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45
Semiparametric Inference in dynamic binary choice models
, 2012
"... We introduce an approach for semiparametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is applica ..."
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Cited by 12 (2 self)
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We introduce an approach for semiparametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is applicable to models with finite space of observed states. We demonstrate the method on Rust’s model of bus engine replacement. The estimation experiments show that the parametric assumptions about the distribution of the unobserved states can have a considerable effect on the estimates of perperiod payoffs. At the same time, the effect of these assumptions on counterfactual conditional choice probabilities can be small for most of the observed states.
Nonparametric Identification and Estimation of Random Coefficients in Nonlinear Economic Models
, 2010
"... We show how to nonparametrically identify and estimate the distribution of random coefficients that characterizes the heterogeneity among agents in a general class of economic choice models. We introduce an axiom that we term separability and prove that separability of a structural model ensures ide ..."
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We show how to nonparametrically identify and estimate the distribution of random coefficients that characterizes the heterogeneity among agents in a general class of economic choice models. We introduce an axiom that we term separability and prove that separability of a structural model ensures identification. Identification naturally gives rise to a nonparametric minimum distance estimator. We prove identification of distributions of utility functions in multinomial choice, distributions of labor supply responses to tax changes, and distributions of wage functions in the Roy selection model. We also reconsider the problem of endogeneity in economic choice models, leading to new results on the twostage least squares model.
The Response of Drug Expenditures to NonLinear Contract Design: Evidence from Medicare Part D
 NBER Working Paper No. 19393, National Bureau of Economic Research
, 2013
"... Abstract. We study the demand response to nonlinear price schedules using data on insurance contracts and prescription drug purchases in Medicare Part D. We exploit the kink in individualsbudget set created by the famous donut hole,where insurance becomes discontinuously much less generous on the m ..."
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Cited by 12 (3 self)
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Abstract. We study the demand response to nonlinear price schedules using data on insurance contracts and prescription drug purchases in Medicare Part D. We exploit the kink in individualsbudget set created by the famous donut hole,where insurance becomes discontinuously much less generous on the margin, to provide descriptive evidence of the drug purchase response to a price increase. We then specify and estimate a simple dynamic model of drug use that allows us to quantify the spending response along the entire nonlinear budget set. We use the model for counterfactual analysis of the increase in spending from
llingthe donut hole, as will be required by 2020 under the A¤ordable Care Act. In our baseline model, which considers spending decisions within a single year, we estimate that
llingthe donut hole will increase annual drug spending by about $150, or about 8 percent. About onequarter of this spending increase reects anticipatorybehavior, coming from bene
ciaries whose spending prior to the policy change would leave them short of reaching the donut hole. We also present descriptive evidence of crossyear substitution of spending by individuals who reach the kink, which motivates a simple extension to our baseline model that allows in a highly stylized way for individuals to engage in such cross year substitution. Our estimates from this extension suggest that a large share of the $150 drug spending increase could be attributed to crossyear substitution, and the net increase could be as little as $45 per year.
Nonparametric Identification Using Instrumental Variables: Sufficient Conditions For Completeness
, 2011
"... This paper provides sufficient conditions for the nonparametric identification of the regression function m (·) in a regression model with an endogenous regressor x and an instrumental variable z. It has been shown that the identification of the regression function from the conditional expectation o ..."
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Cited by 10 (3 self)
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This paper provides sufficient conditions for the nonparametric identification of the regression function m (·) in a regression model with an endogenous regressor x and an instrumental variable z. It has been shown that the identification of the regression function from the conditional expectation of the dependent variable on the instrument relies on the completeness of the distribution of the endogenous regressor conditional on the instrument, i.e., f(xz). We provide sufficient conditions for the completeness of f(xz) without imposing a specific functional form, such as the exponential family. We show that if the conditional density f(xz) coincides with an existing complete density at a limit point in the support of z, then f(xz) itself is complete, and therefore, the regression function m (·) is nonparametrically identified. We use this general result provide specific sufficient conditions for completeness in three different specifications of the relationship between the endogenous regressor x and the instrumental variable z.
2011): “Nonlinear Panel Data Analysis
 Annual Review of Economics
"... Nonlinear panel data models arise naturally in economic applications, yet their analysis is challenging. Here we provide a progress report on some recent advances in the area. We start by reviewing the properties of randomeffects likelihood approaches. We emphasize a link with Bayesian computation ..."
