Results 1  10
of
18
An Extended Class of Instrumental Variables for the Estimation of Causal Effects
 UCSD DEPT. OF ECONOMICS DISCUSSION PAPER
, 1996
"... This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regresso ..."
Abstract

Cited by 40 (16 self)
 Add to MetaCart
This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.
Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects
, 2009
"... This paper is concerned with extending the familiar notion of fixed effects to nonlinear setups with infinite dimensional unobservables like preferences. The main result is that a generalized version of differencing identifies local average structural derivatives (LASDs) in very general nonseparable ..."
Abstract

Cited by 13 (3 self)
 Add to MetaCart
This paper is concerned with extending the familiar notion of fixed effects to nonlinear setups with infinite dimensional unobservables like preferences. The main result is that a generalized version of differencing identifies local average structural derivatives (LASDs) in very general nonseparable models, while allowing for arbitrary dependence between the persistent unobservables and the regressors of interest even if there are only two time periods. These quantities specialize to well known objects like the slope coefficient in the semiparametric panel data binary choice model with fixed effects. We extend the basic framework to include dynamics in the regressors and time trends, and show how distributional effects as well as average effects are identified. In addition, we show how to handle endogeneity in the transitory component. Finally, we adapt our results to the semiparametric binary choice model with correlated coefficients, and establish that average structural marginal probabilities are identified. We conclude this paper by applying the last result to a real world data example. Using the PSID, we analyze the way in which the lending restrictions for mortgages eased between 2000 and 2004.
Endogeneity in Semiparametric Binary Random Coe¢ cient Models,Discussion paper
, 2008
"... Abstract In this paper we consider the case of endogenous regressors in the binary choice model under a weak median exclusion restriction, but without further specification of the distribution of the unobserved random components. As a particularly relevant example for a model where no semiparametri ..."
Abstract

