We review recent likelihood-based approaches to modeling demand for medical care. A semi-nonparametric model along the lines of Cameron and Johansson's Poisson polynomial model, but using a negative binomial baseline model, is introduced. We apply these models, as well a semiparametric Poisson,...

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect...

Standard indirect Inference (II) estimators take a given finite-dimensional statistic, Z_{n} , and then estimate the parameters by matching the sample statistic with the model-implied population moment. We here propose a novel estimation method that utilizes all available information contained...

Consider a model with parameter phi, and an auxiliary model with parameter theta. Let phi be a randomly sampled from a given density over the known parameter space. Monte Carlo methods can be used to draw simulated data and compute the corresponding estimate of theta, say theta_tilde. A large...

This note describes ParallelKnoppix, a bootable CD that allows econometricians with average knowledge of computers to create and begin using a high performance computing cluster for parallel computing in very little time. The computers used may be heterogeneous machines, and clusters of up to...

This note describes ParallelKnoppix, a bootable CD that allows creationof a Linux cluster in very little time.An experienced user can create a cluster ready to execute MPI programsin less than 10 minutes.The computers used may be heterogeneous machines, of the IA-32 architecture.When the cluster...

This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of...

This document contains lecture notes for a first year graduate course in econometrics, with coverage of basic topics such as OLS and hypothesis testing, through maximum likelihood and GMM, nonlinear models, time series, panel data, Bayesian methods, simulation-based methods, and other topics....

PelicanHPC is a rapid (around 5 minutes, when you know what you're doing) means of setting up a high performance computing (HPC) cluster for parallel computing using MPI. This tutorial gives a basic description of what PelicanHPC does,  addresses how to use the released CD images to set up a...