We present a minimum distance approach for conducting hypothesis testing in the presence of potentially weak instruments. Under this approach, we propose size correct tests for limited dependent variable models with endogenous explanatory variables such as endogenous Tobit and probit models....

In this paper, we develop methods of the determination of the rank of random matrix. Using the matrix perturbation theory to construct or find a suitable bases of the kernel (null space) of the matrix and to determine the limiting distribution of the estimator of the smallest singular values. We...

We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables for the...

In this paper, we propose methods of the determination of the rank of matrix. We consider a rank test for an unobserved matrix for which an estimate exists having normal asymptotic distribution of order N1/2 where N is the sample size. The test statistic is based on the smallest estimated...

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to...

We study several tests for the coefficient of the single right-hand-side endogenous variable in a linear equation estimated by instrumental variables. We show that writing all the test statistics -- Student's t, Anderson-Rubin, the LM statistic of Kleibergen and Moreira (K), and likelihood ratio...

We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of...

We investigate confidence intervals and inference for the instrumental variables model with weak instruments. Wald-based confidence intervals perform poorly in that the probability they reject the null is far greater than their nominal size. In the worst case, Wald-based confidence intervals...

A correction factor, depending on sample size and parameters, is found for the likelihood ratio test for some linear hypotheses on the cointegrating space in a vector autoregressive model, where the adjustment coefficients are known. The main idea is to condition on the common trends when making...

Using examples drawn from two important papers in the recent literature on weak instruments, we demonstrate how observed experimental outcomes can be pro- foundly inuenced by the dierent conceptual frameworks underlying two exper- imental designs commonly employed when simulating simultaneous...