NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

NBER Publications by Bryan S. Graham

Working Papers and Chapters

April 2009Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis
with Guido W. Imbens, Geert Ridder: w14860
This paper presents methods for evaluating the effects of reallocating an indivisible input across production units, taking into account resource constraints by keeping the marginal distribution of the input fixed. When the production technology is nonseparable, such reallocations, although leaving the marginal distribution of the reallocated input unchanged by construction, may nonetheless alter average output. Examples include reallocations of teachers across classrooms composed of students of varying mean ability. We focus on the effects of reallocating one input, while holding the assignment of another, potentially complementary, input fixed. We introduce a class of such reallocations -- correlated matching rules -- that includes the status quo allocation, a random allocation, and bot...
November 2008Identification and Estimation of 'Irregular' Correlated Random Coefficient Models
with James Powell: w14469
In this paper we study identification and estimation of the causal effect of a small change in an endogenous regressor on a continuously-valued 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 par...
October 2008Efficient Estimation of Missing Data Models Using Moment Conditions and Semiparametric Restrictions
w14376
This paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random (MAR) coincides with that of a particular augmented moment condition problem. The augmented system consists of the inverse probability weighted (IPW) original moment restriction and an additional conditional moment restriction which exhausts all other implications of the MAR assumption. The paper also investigates the value of additional semiparametric restrictions on the conditional expectation function (CEF) of the original moment function given always-observed covariates. In the missing outcome context, for example, such restrictions are implied by a semiparametric model for the outcome CEF given always-observed covariates. The efficienc...
May 2008Inverse Probability Tilting and Missing Data Problems
with Daniel Egel, Cristine Campos de Xavier Pinto: w13981
This paper outlines a new minimum empirical discrepancy (MD) estimator for missing data, sample combination and related problems: inverse probability tilting (IPT). Covered examples include estimation of the average treatment effect (ATE), the average treatment effect on the treated (ATT) and the two sample instrumental variables (TSIV) model. The proposed estimator attains the semiparametric efficiency bound under two auxiliary parametric restrictions (local efficiency), but is consistent so long as one or the other holds (double robustness). A novel feature of IPT is its 'exact balancing' property: after reweighting, sample moments of always-observed covariates in the complete-case subsample equal their corresponding (unweighted) full sample means. We also show how prior restrictions on ...

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