Alternative approaches to evaluation in empirical microeconomics

This paper reviews a range of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching methods, instrumental variables, discontinuity design and control functions. It discusses the identification of both the traditionally used average parameters and more complex distributional parameters. In each case, the necessary assumptions and the data requirements are considered. The adequacy of each approach is discussed drawing on the empirical evidence from the education and labor market policy evaluation literature. We also develop an education evaluation model which we use to carry through the discussion of each alternative approach. A full set of STATA datasets are provided free online which contain Monte-Carlo replications of the various specifications of the education evaluation model. There are also a full set of STATA .do files for each of the estimation approaches described in the paper. The .do-files can be used together with the datasets to reproduce all the results in the paper.

Alternative Approaches to Evaluation in Empirical Microeconomics

This paper reviews a range of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching methods, instrumental variables, discontinuity design and control functions. It discusses the identification of both the traditionally used average parameters and more complex distributional parameters. In each case, the necessary assumptions and the data requirements are considered. The adequacy of each approach is discussed drawing on the empirical evidence from the education and labor market policy evaluation literature. We also develop an education evaluation model which we use to carry through the discussion of each alternative approach. A full set of STATA datasets are provided free online which contain Monte-Carlo replications of the various specifications of the education evaluation model. There are also a full set of STATA .do files for each of the estimation approaches described in the paper. The .do-files can be used together with the datasets to reproduce all the results in the paper.Evaluation methods, policy evaluation, matching methods, instrumental variables, social experiments, natural experiments, difference-in-differences, discontinuity design, control function.

Alternative Approaches to Evaluation in Empirical Microeconomics

This paper reviews some of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching, instrumental variables, discontinuity design, and control functions. It discusses identification of traditionally used average parameters and more complex distributional parameters. The adequacy, assumptions, and data requirements of each approach are discussed drawing on empirical evidence from the education and employment policy evaluation literature. A workhorse simulation model of education returns is used throughout the paper to discuss and illustrate each approach. The full set of STATA datasets and do-files are available free online and can be used to reproduce all estimation and simulation results.evaluation methods

Essays on Latent Variable Models and Roll Call Scaling

This dissertation comprises three essays on latent variable models and Bayesian statistical methods for the study of American legislative institutions and the more general problems of measurement and model comparison. In the first paper, I explore the dimensionality of latent variables in the context of roll call scaling. The dimensionality of ideal points is an aspect of roll call scaling which has received significant attention due to its impact on both substantive and spatial interpretations of estimates. I find that previous evidence for unidimensional ideal points is a product of the Scree procedure. I propose a new varying dimensions model of legislative voting and a corresponding Bayesian nonparametric estimation procedure (BPIRT) that allows for probabilistic inference on the number of dimensions. Using this approach, I show that there is strong evidence for multidimensional ideal points in the U.S. Congress and that using only a single dimension misses much of the disagreement that occurs within parties. I reexamine theories of U.S. legislative voting and find that empirical evidence for these models is conditional on unidimensionality. In the second paper, I expand on the varying dimensions model of legislative voting and explore the role of group dependencies in legislative voting. Assumptions about independence of observations in the scaling model ignore the possibility that members of the voting body have shared incentives to vote as a group and lead to problems in estimating ideal points and corresponding latent dimensions. I propose a new ideal point model, clustered beta process IRT (C-BPIRT), that explicitly allows for group contributions in the underlying spatial model of voting. I derive a corresponding empirical model that uses flexible Bayesian nonparametric priors to estimate group effects in ideal points and the corresponding dimensionality of the ideal points. I apply this model to the 107th U.S. House (2001 - 2003) and the 88th U.S. House (1963 - 1965) and show how modeling group dynamics improves the estimation and interpretation of ideal points. Similarly, I show that existing methods of ideal point estimation produce results that are substantively misaligned with historical studies of the U.S. Congress. In the third and final paper, I dive into the more general problem of Bayesian model comparison and marginal likelihood computation. Various methods of computing the marginal likelihood exist, such as importance sampling or variational methods, but they frequently provide inaccurate results. I demonstrate that point estimates for the marginal likelihood achieved using importance sampling are inaccurate in settings where the joint posterior is skewed. I propose a light extension to the variational method that treats the marginal likelihood as a random variable and create a set of intervals on the marginal likelihood which do not share the same inaccuracies. I show that these new intervals, called kappa bounds, provide a computationally efficient and accurate way to estimate the marginal likelihood under arbitrarily complex Bayesian model specifications. I show the superiority of kappa bounds estimates of the marginal likelihood through a series of simulated and real-world data examples, including comparing measurement models that estimate latent variables from ordered discrete survey data.PHDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163023/1/kamcal_1.pd

