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10
APPLIED NONPARAMETRIC INSTRUMENTAL VARIABLES ESTIMATION
, 2009
"... Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up ..."
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Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used identify and estimate these parameters. However, linear and other finitedimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators.
Uniform confidence bands for functions estimated nonparametrically with instrumental variables
, 2009
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Supplement to “QuasiBayesian analysis of nonparametric instrumental variables models
, 2013
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Identification and Estimation of Nonparametric Panel Data Regressions with Measurement Error
, 2012
"... This paper provides a constructive argument for identification of nonparametric panel data models with measurement error in a continuous explanatory variable. The approach point identifies all structural elements of the model using only observations of the outcome and the mismeasured explanatory var ..."
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This paper provides a constructive argument for identification of nonparametric panel data models with measurement error in a continuous explanatory variable. The approach point identifies all structural elements of the model using only observations of the outcome and the mismeasured explanatory variable; no further external variables such as instruments are required. Restricting either the structural or the measurement error to be independent over time allows past covariates or outcomes to serve as instruments. Time periods have to be linked through serial dependence in the latent explanatory variable, but the transition process is left nonparametric. The paper discusses the general identification result in the context of a nonlinear panel data regression model with additively separable fixed effects. It provides a nonparametric plugin estimator, derives its uniform rate of convergence, and presents simulation evidence for good performance in finite samples.
Adaptive Nonparametric Instrumental Variables Estimation: Empirical Choice of the Regularization Parameter,Northwestern, unpublished working paper
, 2010
"... ABSTRACT In nonparametric instrumental variables estimation, the mapping that identifies the function of interest, g say, is discontinuous and must be regularized (that is, modified) to make consistent estimation possible. The amount of modification is controlled by a regularization parameter. The ..."
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ABSTRACT In nonparametric instrumental variables estimation, the mapping that identifies the function of interest, g say, is discontinuous and must be regularized (that is, modified) to make consistent estimation possible. The amount of modification is controlled by a regularization parameter. The optimal value of this parameter depends on unknown population characteristics and cannot be calculated in applications. Theoretically justified methods for choosing the regularization parameter empirically in applications are not yet available. This paper presents such a method for use in series estimation, where the regularization parameter is the number of terms in a series approximation to g . The method does not require knowledge of the smoothness of g or of other unknown functions. It adapts to their unknown smoothness. The estimator of g based on the empirically selected regularization parameter converges in probability at a rate that is at least as fast as the asymptotically optimal rate multiplied by 1/ 2 (log ) n , where n is the sample size. The asymptotic integrated meansquare error (AIMSE) of the estimator is within a specified factor of the optimal AIMSE.
Earnings and consumption dynamics: a nonlinear panel data framework,” Discussion paper, cemmap CWP53/15.
, 2015
"... Abstract We develop a new quantilebased panel data framework to study the nature of income persistence and the transmission of income shocks to consumption. Logearnings are the sum of a general Markovian persistent component and a transitory innovation. The persistence of past shocks to earnings ..."
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Abstract We develop a new quantilebased panel data framework to study the nature of income persistence and the transmission of income shocks to consumption. Logearnings are the sum of a general Markovian persistent component and a transitory innovation. The persistence of past shocks to earnings is allowed to vary according to the size and sign of the current shock. Consumption is modeled as an agedependent nonlinear function of assets and the two earnings components. We establish the nonparametric identification of the nonlinear earnings process and the consumption policy rule. Exploiting the enhanced consumption and asset data in recent waves of the Panel Study of Income Dynamics, we find nonlinear persistence and conditional skewness to be key features of the earnings process. We show that the impact of earnings shocks varies substantially across earnings histories, and that this nonlinearity drives heterogeneous consumption responses. The transmission of shocks is found to vary systematically with assets. JEL code: C23, D31, D91.
A Service of zbw LeibnizInformationszentrum Wirtschaft Leibniz Information Centre for Economics Earnings and consumption dynamics: a nonlinear panel data framework Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework *
"... StandardNutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, ..."
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StandardNutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter OpenContentLizenzen (insbesondere CCLizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We develop a new quantilebased panel data framework to study the nature of income persistence and the transmission of income shocks to consumption. Logearnings are the sum of a general Markovian persistent component and a transitory innovation. The persistence of past shocks to earnings is allowed to vary according to the size and sign of the current shock. Consumption is modeled as an agedependent nonlinear function of assets and the two earnings components. We establish the nonparametric identification of the nonlinear earnings process and the consumption policy rule. Exploiting the enhanced consumption and asset data in recent waves of the Panel Study of Income Dynamics, we find nonlinear persistence and conditional skewness to be key features of the earnings process. We show that the impact of earnings shocks varies substantially across earnings histories, and that this nonlinearity drives heterogeneous consumption responses. The transmission of shocks is found to vary systematically with assets. Terms of use: Documents in JEL code: C23, D31, D91.
Department of Agricultural and Resource EconomicsDetermining the Impact of Retailer Store Brand Procurement on Vertical Relationships with Brand Manufacturers and on Market Equilibrium †
, 2010
"... This paper investigates how a retailer’s store brand supply source impacts vertical pricing and supply channel profitability. Using chainlevel retail scanner data from major supermarkets in Boston prior to the leading retailer’s divestiture of its store brand milk processing to a major brand manufa ..."
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This paper investigates how a retailer’s store brand supply source impacts vertical pricing and supply channel profitability. Using chainlevel retail scanner data from major supermarkets in Boston prior to the leading retailer’s divestiture of its store brand milk processing to a major brand manufacturer I estimate a random coefficients logit demand model employing a Bayesian estimation approach. Bayesian decision theory is applied to select from a set of pricing games the one most likely for the data sample analyzed. Results from this analysis indicate that the empirically valid model has the predivested retailer integrated into the processing of its own milk and takes as given the wholesale price of brand milks while competing retailers have nonlinear pricing contracts with brand manufacturers who produce their store brands. This model is matched against a series of counterfactual simulations as a baseline. The counterfactual simulations consider the eventual divestiture of store brand milk processing by the leading retailer
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On completeness and consistency in nonparametric instrumental variable models∗
, 2015
"... This paper provides a first test for the identification condition in a nonparametric instrumental variable model, known as completeness, by linking the outcome of the test to consistency of an estimator. In particular, I show that uniformly over all distributions for which the test rejects with pro ..."
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This paper provides a first test for the identification condition in a nonparametric instrumental variable model, known as completeness, by linking the outcome of the test to consistency of an estimator. In particular, I show that uniformly over all distributions for which the test rejects with probability bounded away from 0, an estimator of the structural function is consistent. This is the case for a large class of complete distributions as well as certain sequences of incomplete distributions. As a byproduct of this result, the paper makes two additional contributions. First, I present a definition of weak instruments in the nonparametric instrumental variable model, which is equivalent to the failure of a restricted version of completeness. Second, I show that the null hypothesis of weak instruments, and thus failure of a restricted version of completeness, is testable and I provide a test statistic and a bootstrap procedure to obtain the critical values. Finally, I demonstrate the finite sample properties of the tests and the estimator in Monte Carlo simulations.