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29
2007), "Linear Regression Estimation of Discrete Choice Models with Nonparametric Distributions or Random Coe¢ cients
 AEA Papers and Proceedings
"... Random coefficient discrete choice models are a popular method for estimating demand in differentiated product markets. We introduce a computationally simple estimator that uses linear regression to estimate the distribution of random coefficients. The estimator is nonparametric for the distributio ..."
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Cited by 21 (4 self)
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Random coefficient discrete choice models are a popular method for estimating demand in differentiated product markets. We introduce a computationally simple estimator that uses linear regression to estimate the distribution of random coefficients. The estimator is nonparametric for the distribution of the random coefficients. We compare our estimator to several alternatives in a Monte Carlo exercise, and find the estimator predicts outofsample market shares well. We discuss extensions to panel data and dynamic programming.
A Simple Nonparametric Estimator for the Distribution of Random Coefficients in Discrete Choice Models
, 2008
"... We propose an estimator for discrete choice models, such as the logit, with a nonparametric distribution of random coefficients. The estimator is linear regression subject to linear inequality constraints and is robust, simple to program and quick to compute compared to alternative estimators for mi ..."
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Cited by 20 (3 self)
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We propose an estimator for discrete choice models, such as the logit, with a nonparametric distribution of random coefficients. The estimator is linear regression subject to linear inequality constraints and is robust, simple to program and quick to compute compared to alternative estimators for mixture models. We discuss three methods for proving identification of the distribution of heterogeneity for any given economic model. We prove the identification of the logit mixtures model, which, surprisingly given the wide use of this model over the last 30 years, is a new result. We also derive our estimator’s nonstandard asymptotic distribution and demonstrate its excellent small sample properties in a Monte Carlo. The estimator we propose can be extended to allow for endogenous prices. The estimator can also be used to reduce the computational burden of nested fixed point methods for complex models like dynamic programming discrete choice.
Tariff Choice with Consumer Learning and Switching Costs
, 2010
"... Consumers choosing fixedrate contracts tend to have insufficient usage to warrant the cost, particularly for new products. We propose and estimate a Bayesian learning model of tariff and usage choice that explains this “flatrate bias” without relying on behavioral misjudgments or tariffspecific p ..."
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Cited by 18 (0 self)
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Consumers choosing fixedrate contracts tend to have insufficient usage to warrant the cost, particularly for new products. We propose and estimate a Bayesian learning model of tariff and usage choice that explains this “flatrate bias” without relying on behavioral misjudgments or tariffspecific preferences. Consumers face both idiosyncratic and aggregate uncertainty since utility varies across consumers and the mean is unknown. Aggregate uncertainty inflates prior variances causing consumers to heavily weight private signals. Consumers with high posteriors are therefore overly optimistic. Although posteriors are unbiased across products, this productlevel bias conditional on one’s posterior (i.e., “conditional bias”) arises for each product, thereby explaining the flatrate bias. The consequences of conditional bias are exacerbated by switching costs that deter consumers from changing tariffs after updating their beliefs. We demonstrate the effects of uncertainty and switching costs on tariff choices and assess the pricing implications. We find tariff menus are ineffective screening devices for price discrimination by an online grocer. Predicted revenues increase by 20 percent when the peruse tariff is dropped.
A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables
, 2009
"... This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample p ..."
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Cited by 16 (4 self)
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This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and race effects, and much stronger effects of education, young children and husband’s income on female labor supply decisions.
Do frequency reward programs create switching costs. Research Paper No.1941. Standford Graduate School of Business
, 2006
"... This paper examines a common assertion that customers in reward programs become “locked in ” as they accumulate credits toward earning a reward. We define a measure of switching costs and use a dynamic structural model of demand in a reward program to illustrate that frequent customers ’ purchase in ..."
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Cited by 14 (0 self)
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This paper examines a common assertion that customers in reward programs become “locked in ” as they accumulate credits toward earning a reward. We define a measure of switching costs and use a dynamic structural model of demand in a reward program to illustrate that frequent customers ’ purchase incentives are practically invariant to the number of credits. In our empirical example, these customers comprise over eighty percent of all rewards and over twothirds of all purchases. Less frequent customers may face substantial switching costs when close to a reward, but rarely reach this state.
General DirectionofArrival Tracking with Acoustic Nodes
 IEEE Trans. on Signal Processing
, 2005
"... Traditionally in target tracking, much emphasis is put on the motion model that realistically represents the target's movements. In this paper, we first present the classical constant velocity model and then introduce a new model that incorporates an acceleration component along the heading d ..."
