User-Friendly Parallel Computations with Econometric Examples

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 parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.parallel computing, Monte Carlo, bootstrapping,maximum likelihood, GMM, kernel regression

Gender Differences in Survival in Idiopathic Pulmonary Fibrosis and Following Lung Transplant

Idiopathic Pulmonary Fibrosis (IPF) is a chronic and progressive form of interstitial lung disease characterized by inflammation and abnormal tissue repair ultimately leading to decreased pulmonary function and death. Risk factors for IPF are largely unknown and medical treatment offers a poor prognosis due to the lack of effective treatment options. Survival outcomes were analyzed for a cohort of 331 patients. The median age at clinical evaluation for IPF was 69 years. Subjects survived an average of 21.82 months after diagnosis, with a higher survival in females than in males. Males had a risk 2.85 times higher than females of death. Subjects older than 69 years of age had a relative risk of dying of 1.6 in comparison to subjects younger than 69 years. Predictors of survival after lung transplant were also analyzed in a cohort of 990 lung transplanted patients. The overall survival was 41.6%, (41.5 % in males, and 41.8 % in females), the average length of the follow up was 45.84 plus or minus 51.98 months (range 0 to 282.47 months). Females tend to live longer than males: 50.75 plus or minus 55.41 months versus 40.64 plus or minus 47.60 months, respectively. Males had a risk of dying during the follow up that was 1.18 (95% CI 1.01-1.40) relative to females, after adjusting for ethnicity, age, smoking status, diagnosis and donor characteristics. Females who had at least one full term pregnancy during their life had better survival rates than females who had no full term pregnancies.Our results of a better survival after lung transplant in females (particularly females with at least one pregnancy) support the hypothesis of a hormonal contribution to survival and of the development of immunotolerance after pregnancy.The public health significance includes the use of the current study as a model in understanding the role of immunity in cancer development. The age-adjusted incidence rate is 555.8 per 100,000 men and 411.3 per 100,000 women per year (2000-2004), and the combined lifetime risk of cancer is approximately 1 in 2. Thus, any further understanding of cancer causes would be worthwhile in cancer prevention and treatment efforts

A Note on Parallelizing the Parameterized Expectations Algorithm

The parameterized expectations algorithm (PEA) involves a long simulationand a nonlinear least squares (NLS) fit, both embedded in a loop. Both steps are natural candidates for parallelization.This note shows that parallelization can lead to important speedups forthe PEA.I provide example code for a simple model that can serve as a templatefor parallelization of more interesting models, as well as a download linkfor an image of a bootable CD that allows creation of a cluster and executionof the example code in minutes, with no need to install any software.parameterized expectations, parallel computing

A Data Mining Approach to Indirect Inference

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 set of tuples (phi, theta_tilde) can be generated in this manner. Nonparametric methods may be use to fit the function E(phi|theta_tilde=a), using these tuples. It is proposed to estimate phi using the fitted E(phi|theta_tilde=theta_hat), where theta_hat is the auxiliary estimate, using the real sample data. This is a consistent and asymptotically normally distributed estimator, under certain assumptions. Monte Carlo results for dynamic panel data and vector autoregressions show that this estimator can have very attractive small sample properties. Confidence intervals can be constructed using the quantiles of the phi for which theta_tilde is close to theta_hat. Such confidence intervals are found to have very accurate coverage.simulation-based estimation; data mining; dynamic panel data; vector autoregression; bias reduction Abstract JEL codes: C13, C14, C15, C33

Unconstrained Optimization with MINTOOLKIT for GNU Octave

The paper documents MINTOOLKIT for GNU Octave. MINTOOLKIT provides functions for minimization and numeric differentiation. The main algorithms are BFGS, LBFGS, and simulated annealing. Examples are given.minimization, optmization, software

Modeling Usage of Medical Care Services: The Medical Expenditure Panel Survey Data, 1996-2000

We explore the determinants of usage of six different types of health careservices, using the Medical Expenditure Panel Survey data, years 1996-2000.We apply a number of models for univariate count data, including semiparametric, semi-nonparametric and finite mixture models.We find that the complexity of the model that is required to fit the datawell depends upon the way in which the data is pooled across sexes andover time, and upon the characteristics of the usage measure.Pooling across time and sexes is almost always favored, but when more heterogeneous data is pooled it is often the case that a more complex statisticalmodel is required.medical care; count data; maximum likelihood

Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation

Indirect likelihood inference

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 likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.indirect inference; maximum-likelihood; simulation-based

SNM Guide

This is a guide that explains how to use software that implements the simulated nonparametric moments (SNM) estimator proposed by Creel and Kristensen (2009). The guide shows how results of that paper may easily be replicated, and explains how to install and use the software for estimation of simulable econometric models.econometric software; dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation

The black-white test score gap widens with age?

Abstract We re-examine the theoretical concept of a production function for cognitive achievement, and argue that an indirect production function that depends upon the variables that constrain parents' choices is both more tractable from an econometric point of view, and more interesting from an economic point of view than is a direct production function that depends upon a detailed list of direct inputs such as number of books in the household. We estimate flexible econometric models of indirect production functions for two achievement measures from the Woodcock-Johnson Revised battery, using data from two waves of the Child Development Supplement to the PSID. Elasticities of achievement measures with respect to family income and parents' educational levels are positive and significant. Gaps between scores of black and white children narrow or remain constant as children grow older, a result that differs from previous findings in the literature. The elasticities of achievement scores with respect to family income are substantially higher for children of black families, and there are some notable difference in elasticities with respect to parents' educational levels across blacks and whites.education; cognitive achievement; test score gaps