Cited by 13 (0 self)
 Add to MetaCart
Abstract In this paper we consider the case of endogenous regressors in the binary choice model under a weak median exclusion restriction, but without further specification of the distribution of the unobserved random components. As a particularly relevant example for a model where no semiparametric estimator has of yet been analyzed, we consider the binary random coefficients model with endogenous regressors. However, many of the arguments we make hold more generally in all endogenous binary choice models with heteroscedasticity. We focus on the estimation of a centrality parameter β, because even in random coefficient models usually an average effect and not the entire distribution of coefficients is of interest. We use a control function IV assumption to identify a centrality parameter that has the interpretation of a local average structural effect of the regressor on the latent variable, and establish identification based on the mean ratio of derivatives of two functions of the instruments. We propose an estimator based on sample counterparts, and discuss the large sample behavior. In particular, we show √ n consistency and derive the asymptotic distribution. In the same framework, we propose tests for heteroscedasticity, overidentification and endogeneity. We analyze the small sample performance through a simulation study. An application of the model to IO demand data concludes this paper.
Endogenous Semiparametric Binary Choice Models with Heteroscedasticity,” 2009. Boston College working paper
"... In this paper we consider endogenous regressors in the binary choice model under a weak median exclusion restriction, but without further specification of the distribution of the unobserved random components. Our reduced form specification with heteroscedastic residuals covers various heterogeneous ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
In this paper we consider endogenous regressors in the binary choice model under a weak median exclusion restriction, but without further specification of the distribution of the unobserved random components. Our reduced form specification with heteroscedastic residuals covers various heterogeneous structural binary choice models. We employ a control function IV assumption to establish identification of a slope parameter in the reduced form model by the mean ratio of derivatives of two functions of the instruments. We propose a direct estimator based on sample counterparts, and discuss the large sample behavior of this estimator. In particular, we show p n consistency and derive the asymptotic distribution. As a particularly relevant example of a structural model where no semiparametric estimator has of yet been analyzed, we consider the binary random utility model with endogenous regressors and heterogeneous parameters. Moreover, we propose tests for heteroscedasticity, overidentification and endogeneity. We analyze the small sample performance through a simulation study. An application of the model to discrete choice demand data concludes this paper.
2004), "Integrability of Demand Accounting for Unobservable Heterogeneity: A Test on Panel Data," unpublished manuscript
"... In recent years it has become apparent that we must take unobservable heterogeneity into account when conducting empirical consumer demand analysis. This paper is concerned with integrability (that is, whether demand is consistent with utility maximization) of the conditional mean demand (that is, t ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
In recent years it has become apparent that we must take unobservable heterogeneity into account when conducting empirical consumer demand analysis. This paper is concerned with integrability (that is, whether demand is consistent with utility maximization) of the conditional mean demand (that is, the estimated demand) when allowing for unobservable heterogeneity. Integrability is important because it is necessary in order for the demand system estimates to be used for welfare analysis. Conditions for conditional mean demand to be integrable in the presence of unobservable heterogeneity are developed in the literature. There is, however, little empirical evidence suggesting whether these conditions for integrability are likely to be met in the data or not. In this paper we exploit the fact that the integrability conditions have testable implications for panel data and use a unique long panel data set to test them. Because of the sizeable longitudinal length of the panel, we are able to identify a very flexible specification of unobservable heterogeneity: We model individual demands as an Almost Ideal Demand system and allow for unobservable heterogeneity by allowing all intercept and slope parameters of the demand system to be individualspecific. We test the conditions for integrability of the conditional mean demand of this demand system. We do not reject them. This
Local Indirect Least Squares and Average Marginal Effects in Nonseparable Structural Systems
, 2009
"... We study the scope of local indirect least squares (LILS) methods for nonparametrically estimating average marginal effects of an endogenous cause X on a response Y in triangular structural systems that need not exhibit linearity, separability, or monotonicity in scalar unobservables. One main findi ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
(Show Context)
We study the scope of local indirect least squares (LILS) methods for nonparametrically estimating average marginal effects of an endogenous cause X on a response Y in triangular structural systems that need not exhibit linearity, separability, or monotonicity in scalar unobservables. One main finding is negative: in the fully nonseparable case, LILS methods cannot recover the average marginal effect. LILS methods can nevertheless test the hypothesis of no effect in the general nonseparable case. We provide new nonparametric asymptotic theory, treating both the traditional case of observed exogenous instruments Z and the case where one observes only errorladen proxies for Z.
Structural Measurement Errors in Nonseparable Models ∗
, 2009
"... This paper considers measurement error from a new perspective. In surveys, response errors are often caused by the fact that respondents recall past events and quantities imperfectly. We explore the consequences of recall errors for such key econometric issues as the identification of marginal effec ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
This paper considers measurement error from a new perspective. In surveys, response errors are often caused by the fact that respondents recall past events and quantities imperfectly. We explore the consequences of recall errors for such key econometric issues as the identification of marginal effects or economic restrictions in structural models. Our identification approach is entirely nonparametric, using Matzkintype nonseparable models that nest a large class of potential structural models. We establish that measurement errors due to poor recall are generally likely to exhibit nonstandard behavior, in particular be nonclassical and differential, and we provide means to deal with this situation. Moreover, our findings suggest that conventional wisdom about measurement errors may be misleading in many economic applications. For instance, under certain conditions lefthand side recall errors will be problematic even in the linear model, and quantiles will be less robust than means. Finally, we apply the main concepts put forward in this paper to real world data, and find evidence that underscores the importance of focusing on individual response behavior.
Price Dimension Reduction in Demand Systems With Many Goods
, 2006
"... Estimation of demand systems with many goods is empirically difficult because demand functions depend, flexibly and usually nonlinearly, on the prices of all goods. The standard solution is to impose strong, empirically questionable behavioral restrictions on price elasticities via separability. Th ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Estimation of demand systems with many goods is empirically difficult because demand functions depend, flexibly and usually nonlinearly, on the prices of all goods. The standard solution is to impose strong, empirically questionable behavioral restrictions on price elasticities via separability. This paper proposes an alternative based on applying statistical dimension reduction methods to the price vector, and deriving the resulting restrictions on demand functions that remain due to Slutsky symmetry and other implications of utility maximization. The results permit estimation of the effects of income and of prices of some goods on the demand functions for every good without imposing any separability. We illustrate the results by reporting estimates of the effects of gasoline prices on the demands for many goods.
Regressor Dimension Reduction with Economic Constraints: The Example of Demand Systems with Many Goods
, 2006
"... Microeconomic theory often yields models with multiple nonlinear equations, nonseparable unobservables, nonlinear cross equation restrictions, and many potentially multicollinear covariates. We show how statistical dimension reduction techniques can be applied in models with these features. In parti ..."
Abstract
 Add to MetaCart
Microeconomic theory often yields models with multiple nonlinear equations, nonseparable unobservables, nonlinear cross equation restrictions, and many potentially multicollinear covariates. We show how statistical dimension reduction techniques can be applied in models with these features. In particular, we consider estimation of derivatives of average structural functions in large consumer demand systems, which depend nonlinearly on the prices of many goods. Utility maximization imposes nonlinear cross equation constraints including Slutsky symmetry, and preference heterogeneity yields demand functions that are nonseparable in unobservables. The standard method of achieving dimension reduction in demand systems is to impose strong, empirically questionable economic restrictions like separability. In contrast, the validity of statistical methods of dimension reduction like principal components have not hitherto been studied in contexts like these. We derive the restrictions implied by utility maximization on dimension reduced demand systems, and characterize the implications for identi…cation and estimation of structural marginal e¤ects. We illustrate the results by reporting estimates of the e¤ects of gasoline prices on the demands for many goods, without imposing any economic separability assumptions.