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52
Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation
"... This paper estimates models of the evolution of cognitive and noncognitive skills and explores the role of family environments in shaping these skills at different stages of the life cycle of the child. Central to this analysis is identification of the technology of skill formation. We estimate a dy ..."
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Cited by 215 (42 self)
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This paper estimates models of the evolution of cognitive and noncognitive skills and explores the role of family environments in shaping these skills at different stages of the life cycle of the child. Central to this analysis is identification of the technology of skill formation. We estimate a dynamic factor model to solve the problem of endogeneity of inputs and multiplicity of inputs relative to instruments. We identify the scale of the factors by estimating their effects on adult outcomes. In this fashion we avoid reliance on test scores and changes in test scores that have no natural metric. Parental investments are generally more effective in raising noncognitive skills. Noncognitive skills promote the formation of cognitive skills but, in most specifications of our model, cognitive skills do not promote the formation of noncognitive skills. Parental inputs have different effects at different stages of the child’s life cycle with cognitive skills affected more at early ages and noncognitive skills affected more at later ages.
Instrumental variable treatment of nonclassical measurement error models.
 Econometrica,
, 2008
"... Abstract While the literature on nonclassical measurement error traditionally relies on the availability of an auxiliary dataset containing correctly measured observations, we establish that the availability of instruments enables the identification of a large class of nonclassical nonlinear errors ..."
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Cited by 64 (18 self)
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Abstract While the literature on nonclassical measurement error traditionally relies on the availability of an auxiliary dataset containing correctly measured observations, we establish that the availability of instruments enables the identification of a large class of nonclassical nonlinear errorsinvariables models with continuously distributed variables. Our main identifying assumption is that, conditional on the value of the true regressors, some "measure of location" of the distribution of the measurement error (e.g. its mean, mode or median) is equal to zero. The proposed approach relies on the eigenvalueeigenfunction decomposition of an integral operator associated with specific joint probability densities. The main identifying assumption is used to "index" the eigenfunctions so that the decomposition is unique. We propose a convenient sievebased estimator, derive its asymptotic properties and investigate its finitesample behavior through Monte Carlo simulations.
Nonlinear policy rules and the identification and estimation of causal effects in a generalized regression kink design. National Bureau of Economic Research, Working Paper No
, 2012
"... Uppsala, Wharton and Zürich. Andrea Weber gratefully acknowledges research funding from the Austrian ..."
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Cited by 25 (1 self)
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Uppsala, Wharton and Zürich. Andrea Weber gratefully acknowledges research funding from the Austrian
Nonparametric estimation of nonadditive hedonic models
, 2002
"... We present methods to estimate marginal utility and marginal product functions that are nonadditive in the unobservable random terms, using observations from a single hedonic equilibrium market. We show that nonadditive marginal utility and nonadditive marginal product functions are capable of gener ..."
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Cited by 24 (7 self)
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We present methods to estimate marginal utility and marginal product functions that are nonadditive in the unobservable random terms, using observations from a single hedonic equilibrium market. We show that nonadditive marginal utility and nonadditive marginal product functions are capable of generating equilibria that exhibit bunching, as well as other types of equilibria. We provide conditions under which these types of utility and production functions are nonparametrically identified, and we propose nonparametric estimators for them. The estimators are shown to be consistent and asymptotically normal.
Identification and Estimation of ‘Irregular’ Correlated Random Coefficient Models
, 2008
"... In this paper we study identification and estimation of the causal effect of a small change in an endogenous regressor on a continuouslyvalued outcome of interest using panel data. We focus on the average partial effect (APE) over the full population distribution of unobserved heterogeneity (e.g., ..."
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Cited by 15 (1 self)
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In this paper we study identification and estimation of the causal effect of a small change in an endogenous regressor on a continuouslyvalued outcome of interest using panel data. We focus on the average partial effect (APE) over the full population distribution of unobserved heterogeneity (e.g., Chamberlain, 1984; Blundell and Powell, 2003; Wooldridge, 2005a). In our basic model the outcome of interest varies linearly with a (scalar) regressor, but with an intercept and slope coefficient that may vary across units and over time in a way which depends on the regressor. This model is a special case of Chamberlain’s (1980b, 1982, 1992a) correlated random coefficients (CRC) model, but not does not satisfy the regularity conditions he imposes. Irregularity, while precluding estimation at parametric rates, does not result in a loss of identification under mild smoothness conditions. We show how two measures of the outcome and regressor for each unit are sufficient for identification of the APE as well as aggregate time trends. We identify aggregate trends using units with a zero first difference in the regressor or, in the language of Chamberlain (1980b, 1982), ‘stayers’ and the average partial effect using units with nonzero first differences or ‘movers’. We discuss extensions of our approach to models with multiple regressors and more than two time periods. We use our
MarkovSwitching Models with Endogenous Explanatory Variables
 Journal of Econometrics
, 2004
"... Jointly organised by EUROSTAT ..."
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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Cited by 12 (4 self)
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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.
Measuring the average outcome and inequality effects of segregation in the presence of social spillovers
, 2009
"... ..."
Testing Multivariate Economic Restrictions Using Quantiles: The Example of Slutsky Negative
, 2013
"... This paper is concerned with testing rationality restrictions using quantile regression methods. Speci
cally, we consider negative semide
niteness of the Slutsky matrix, arguably the core restriction implied by utility maximization. We consider a heterogeneous population characterized by a system o ..."
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Cited by 4 (0 self)
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This paper is concerned with testing rationality restrictions using quantile regression methods. Speci
cally, we consider negative semide
niteness of the Slutsky matrix, arguably the core restriction implied by utility maximization. We consider a heterogeneous population characterized by a system of nonseparable structural equations with in
nite dimensional unobservable. To analyze this economic restriction, we employ quantile regression methods because they allow us to utilize the entire distribution of the data. Di ¢ culties arise because the restriction involves several equations, while the quantile is a univariate concept. We establish that we may test the economic restriction by considering quantiles of linear combinations of the dependent variable. For this hypothesis we develop a new empirical process based test that applies kernel quantile estimators, and derive its large sample behavior. We investigate the performance of the test in a simulation study. Finally, we apply all concepts to Canadian microdata, and show that rationality is not rejected.
Semiparametric identification and estimation of correlated . . .
, 2008
"... In this paper we study identification and estimation of the causal effect of a small change in an endogenous regressor on a continuouslyvalue outcome of interest using panel data. Specifically we focus on averages over the population distribution of unobserved heterogeneity, or average partial e¤ec ..."
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Cited by 4 (0 self)
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In this paper we study identification and estimation of the causal effect of a small change in an endogenous regressor on a continuouslyvalue outcome of interest using panel data. Specifically we focus on averages over the population distribution of unobserved heterogeneity, or average partial e¤ects (APEs), and averages over subpopulations defined by their regressor values, or local average responses (LARs). Our central model assumes that the outcome variable is related to a (scalar) regressor, where the intercept and slope coefficients vary across individuals and are not independent of the regressor; for this model, we show how two measures of the outcome and regressor for each unit are sufficient for identification of the partial effects. This model is a semiparametric extension of the textbook linear fixed effects (FE) model widely used in empirical research; a distinctive feature of our approach is that it semiparametrically just identifies the APE and LAR and hence clearly illustrates the value and limits of panel data in dealing with endogeneity. A strengthening of our basic assumptions also allows us to identify Quantile Partial E¤ects (QPEs). We discuss extensions of our approach to models with multiple regressors and more than two time periods, and to models which permit contemporaneous endogeneity of the regressors and timespeci…c error terms.