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Cited by 9 (0 self)
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Nonlinear panel data models arise naturally in economic applications, yet their analysis is challenging. Here we provide a progress report on some recent advances in the area. We start by reviewing the properties of randomeffects likelihood approaches. We emphasize a link with Bayesian computation and Markov Chain Monte Carlo, which provides a convenient approach to estimation and inference. Relaxing parametric assumptions on the distribution of individual effects raises serious identification problems. In discrete choice models, common parameters and average marginal effects are generally setidentified. The availability of continuous outcomes, however, provides opportunities for pointidentification. We end the paper by reviewing recent progress on non fixedT approaches. In panel applications where the time dimension is not negligible relative to the size of the crosssection, it makes sense to view the estimation problem as a timeseries finite sample bias. Several perspectives to bias reduction are now available. We review their properties, with a special emphasis on randomeffects methods. JEL codes: C23.
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.
Why Do Life Insurance Policyholders Lapse? The Roles of Income, Health and Bequest Motive Shocks.” Working paper
, 2010
"... We present an empirical dynamic discrete choice model of life insurance decisions designed to bypass data limitations where researchers only observe whether an individual has made a new life insurance decision but but do not observe the actual policy choice or the choice set from which the policy is ..."
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We present an empirical dynamic discrete choice model of life insurance decisions designed to bypass data limitations where researchers only observe whether an individual has made a new life insurance decision but but do not observe the actual policy choice or the choice set from which the policy is selected. The model also incorporates serially correlated unobservable state variables, for which we provide ample evidence that they are required to explain some key features in the data. We empirically implement the model using the limited life insurance holding information from the Health and Retirement Study (HRS) data. We deal with serially correlated unobserved state variables using posterior distributions of the unobservables simulated from Sequential Monte Carlo (SMC) methods. Counterfactual simulations using the estimates of our model suggest that a large fraction of life insurance lapsations are driven by i.i.d choice specific shocks, particularly when policyholders are relatively young. But as the remaining policyholders get older, the role of such i.i.d. shocks gets less important, and more of their lapsations are driven either by income, health or bequest motive shocks. Income and health shocks are relatively more important than bequest motive shocks in explaining lapsations when policyholders are young, but as they age, the bequest motive shocks play a more important role.
Identifying dynamic discrete choice models off short panels
, 2013
"... This paper analyzes identi
cation in dynamic discrete choice models of single agents and noncooperative games. We extend previous work by providing new conditions for identifying ow payo¤s in stationary settings, and in nonstationary settings when the data is sampled every period respondents make d ..."
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Cited by 6 (3 self)
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This paper analyzes identi
cation in dynamic discrete choice models of single agents and noncooperative games. We extend previous work by providing new conditions for identifying ow payo¤s in stationary settings, and in nonstationary settings when the data is sampled every period respondents make decisions. These results are also a benchmark for investigating identi
cation when the relevant time horizon extends beyond the length of the data. We show that in short panels the utility ows of models with
nite dependence are partially identi
ed for particular normalizations. Finally, when nite dependence does not hold, or when the required normalizations are unattractive, we show how exclusion restrictions or stability of the ow payo ¤ function over time can be used to recover ow payo¤s. 1
Dynamic Entry with Cross Product Spillovers: An Application to the Generic Drug Industry. Yale University Working Paper
, 2011
"... Abstract Experience in one product market can potentially improve firm performance in a related product market in the future. Thus, entry into a market is determined not just by profits in that market but also by the future effect of entry on profitability in other markets. We formulate and estimat ..."
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Abstract Experience in one product market can potentially improve firm performance in a related product market in the future. Thus, entry into a market is determined not just by profits in that market but also by the future effect of entry on profitability in other markets. We formulate and estimate a dynamic game of entry decisions of firms in the presence of such spillovers using data on the generic drug industry. Spillovers imply that the unobserved component of a firm's profitability not only changes stochastically but is also is endogenous to past entry decisions. Therefore, the model needs to accommodate unobserved state variables that are endogenous to firm actions and serially correlated. We address the methodological challenge of estimating such a dynamic game using a sequential importance sampling based technique. Our estimates show significant spillover effects of entry on future profits. On average, each entry reduces costs by 7% at the next entry opportunity. In our sample there are on average eight entry opportunities annually. The average cumulative benefit of a firm that enters all eight markets in a year is 51%. We conclude that spillovers are critical in the equilibrium evolution of the structure of the generic drug industry.