Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64720
Authors: 
Freyberger, Joachim
Horowitz, Joel
Year of Publication: 
2012
Series/Report no.: 
cemmap working paper CWP15/12
Abstract: 
This paper is concerned with inference about an unidentified linear functional, L(g), where the function g satisfies the relation Y=g(x) + U; E(U/W) = 0. In this relation, Y is the dependent variable, X is a possibly endogenous explanatory variable, W is an instrument for X, and U is an unobserved random variable. The data are an independent random sample of (Y, X, W). In much applied research, X and W are discrete, and W has fewer points of support than X. Consequently, neither g nor L(g) is nonparametrically identified. Indeed, L(g) can have any value in (-∞, ∞). In applied research, this problem is typically overcome and point identification is achieved by assuming that g is a linear function of X. However, the assumption of linearity is arbitrary. It is untestable if W is binary, as is the case in many applications. This paper explores the use of shape restrictions, such as monotonicity or convexity, for achieving interval identification of L(g). Economic theory often provides such shape restrictions. This paper shows that they restrict L(g) to an interval whose upper and lower bounds can be obtained by solving linear programming problems. Inference about the identified interval and the functional L(g) can be carried out by using by using the bootstrap. An empirical application illustrates the usefulness of shape restrictions for carrying out nonparametric inference about L(g).
JEL: 
C13
C14
C26
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
426.08 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.