This paper gives a new jackknife estimator for instrumental variable inference with unknown heteroskedasticity. The estimator is derived by using a method of moments approach similar to the one that produces LIML in case of homoskedasticity. The estimator is symmetric in the endogenous variables including the dependent variable. Many instruments and many weak instruments asymptotic distributions are derived using high-level assumptions that allow for the simulataneous presence of weak and strong instruments for different explanatory variables. Standard errors are formulated compactly. We review briefly known estimators and show in particular that the symmetric jackknife estimator performs well when compared to the HLIM and HFUL estimators of Hausman et al. (2011) in Monte Carlo experiments.
This paper proposes novel inference procedures for instrumental variable models in the presence of many, potentially weak instruments that are robust to the presence of heteroskedasticity. First, we provide an Anderson–Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson–Rubin-type tests. Second, we consider the case of testing a subset of parameters under the assumption that a consistent estimator for the parameters not under test exists. We show that under the null, the proposed statistics have Gaussian limiting distributions and derive alternative chi-square approximations. An extensive simulation study shows the competitive finite sample properties in terms of size and power of our procedures. Finally, we provide an empirical application using college proximity instruments to estimate the returns to education.
This paper presents new evidence on the assessment of banks' cost efficiency gains stemming from ICT adoption. With respect to the existing literature we introduce two novelties. First, banking operating costs are explained in terms of a commonly used measure of IT innovation (the relative diffusion of ATMs) and a new variable defined as automated payment transactions. Second, the results obtained via standard parametric estimation methods are compared with those obtained via nonparametric estimation techniques. Using an original dataset of Italian banks observed in the period 2006-2010, we do not find clear cost efficiency enhancing effects due to ATMs diffusion. On the other hand, the diffusion of electronic payments shows a significant effect in terms of cost inefficiency reduction.(1 Regarding the screening of European banks, Ayadi et al. (2012) find that 'diversified retail' banks (using diversified sources of funding and providing predominantly customer loans) are safer than others allowing for lower default probability and long-term liquidity risks.
In this paper we propose a blockwise Euclidean likelihood method for the estimation of a spatial binary field obtained by thresholding a latent Gaussian random field. The moment conditions used in the Euclidean likelihood estimator derive from the score of the composite likelihood based on marginal pairs. A feature of this approach is that it is possible to obtain computational benefits with respect to the pairwise likelihood depending on the choice of the spatial blocks. A simulation study and an analysis on cancer mortality data compares the two methods in terms of statistical and computational efficiency. We also study the asymptotic properties of the proposed estimator
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