack in the last 20 years. This paper first presents evidence of a strong sensitivity of price inflation to labor market tightness once we remove the late 90s from our sample and account for the...

This study proposes a Bayesian approach for exact finite-sample inference of an instrument-free estimation method that builds upon joint estimation using copulas to deal with endogenous covariates. Although copula approaches with applications to handle regressor-endogeneity have been frequently...

Typically, the error term in stochastic frontier models is assumed to be positively skewed; however, efficiency scores are biased if this assumption is violated. This paper considers the case in which also negative skewness is allowed in the model. In contrast to other approaches, this skewness is...

Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In...