Jason R Blevins

The Ohio State University | OSU · Department of Economics

This paper develops a dynamic model of retail competition and uses it to study the impact of the expansion of a new national competitor on the structure of urban markets. In order to accommodate substantial heterogeneity (both observed and unobserved) across agents and markets, the paper first develops a general framework for estimating and solving...

This paper revisits the question of parameter identification when a linear continuous time model is sampled only at equispaced points in time. Following the framework and assumptions of Phillips (1973), we consider models characterized by first-order, linear systems of stochastic differential equations and use a priori restrictions on the model par...

This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on s...

Continuous-time formulations of dynamic discrete choice games offer notable computational advantages, particularly in modeling strategic interactions in oligopolistic markets. This paper extends these benefits by addressing computational challenges in order to improve model solution and estimation. We first establish new results on the rates of con...

We introduce a sequential estimator for continuous time dynamic discrete choice models (single-agent models and games) by adapting the nested pseudo likelihood (NPL) estimator of Aguirregabiria and Mira (2002, 2007), developed for discrete time models with discrete time data, to the continuous time case with data sampled either discretely (i.e., un...

We propose a new sequential Efficient Pseudo-Likelihood (EPL) estimator for structural economic models with an equality constraint, particularly dynamic discrete choice games of incomplete information. Each iteration in the EPL sequence is consistent and asymptotically efficient, and iterating to convergence improves finite sample performance. For...

This paper develops a dynamic model of consumer search that, despite placing very little structure on the dynamic problem faced by consumers, allows us to exploit intertemporal variation in within-period price and search cost distributions to estimate the population distribution from which consumers' search costs are initially drawn. We show that s...

We develop and estimate a dynamic game of strategic firm expansion and contraction decisions to study the role of firm size in future profitability and market dominance. Modeling firm size is important because retail chain dynamics are more richly driven by expansion and contraction than de novo entry or permanent exit. Additionally, anticipated si...

We develop and estimate a dynamic game of strategic firm expansion and contraction decisions to study the role of firm size on future profitability and market dominance. Modeling firm size is important because retail chain dynamics are more richly driven by expansion and contraction than de novo entry or permanent exit. Additionally, anticipated si...

In models of strategic interaction, there may be important order of entry effects if one player can credibly commit to an action (e.g., entry) before other players. If one estimates a simultaneous-move model, then the move-order effects will be confounded with the payoffs. This paper considers nonparametric identification and simulation-based estim...

This paper establishes consistency and non-standard rates of convergence for set estimators based on contour sets of criterion functions for a semiparametric binary response model under a conditional median restriction. The model may be partially identified due to potentially limited-support regressors. A set estimator analogous to the maximum scor...

This paper establishes conditions for nonparametric identification of dynamic optimization models in which agents make both discrete and continuous choices. We consider identification of both the payoff function and the distribution of unobservables. Models of this kind are prevalent in applied microeconomics and many of the required conditions are...

In this article, we consider two recently proposed semiparametric estimators for distribution-free binary response models under a conditional median restriction. We show that these estimators can be implemented in Stata by using the nl command through simple modifications to the nonlinear least-squares probit criterion function. We then introduce d...

In this paper, nonlinear least squares (NLLS) estimators are proposed for semiparametric binary response models under conditional median restrictions. The estimators can be identical to NLLS procedures for parametric binary response models (e.g. Probit), and consequently have the advantage of being easily implementable using standard software packa...

Many semiparametric fixed effects panel data models, such as binary choice models and duration models, are known to be point identified when at least one regressor has full support on the real line. It is common in practice, however, to have only discrete or continuous but possibly bounded regressors. This paper addresses identification, estimation...

This paper develops a dynamic model of retail competition and uses it to study the impact of the expansion of a new national competitor on the structure of urban markets. In order to accommodate substantial heterogeneity (both observed and unobserved) across agents and markets, the paper first develops a general framework for estimating and solving...

This paper develops a standard conforming generic linked list in Fortran 95 which is capable of storing data of an any type. The list is implemented using the transfer intrinsic function, and although the interface is generic, it remains relatively simple and minimizes the potential for error. Although linked lists are the focus of this paper, the...

Clustering methods for categorical data can easily have ties. These ties occur when an ambiguity arises in the process of executing an algo-rithm. This paper identifies two types of ties and studies their effect on the k-modes method for categorical data. Three variants of the k-modes algorithm, each of which handles tie breaking and stopping crite...

Three example variants of the k-modes algorithm are compared as tools to illustrate the eects of ties on convergence of any k-modes like algorithm. Two types of ties are discussed as well as their aect on the convergence of the 3 variants. These consequences of resolving these ties are shown to greatly aect speed of convergence and quality of resul...

The centroid decomposition (CD) is an approximate singular value decomposi- tion (SVD) with applications in factor analysis and latent semantic indexing (LSI). This paper presents updating methods for the centroid decomposition based on recent work in SVD updating methods. A general rank-1 updating framework is developed first and then more specifi...

Categorical data can easily have ties. This paper identifies two types of ties and studies their effect on the k-modes methods for categorical data. Three commonly used variants of the k-modes algorithm, each of which handles tie breaking and stopping criterion differently, are compared. It is shown via simple yet subtly constructed examples that t...

This dissertation consists of three chapters relating to identification and inference in dynamic microeconometric models including dynamic discrete games with many players, dynamic games with discrete and continuous choices, and semiparametric binary choice and duration panel data models. The first chapter provides a framework for estimating l...