Results 1  10
of
25
2009): “Nonparametric and semiparametric analysis of a dynamic game model
"... In this paper, we study the identification and estimation of a dynamic discrete game allowing for discrete or continuous state variables. We first provide a general nonparametric identification result under the imposition of an exclusion restrictions on agents payoffs. Next we analyze large sample s ..."
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Cited by 24 (1 self)
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In this paper, we study the identification and estimation of a dynamic discrete game allowing for discrete or continuous state variables. We first provide a general nonparametric identification result under the imposition of an exclusion restrictions on agents payoffs. Next we analyze large sample statistical properties of nonparametric and semiparametric estimators for the econometric dynamic game model. Numerical simulations are used to demonstrate the finite sample properties of the dynamic game estimators.
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.
Identification and Estimation in Discrete Choice Demand Models when Endogenous Variables Interact with the Error
, 2012
"... We develop an estimator for the parameters of a utility function that has interactions between the unobserved demand error and observed factors including price. We show that the Berry (1994)/Berry, Levinsohn, and Pakes (1995) inversion and contraction can still be used to recover the mean utility t ..."
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Cited by 6 (3 self)
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We develop an estimator for the parameters of a utility function that has interactions between the unobserved demand error and observed factors including price. We show that the Berry (1994)/Berry, Levinsohn, and Pakes (1995) inversion and contraction can still be used to recover the mean utility term that now contains both the demand error and the interactions with the error. However, the instrumental variable (IV) solution is no longer consistent because the price interaction term is correlated with the instrumented price. We show that the standard conditional moment restrictions (CMRs) do not generally suffice for identification. We supplement the standard CMRs with “generalized ” control functions and we show together they are sufficient for identification of all of the demand parameters. Our estimator extends (Berry, Linton, and Pakes, 2004) to the case where there are estimated regressors. We run several monte carlos that show our approach works when the standard IV approaches fail because of nonseparability. We also test and reject additive separability in the original Berry, Levinsohn, and Pakes (1995) automobile data, and we show that demand becomes significantly more elastic when the correction is applied.
Penalized Sieve Estimation and Inference of Seminonparametric Dynamic Models: A Selective Review
, 2011
"... In this selective review, we …rst provide some empirical examples that motivate the usefulness of seminonparametric techniques in modelling economic and …nancial time series. We describe popular classes of seminonparametric dynamic models and some temporal dependence properties. We then present pe ..."
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Cited by 6 (2 self)
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In this selective review, we …rst provide some empirical examples that motivate the usefulness of seminonparametric techniques in modelling economic and …nancial time series. We describe popular classes of seminonparametric dynamic models and some temporal dependence properties. We then present penalized sieve extremum (PSE) estimation as a general method for seminonparametric models with crosssectional, panel, time series, or spatial data. The method is especially powerful in estimating di ¢ cult illposed inverse problems such as seminonparametric mixtures or conditional moment restrictions. We review recent advances on inference and large sample properties of the PSE estimators, which include (1) consistency and convergence rates of the PSE estimator of the nonparametric part; (2) limiting distributions of plugin PSE estimators of functionals that are either smooth (i.e., rootn estimable) or nonsmooth (i.e., slower than rootn estimable); (3) simple criterionbased inference for plugin PSE estimation of smooth or nonsmooth functionals; and (4) rootn asymptotic normality of semiparametric twostep estimators and their consistent variance estimators. Examples from dynamic asset pricing, nonlinear spatial VAR, semiparametric GARCH,
Consistency and Asymptotic Normality of Sieve Estimators Under Weak and Verifiable Conditions
, 2012
"... This paper considers sieve estimation of seminonparametric (SNP) models with an unknown density function as nonEuclidean parameter, next to a finitedimensional parameter vector. The density function involved is modeled via an infinite series expansion, so that the actual parameter space is infini ..."
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Cited by 5 (4 self)
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This paper considers sieve estimation of seminonparametric (SNP) models with an unknown density function as nonEuclidean parameter, next to a finitedimensional parameter vector. The density function involved is modeled via an infinite series expansion, so that the actual parameter space is infinitedimensional. It will be shown that under weak and verifiable conditions the sieve estimators of these parameters are consistent, and the estimators of the Euclidean parameters are √ N asymptotically normal, given a random sample of size N. The latter result is derived in a different way than in the sieve estimation literature. It appears that this asymptotic normality result is in essence the same as for the finite dimensional case. This approach is motivated and illustrated by an SNP discrete choice model. The helpful comments of Xiaohong Chen, two referees and the coeditor, Arthur Lewbel, are gratefully acknowledged. Previous versions of this paper have been presented at Penn State
Extremum estimation and numerical derivatives
, 2010
"... Abstract Many empirical researchers rely on the use of finitedifference approximation to evaluate derivatives of estimated functions. For instance commonly used optimization routines implicitly use finitedifference formulas for the gradients, which require the choice of step size parameters This ..."