Essays on the Bayesian inequality restricted estimation

Bayesian estimation has gained ground after Markov Chain Monte Carlo process made it possible to sample from exact posterior distributions. This research aims at contributing to the ongoing debate about the relative virtues of the Frequentist and Bayesian theories by concentrating on the qualitative dependent variable models. Two Markov Chain Monte Carlo (MCMC) methods have been used throughout this dissertation to facilitate Bayesian estimation, namely Gibbs (1984) sampling and the Metropolis (1953, 1970) Algorithm. In this research, several Monte Carlo experiments have been carried out to better understand the finite sample properties of Bayesian estimator and its relative performance to Maximum Likelihood Estimation (MLE) in probit and poisson models. In addition, the performance of the estimators is compared when inequality restrictions are imposed on the coefficients of the models. The restrictions are imposed within the context of a Monte Carlo experiment for the probit model and applied to the real data in the poisson regression framework. The research demonstrates the ease with which the inequality restrictions on the coefficients of the probit and poisson models via the Gibbs sampler and Metropolis Algorithms, respectively. It has been shown throughout the research that sample size has the largest impact on the risk of the parameters in both techniques. Bayesian estimation is very sensitive to prior specification even in the case of non-informative priors. Lowering the variance of the non-informative prior improves the Bayesian estimation, without significantly changing the nature of the distribution. In the cases where Bayesian prior variance is very large, MLE dominates the Bayesian in the almost all of the experimental designs. Whereas, when the prior variance is lowered, the improvement in the estimation process is remarkable. In the constrained cases, the Bayesian estimator has lower variance and lower MSE when the restrictions are correct. As the specification error increases, the Bayesian estimator suffers more than the MLE. The increase in bias is more than the efficiency gain for the Bayesian case. The effects of changes such as the changes in the distribution of regressors, parameter values, collinearity, and their interactions warrant more investigation

tobit models in strategy research critical issues and applications

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Tobit models in strategy research:Critical issues and applications

Research Summary: Tobit models have been used to address several questions in management research. Reviewing existing practices and applications, we discuss three challenges: (a) assumptions about the nature of data, (b) apparent interchangeability between censoring and selection bias, and (c) potential violations of key assumptions in the distribution of residuals. Empirically analyzing the relationship between import competition and industry diversification, we contrast Tobit models with results from other estimators and show the conditions that make Tobit a suitable empirical approach. Finally, we offer suggestions and guidelines on how to use Tobit models to deal with censored data in strategy research. Managerial Summary: Data on strategic decisions often exhibit certain features, such as excess zeros and values bounded within a given range, which complicate the use of linear econometric techniques. Deriving statistical evidence in such instances may suffer from biases that undermine managerial applications. Our study presents an extensive comparison of different econometric models to deal with censored data in strategic management showing the strengths and weaknesses of each model. We also conduct an application to the context of import penetration and industry diversification to highlight how the relationship between these two variables changes depending on the econometric model used for the analysis. In conclusion, we provide a set of recommendations for scholars interested in censored data

