Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests which instead have power against generic nonlinear alternatives. A Monte Carlo study shows...

In this paper, we empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, we carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the...

Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests that have power against generic nonlinear alternatives. A Monte Carlo study shows that the...

This paper estimates determinants of long-run growth rates of GDP per capita in a cross section of countries. We propose a novel Measurement Error Model Averaging (MEMA) approach that accounts for measurement error in international income data as well as model uncertainty. Estimating the model...

An economic agent who is uncertain of her model updates her beliefs in response to the data. The updating is sensitive to measurement error which, in many cases of macroeconomic interest, is apparent from the process of data revision. I make this point through simple illustrations and then...

This paper discusses how specification of probabilistic models for multistate duration data generated by individual choices should be justified on a priori theoretical grounds. Preferences are assumed represented by random utilities, where utilities are viewed as random also to the agent...

In this paper we provide a brief overview of some of the most recent empirical research on spatial econometric models and spatial data mining. Data mining in general is the search for hidden patterns that may exist in large databases. Spatial data mining is a process to discover interesting,...

We address the bias from using potential vs. actual experience in earnings models. Statistical tests reject the classical errors-in-variable framework. The nature of the measurement error is best viewed as a model misspecification problem. We correct for this by modeling actual experience as a...