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Cited by 4 (0 self)
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Abstract Many empirical researchers rely on the use of finitedifference approximation to evaluate derivatives of estimated functions. For instance commonly used optimization routines implicitly use finitedifference formulas for the gradients, which require the choice of step size parameters This paper investigates the statistical properties of numerically evaluated gradients and of extremum estimators computed using numerical gradients. We find that first, one needs to adjust the step size or the tolerance as a function of the sample size. Second, higherorder finite difference formulas reduce the asymptotic bias similar to higher order kernels. Third, we provide weak sufficient conditions for uniform consistency of the finitedifference approximations for gradients and directional derivatives. Fourth, we analyze the numerical gradientbased extremum estimators and find that the asymptotic distribution of the resulting estimators can sometimes depend on the sequence of step sizes. We state conditions under which the numerical derivative estimator is consistent and asymptotically normal. Fifth, we generalize our results to semiparametric estimation problems. Finally, we show that the theory is also useful in a range of nonstandard estimation procedures. JEL Classification: C14; C52
Productivity dynamics and the role of “bigbox” entrants in retailing
, 2009
"... Entry of large (“bigbox”) stores along with a drastic fall in the total number of stores is a striking trend in retail markets. We use a dynamic structural model to estimate total factor productivity in retail. Then we assess whether entry of large stores drives exit and growth in the productivity ..."
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Cited by 3 (1 self)
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Entry of large (“bigbox”) stores along with a drastic fall in the total number of stores is a striking trend in retail markets. We use a dynamic structural model to estimate total factor productivity in retail. Then we assess whether entry of large stores drives exit and growth in the productivity distribution of incumbents. Using detailed data on all retail food stores in Sweden, we find that local market characteristics, selection, and nonlinearities in the productivity process are important when estimating retail productivity. Large entrants force low productive stores to exit and surviving stores to increase their productivity growth. Growth increases most among incumbents in the bottom part of the productivity distribution, and then declines with the productivity level of incumbents. We use political preferences in local markets to control for endogeneity of large entrants. Our findings suggest that large entrants play a crucial role for driving productivity growth.
Estimating Production Functions with Robustness Against Errors in the Proxy Variables ∗
, 2011
"... This paper proposes a new seminonparametric maximum likelihood estimation method for estimating production functions. The method extends the literature on structural estimation of production functions, started by the seminal work of Olley and Pakes (1996), by relaxing the scalarunobservable assump ..."
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Cited by 3 (0 self)
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This paper proposes a new seminonparametric maximum likelihood estimation method for estimating production functions. The method extends the literature on structural estimation of production functions, started by the seminal work of Olley and Pakes (1996), by relaxing the scalarunobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables. The assumption seems reasonable in many important cases. The new method is straightforward to apply, and a consistent estimate of the asymptotic covariance matrix of the structural parameters can be easily computed. 1
Slutsky Matrix Norms and the Size of Bounded Rationality∗
, 2014
"... Given any observed demand behavior by means of a demand function, we quantify by how much it departs from rationality. Using a recent elaboration of the “almost implies near ” principle, the measure of the gap is the smallest norm of the correcting matrix that would yield a Slutsky matrix with its ..."
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Cited by 3 (0 self)
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Given any observed demand behavior by means of a demand function, we quantify by how much it departs from rationality. Using a recent elaboration of the “almost implies near ” principle, the measure of the gap is the smallest norm of the correcting matrix that would yield a Slutsky matrix with its standard rationality properties (symmetry, singularity, and negative semidefiniteness). A useful classification of departures from rationality is suggested as a result. Variants, examples, and applications are discussed, and illustrations are provided using several bounded rationality models.
CONSISTENCY AND ASYMPTOTIC NORMALITY OF SIEVE ML ESTIMATORS UNDER LOWLEVEL CONDITIONS
, 2013
"... This paper considers sieve maximum likelihood estimation of seminonparametric (SNP) models with an unknown density function as nonEuclidean parameter, next to a finitedimensional parameter vector. The density function involved is modeled via an infinite series expansion, so that the actual parame ..."
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Cited by 2 (2 self)
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This paper considers sieve maximum likelihood estimation of seminonparametric (SNP) models with an unknown density function as nonEuclidean parameter, next to a finitedimensional parameter vector. The density function involved is modeled via an infinite series expansion, so that the actual parameter space is infinitedimensional. It will be shown that under lowlevel conditions the sieve estimators of these parameters are consistent, and the estimators of the Euclidean parameters are √ N asymptotically normal, given a random sample of size N. The latter result is derived in a different way than in the sieve estimation literature. It appears that this asymptotic normality result is in essence the same as for the finite dimensional case. This approach is motivated and illustrated by an SNP discrete choice model. Professor Emeritus of Economics. The very helpful comments of Xiaohong Chen, Arthur Lewbel, Peter Phillips and four referees, leading to substantial improvements over previous versions, are gratefully acknowledged. Previous versions of this paper have been presented at