The chapter discusses generalized method of moments (GMM) estimation methods for spatial models. Much of the discussion is on GMM estimation of Cliff-Ord-type models where spatial interactions are modeled in terms of spatial lags. The chapter also discusses recent developments on GMM estimation from data processes which are spatially α-mixing. I.R. Prucha Department of Economics, University of ...

Heterogeneous Treatment Effects: Instrumental Variables without Monotonicity? A fundamental identification problem in program evaluation arises when idiosyncratic gains from participation and the treatment decision depend on each other. Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify an average treatment effect parameter using instrumental varia...

We consider a method for extending instrumental variables methods in order to estimate the overall effect of a treatment or exposure. The approach is designed for settings in which the instrument influences both the treatment of interest and a secondary treatment also influenced by the primary treatment. We demonstrate that, while instrumental variables methods may be used to estimate the joint...

function Find-Simple-IVs(G, X, Y ) Let B be all nodes d-separated from Y in G Let D = De(An(X)) Construct a flow graph F (G) with nodes: {V + | V ∈ D} ∪ {V |V ∈ An(X)} ∪ {S, Y } and edges: {V + → W | V,W ∈ An(D) and V ← W ∈ E} ∪ {V + → V − | V ∈ An(X)} ∪ {V − → W | V,W ∈ An(X) and V → W ∈ E} ∪ {S → V + | V ∈ (D ∩M ∩B)} ∪ {X → Y | X ∈ X} Assign capacities to nodes in G: infinite capacity to S, Y...

Abstract Instrumental variables are often associated with low estimator precision. This paper explores efficiency gains which might be achievable using moment conditions which are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a ...

To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of unfamiliarity or inapplicability. By introducing the method in a nontechnical way, we show that IV in a linear model is quite easy to understand and easy to apply once an appropriate instrumental variable has been identified. We also point out...

Poskitt and Skeels (2003) provide a new approximation to the sampling distribution of the IV estimator in a simultaneous equations model, the approximation is appropriate when the concentration parameter associated with the reduced form model is small. A basic purpose of this paper is to provide the practitioner with easily implemented inferential tools based upon extensions to these small conc...

Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage is proposed. This is particularly relevant in applications in which there is a large number of candidate instruments and, therefore, a considerable ...

Econometric analyses of treatment response often use instrumental variable (IV) assumptions to identify treatment effects. The traditional IV assumption holds that mean response is constant across the subpopulations of persons with different values of an observed covariate. Manski and Pepper (2000) introduced monotone instrumental variable (MIV) assumptions, which replace equalities with weak i...

Instrumental Variables are a popular way to identify the direct causal effect of a random variable X on a variable Y . Often no single instrumental variable exists, although it is still possible to find a set of generalized instrumental variables (GIVs) and identify the causal effect of all these variables at once. Till now it was not known how to find GIVs systematically or even test efficient...