This paper surveys recently developed approaches to analyzing panel data with nonlinear models. We summarize a number of results on estimation of fixed and random effects models in nonlinear modeling frameworks such as discrete choice, count data, duration, censored data, sample selection,...

In this poper we present a consistent spacification test of a parametric regression function against a general nonparametric alternative. The proposed test is based on wavelet estimation ant it is shown to have similar rates of convergence to the more commonly used kernel based tests.

The authors examine the Bank of Canada's overnight rate as a measure of monetary policy in vector autoregression (VAR) models.

We discuss the normal vairance-mean mixture model from a semi-parametric point of view, i.e. we let the mixing distribution belong to a non parametric family. The main results are consistency of the non parametric maximum likelihood estimator in this case, and construction of an asymptotically...

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. Using the quantum Monte Carlo (QMC) algorithm, we are the first to study whether quantum computing can improve the run time of economic applications and challenges in doing so....

The purpose of this study is to investigate the effect of time-aggregation in discrete and contunous-time hazard models. A Monte Carlo study is conducted in which data are generated according to underlying contunous and discrete-time processes, which data are aggregated into daily, weekly and...

Agent based models take into account limited rational behaviour of individuals acting on financial markets. Explicit simulation of this behaviour and the resulting interaction of individuals provide a description of aggregate financial market time series. Al-though the outcomes of such...

Hidden Markov models is an extension of mixture models providing a flexible class of models exhibiting dependence and a possibly large degree of variability. In this paper the authors show how jump Markov chain Monte Carlo techniques can be used to estimate the parameters as well as the number...

This paper presents a new simulated maximum-likelihood method that rests on estimating the likelihood nonparametrically on a simulated sample. We prove that this method, which can be used on very general models, is consistent and asymptotically efficient.

Models use for natural resources prices usually preclude the possibility of large changes (jumps) resulting from discrete, unexpected events. To test for the presence of jumps and ARCH effects, we propose to use bounds and bootstrap test techniques, thus solving the unidentified nuisance...