A Bayesian approach to Hybrid Choice models

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2010-2011Les modèles microéconométriques de choix discrets ont pour but d’expliquer le processus du choix individuel des consommateurs parmi un ensemble limité et exhaustive d’options mutuellement exclusives. Les modèles dits de choix hybrides sont une généralisation des modèles de choix discrets standard, où des modèles indépendants plus sophistiqués sont considérés simultanément. Dans cette thèse des techniques d’estimation simultanée sont analysées et appliquées pour un modèle de choix hybride qui, sous la forme d’un système complexe d’équations structurelles généralisées, intègre à la fois des choix discrets et des variables latentes en tant que facteurs explicatifs des processus décisionnels. Ce qui motive l’étude de ce genre de modèles est que pour comprendre le processus du choix il faut incorporer des attitudes, des perceptions et des attributs qualitatifs à l’intérieur de modèles décisionnels économiques conventionnels, tout en prenant ce qui dit la recherche en sciences cognitives ainsi qu’en psychologie sociale. Quoique l’estimation du système d’équations d’un modèle de choix hybride requière l’évaluation d’intégrales multidimensionnelles complexes, on résoudre empiriquement ce problème en applicant la méthode du maximum de vraisemblance simulée. Ensuite on dérive une procédure d’échantillonnage de Gibbs pour l’estimation simultanée bayésienne du modèle qui offre des estimateurs convergents et efficaces. Ceci devient une méthode plus avantageuse comparativement aux méthodes classiques dans un cadre analytique avec un grand nombre de variables latentes. En effet, en vertu de l’approche bayésienne il suffit de considérer des régressions ordinaires pour les variables latentes. Par ailleurs, dériver les intervalles de confiance bayésiennes pour les parts de marché ainsi que pour des dispositions à payer devient trivial. De par sa grande géneralité, le modèle de choix hybride est capable de s’adapter à des situations pratiques. En particulier, la réponse des consommateurs suite à l’innovation technologique est analysée. Par exemple, on étudie les préférences pro-environnementales dans un modèle économique des décisions d’achat de véhicules verts selon lequel les consommateurs soucieux de l’environnement sont prêts à payer davantage pour des véhicules à faibles émissions, en dépit des inconvénients potentiels. En outre, en utilisant un noyau probit et des indicateurs dichotomiques on montre que des connaissances préalables ainsi que des attitudes positives envers l’adoption de nouvelles technologies favorisent l’adoption de la téléphonie IP.Microeconometric discrete choice models aim to explain the process of individual choice by consumers among a mutually exclusive, exhaustive and finite group of alternatives. Hybrid choice models are a generalization of standard discrete choice models where independent expanded models are considered simultaneously. In my dissertation I analyze, implement, and apply simultaneous estimation techniques for a hybrid choice model that, in the form of a complex generalized structural equation model, simultaneously integrates discrete choice and latent explanatory variables, such as attitudes and qualitative attributes. The motivation behind hybrid choice models is that the key to understanding choice comes through incorporating attitudinal and perceptual data to conventional economic models of decision making, taking elements from cognitive science and social psychology. The Bayesian Gibbs sampler I derive for simultaneous estimation of hybrid choice models offers a consistent and efficient estimator that outperforms frequentist full information simulated maximum likelihood. Whereas the frequentist estimator becomes fairly complex in situations with a large choice set of interdependent alternatives with a large number of latent variables, the inclusion of latent variables in the Bayesian approach translates into adding independent ordinary regressions. I also find that when using the Bayesian estimates it is easier to consider behavioral uncertainty; in fact, I show that forecasting and deriving confidence intervals for willingness to pay measures is straightforward. Finally, I confirm the capacity of hybrid choice modeling to adapt to practical situations. In particular, I analyze consumer response to innovation. For instance, I incorporate proenvironmental preferences toward low-emission vehicles into an economic model of purchase behavior where environmentally-conscious consumers are willing to pay more for sustainable solutions despite potential drawbacks. In addition, using a probit kernel and dichotomous effect indicators I show that knowledge as well as a positive attitude toward the adoption of new technologies favor the adoption of IP telephony