When considering multiple hypothesis tests simultaneously, standard statistical techniques will lead to over-rejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this paper we discuss the Romano-Wolf multiple hypothesis correction, and...

A large number of exact inferential procedures in statistics and econometrics involve the sampling distribution of ratios of random variables. If the denominator variable is positive, then tail probabilities of the ratio can be expressed as those of a suitably defined difference of random...

This paper analyzes the higher-order properties of nested pseudo-likelihood (NPL) estimators and their practical implementation for parametric discrete Markov decision models in which the probability distribution is defined as a fixed point. We propose a new NPL estimator that can achieve...

This note discusses some problems possibly arising when approximating via MonteCarlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to reestimate unknown parameters on each simulated Monte-Carlo sample - and...

This paper introduces a method for simulating multivariate samples that have exact means, covariances, skewness and kurtosis. A new class of rectangular orthogonal matrices is fundamental to the methodology, and these "L-matrices'' can be deterministic, parametric or data specific in nature. The...

We develop a simulation algorithm that generates multivariate samples with exact means, covariances, and multivariate skewness. If required for financial applications, absence of arbitrage can be ensured. Potential applications include the simulation of risk factors for the risk management of...

This paper proposes new estimation techniques for gravity models with zero trade values and heteroscedasticity. We propose various Heckman estimators with different distributions of the residuals, nonlinear forms of both selection and measure equations, and various process of the variance. We...

We analyze the Large Deviation Probability (LDP) of linear factor models generated from non-identically distributed components with regularly-varying tails, a large subclass of heavy tailed distributions. An efficient sampling method for LDP estimation of this class is introduced and...

A data-cloning SMC² method is proposed as a general purpose optimization routine for estimating latent variable models by maximum likelihood. The latent variables are first marginalized out by SMC at any fixed parameter value, and the model parameters are then estimated by density tempered SMC....

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...