We examine cross-sectional empirical evidence on the determinants of economic growth in light of an instrumental variables estimator, based on sample moments of order higher than two, which does not require extraneous instruments and which remains consistent, under quite reasonable assumptions,...

A Direct Monte Carlo (DMC) approach is introduced for posterior simulation in theInstrumental Variables (IV) model with one possibly endogenous regressor, multipleinstruments and Gaussian errors under a flat prior. This DMC method can also beapplied in an IV model (with one or multiple...

This paper presents estimates from a latent variables model of the relation between corporate governance and financial performance. We use data on large US corporations to estimate the correlation, conditional on the firms' investment opportunity set, between governance and performance. We find...

The measurement error problem in linear time series regression, with focus on the impact of error memory, modeled as nite-order MA processes, is considered. Three prototype models, two bivariate and one univariate ARMA, and ways of handling the problem by using instrumental variables (IVs) are...

Estimation of polynomial regression equations in one error-ridden variable and a number of error-free regressors, as well as an instrument set for the former is considered. Procedures for identification, operating on moments up to a certain order, are elaborated for single- and multi-equation...

This paper analyses the ability of beta and other factors, like firm size and book-to-market, to explain cross-sectional variation in average stock returns on the Swedish stock market for the period 1980-1990. We correct for errors in variables problem of the estimated market beta. Since this...

A model with proportional errors in variables arising naturally in microeconomics is considered. Unlike the classical additive errors case, all OLS parameter estimates exhibit attenuation bias that does not depend on the limiting distribution of the data. The distribution of OLS estimators is...

We provide an example for an errors in variables problem which might be often neglected but which is quite common in lab experimental practice: In one task, attitude towards risk is measured, in another task participants behave in a way that can possibly be explained by their risk attitude. How...

We provide an example for an errors in variables problem which might be often neglected but which is quite common in lab experimental practice: In one task, attitude towards risk is measured, in another task participants behave in a way that can possibly be explained by their risk attitude. How...

Because the permanent incomes of parents and children are typically unobserved, the estimation of the intergenerational correlation via the use of proxy variables entails an errors-in-variables bias. By solving a system of moment equations for income observed at a given year, and a T-period...