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Weak Instruments (weak + instruments)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	100%

Distribution within Business, Economics, Finance and Accounting

Econometrics	50%

Selected Abstracts

Exponential Tilting with Weak Instruments: Estimation and Testing,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2010
Mehmet Caner
Abstract This article analyses exponential tilting estimator with weak instruments in a nonlinear framework. Our paper differs from the previous literature in the context of consistency proof. Tests that are robust to the identification problem are also analysed. These are Anderson,Rubin and Kleibergen types of test statistics. We also conduct a simulation study wherein we compare empirical likelihood and continuous updating-based tests with exponential tilting (ET)-based ones. The designs involve GARCH(1,1) and contaminated structural errors. We find that ET-based Kleibergen test has the best size among these competitors. [source]

Testing Parameters in GMM Without Assuming that They Are Identified

ECONOMETRICA, Issue 4 2005
Frank Kleibergen
We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic ,2 distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic ,2 distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series. [source]

Exponential Tilting with Weak Instruments: Estimation and Testing,

Assessing the magnitude of the concentration parameter in a simultaneous equations model

THE ECONOMETRICS JOURNAL, Issue 1 2009
D. S. Poskitt
Summary, This paper provides the practitioner with a method of ascertaining when the concentration parameter in a simultaneous equations model is small. We provide some exact distribution theory for a proposed statistic and show that the statistic possesses the minimal desirable characteristics of a test statistic when used to test that the concentration parameter is zero. The discussion is then extended to consider how to test for weak instruments using this statistic as a basis for inference. We also discuss the statistic's relationship to various other procedures that have appeared in the literature. [source]

Estimation with weak instruments: Accuracy of higher-order bias and MSE approximations

THE ECONOMETRICS JOURNAL, Issue 1 2004
Jinyong Hahn
Summary In this paper, we consider parameter estimation in a linear simultaneous equations model. It is well known that two-stage least squares (2SLS) estimators may perform poorly when the instruments are weak. In this case 2SLS tends to suffer from the substantial small sample biases. It is also known that LIML and Nagar-type estimators are less biased than 2SLS but suffer from large small sample variability. We construct a bias-corrected version of 2SLS based on the Jackknife principle. Using higher-order expansions we show that the MSE of our Jackknife 2SLS estimator is approximately the same as the MSE of the Nagar-type estimator. We also compare the Jackknife 2SLS with an estimator suggested by Fuller (Econometrica 45, 933,54) that significantly decreases the small sample variability of LIML. Monte Carlo simulations show that even in relatively large samples the MSE of LIML and Nagar can be substantially larger than for Jackknife 2SLS. The Jackknife 2SLS estimator and Fuller's estimator give the best overall performance. Based on our Monte Carlo experiments we conduct informal statistical tests of the accuracy of approximate bias and MSE formulas. We find that higher-order expansions traditionally used to rank LIML, 2SLS and other IV estimators are unreliable when identification of the model is weak. Overall, our results show that only estimators with well-defined finite sample moments should be used when identification of the model is weak. [source]

PENALIZED- R2 CRITERIA FOR MODEL SELECTION,

THE MANCHESTER SCHOOL, Issue 6 2009
LARRY W. TAYLOR
It is beneficial to observe that popular model selection criteria for the linear model are equivalent to penalized versions of R2. Let PR2 refer to any one of these model selection criteria. Then PR2 serves the dual purpose of selecting the model and summarizing the resulting fit subject to the penalty function. Furthermore, it is straightforward to extend the logic of PR2 to instrumental variables estimation and the non-parametric selection of regressors. For two-stage least squares estimation, a simulation study investigates the finite-sample performance of PR2 to select the correct model in cases of either strong or weak instruments. [source]