Local Indirect Least Squares and Average Marginal Effects in Nonseparable Structural Systems

Journal of Econometrics (Impact Factor: 1.6). 01/2007; 166(680). DOI: 10.1016/j.jeconom.2011.09.041
Source: RePEc


Identification in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of non- and semi- parametric identification in such models are re-examined. Nonparametric identification holds under weaker assumptions than in (S); the proof here does not rely on decomposition of generalized functions into ordinary and singular parts, which may not hold. Conditions for continuity of the identification mapping are provided and a consistent nonparametric plug-in estimator for regression functions in the L₁ space constructed. Semiparametric identification via a finite set of moments is shown to hold for classes of functions that are explicitly characterized; unlike (S) existence of a moment generating function for the measurement

Full-text preview

Available from:
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 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.
    Journal of Econometrics 01/2009; 168(CWP33/09). DOI:10.1016/j.jeconom.2012.01.033 · 1.60 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper compares the structural approach to economic policy analysis with the program evaluation approach. It offers a third way to do policy analysis that combines the best features of both approaches. We illustrate the value of this alternative approach by making the implicit economics of LATE explicit, thereby extending the interpretability and range of policy questions that LATE can answer.
    Journal of Economic Literature 06/2010; 48(2):356-398. DOI:10.1257/jel.48.2.356 · 9.24 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We provide necessary and sufficient conditions for effect identification, thereby characterizing the limits to identification. Our results link the non-structural potential outcome framework for identifying and estimating treatment effects to structural approaches in economics. This permits economic theory to be built into treatment effect methods. We elucidate the sources and consequences of identification failure by examining the biases arising when the necessary conditions fail, and we clarify the relations between unconfoundedness, conditional exogeneity, and the necessary and sufficient identification conditions. A new quantity, the exogeneity score, plays a central role in this analysis, permitting an omitted variable representation for effect biases. This analysis also provides practical guidance for selecting covariates and insight into the price paid for making various identifying assumptions and the benefits gained.
    Econometric Reviews 01/2012; 32(3). DOI:10.1080/07474938.2012.690664 · 1.19 Impact Factor
Show more