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Estimating Models (estimating + models)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	60%

Selected Abstracts

EXTENDING SOCIAL DISORGANIZATION THEORY: MODELING THE RELATIONSHIPS BETWEEN COHESION, DISORDER, AND FEAR,

CRIMINOLOGY, Issue 2 2001
FRED E. MARKOWITZ
In this study, we build on recent social disorganization research, estimating models of the relationships between disorder, burglary, cohesion, and fear of crime using a sample of neighborhoods from three waves of the British Crime Survey. The results indicate that disorder has an indirect effect on burglary through fear and neighborhood cohesion. Although cohesion reduces disorder, nonrecursive models show that disorder also reduces cohesion. Part of the effect of disorder on cohesion is mediated by fear. Similar results are obtained in nonrecursive burglary models. Together, the results suggest a feedback loop in which decreases in neighborhood cohesion increase crime and disorder, increasing fear, in turn, further decreasing cohesion. [source]

Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors

ECONOMETRICA, Issue 4 2005
Joseph G. Altonji
We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning. The second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables. An external variable is used to impose an equality restriction, at two points of support, on the conditional distribution of the unobservable random term given the regressor and the external variable. Our methods apply to cross sections, but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group. [source]

Empirical Likelihood-Based Inference in Conditional Moment Restriction Models

ECONOMETRICA, Issue 6 2004
Yuichi Kitamura
This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples. [source]

Measuring state dependence in individual poverty histories when there is feedback to employment status and household composition

JOURNAL OF APPLIED ECONOMETRICS, Issue 7 2009
Martin Biewen
This paper argues that the assumption of strict exogeneity, which is usually invoked in estimating models of state dependence with unobserved heterogeneity, is violated in the poverty context as important variables determining contemporaneous poverty status, in particular employment status and household composition, are likely to be influenced by past poverty outcomes. Therefore, a model of state dependence is developed that explicitly allows for possible feedback effects from past poverty to future employment and household composition outcomes. Empirical results based on data from the German Socio-Economic Panel (GSOEP) suggest that there are indeed such feedback effects and that failure to take them into account may lead to biased estimates of the state dependence effect. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Correcting for Survey Misreports Using Auxiliary Information with an Application to Estimating Turnout

AMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 3 2010
Jonathan N. Katz
Misreporting is a problem that plagues researchers who use survey data. In this article, we develop a parametric model that corrects for misclassified binary responses using information on the misreporting patterns obtained from auxiliary data sources. The model is implemented within the Bayesian framework via Markov Chain Monte Carlo (MCMC) methods and can be easily extended to address other problems exhibited by survey data, such as missing response and/or covariate values. While the model is fully general, we illustrate its application in the context of estimating models of turnout using data from the American National Elections Studies. [source]