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Cited by 12 (11 self)
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Traditionally in target tracking, much emphasis is put on the motion model that realistically represents the target's movements. In this paper, we first present the classical constant velocity model and then introduce a new model that incorporates an acceleration component along the heading direction of the target. We also show that the target motion parameters can be considered part of a more general feature set for target tracking. This is exemplified by showing that target frequencies, which may be unrelated to the target motion, can also be used to improve the tracking performance. In order to include the frequency variable, a new array steering vector is presented for the directionofarrival (DOA) estimation problems. The independent partition particle filter (IPPF) is used to compare the performances of the two motion models by tracking multiple maneuvering targets using the acoustic sensor outputs directly.
Game theory and econometrics: A survey of some recent research
 Advances in Economics and Econometrics
, 2013
"... Abstract We survey an emerging literature on the econometric analysis of static and dynamic models of strategic interactions. Econometric methods of identification and estimation allow researcher to make use of observed data on individual choice behavior and on the conditional transition distributi ..."
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Cited by 8 (2 self)
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Abstract We survey an emerging literature on the econometric analysis of static and dynamic models of strategic interactions. Econometric methods of identification and estimation allow researcher to make use of observed data on individual choice behavior and on the conditional transition distribution of state variables to recover the underlying structural parameters of payoff functions and discount rates nonparametrically without imposing strong functional form assumptions. We also discuss the progress that the literature has made on understanding the role of unobserved heterogeneity in the estimation analysis of these models, and other related issues.
Estimating a Dynamic Oligopolistic Game with Serially Correlated Unobserved Production Costs
"... We propose a likelihood based method that relies on sequential importance sampling to estimate dynamic discrete games of complete information with serially correlated unobserved state variables. Our method is applicable to similar games that have a Markovian representation of the latent dynamics and ..."
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Cited by 8 (1 self)
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We propose a likelihood based method that relies on sequential importance sampling to estimate dynamic discrete games of complete information with serially correlated unobserved state variables. Our method is applicable to similar games that have a Markovian representation of the latent dynamics and an algorithm to solve the game. We apply the method to a dynamic oligopolistic game of entry for the generic pharmaceutical industry in which the production costs of firms are the serially correlated unobserved state variable. Costs evolve dynamically and endogenously in response to past entry decisions, leading to heterogeneity among firms regardless of whether they are ex ante identical or heterogeneous. We find that there are significant spillovers of entry on costs. Each entry on average reduces costs by 7 % at the next market opportunity; the average annual cumulative reduction is 51%. Our results provide evidence on the dynamic spillover effects of industry experience on subsequent market performance. The dynamic evolution of production cost plays an important role in the equilibrium path of the structure of the generic pharmaceutical industry.
Consumer Learning, Habit Formation and Heterogeneity: A Structural Examination
, 2005
"... I formulate an econometric model of consumer learning and experimentation about new products in markets for packaged goods that nests alternative sources of dynamics, such as habit formation. The model is estimated on household level scanner data of laundry detergent purchases, and the results sugge ..."
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Cited by 7 (0 self)
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I formulate an econometric model of consumer learning and experimentation about new products in markets for packaged goods that nests alternative sources of dynamics, such as habit formation. The model is estimated on household level scanner data of laundry detergent purchases, and the results suggest that consumers have very similar expectations of their match value with new products before consumption experience with the good, and that once consumers have learned their true match values they are very heterogeneous. The estimation results also suggest significant habit formation. Using counterfactual computations derived from the estimates of the structural demand model, I demonstrate that the presence of habit formation with learning changes the implications of the standard empirical learning model: the intermediate run impact of an introductory price cut on a new product’s market share is significantly greater when consumers only form habits as opposed to learning and forming habits at the same time, which suggests that firms should combine price cuts with introductory advertising or free samples to increase their impact. ∗I am indebted to my advisors, Susan Athey, Timothy Bresnahan and Wesley Hartmann for their support and comments. I would also like to thank Liran Einav and Dan Quint for helpful comments. I would like to thank the Stanford Institute for Economic Policy Research for financial support, and the James M. Kilts Center, GSB, University of Chicago, for provision of the data set used in this paper. 1 1
2012) A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models. Quantitative Marketing and
 Economics
"... This paper provides a stepbystep guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed in Imai, Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algor ..."
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Cited by 6 (4 self)
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This paper provides a stepbystep guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed in Imai, Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm, which solves the DDP model and estimates its structural parameters simultaneously. The main computational advantage of this estimation algorithm is the efficient use of information obtained from the past iterations. In the conventional Nested Fixed Point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming algorithm extensively uses the computational results obtained from the past iterations to help solving the DDP model at the current iterated parameter values. Consequently, it significantly alleviates the computational burden of estimating a DDP model. We carefully discuss how to implement the algorithm in practice, and use a simple dynamic store choice model to illustrate how