A test of long memory hypothesis based on self-similarity

This paper develops a new test of true versus spurious long memory, based on log-periodogram estimation of the long memory parameter using skip-sampled data. A correction factor is derived to overcome the bias in this estimator due to aliasing. The procedure is designed to be used in the context of a conventional test of significance of the long memory parameter, and composite test procedure described that has the properties of known asymptotic size and consistency. The test is implemented using the bootstrap, with the distribution under the null hypothesis being approximated using a dependent-sample bootstrap technique to approximate short-run dependence following fractional differencing. The properties of the test are investigated in a set of Monte Carlo experiments. The procedure is illustrated by applications to exchange rate volatility and dividend growth series.

Poor and Sick: Estimating the relationship between Household Income and Health

This study evaluates the effect of the individual‘s household income on their health at the later stages of working life. A structural equation model is utilised in order to derive a composite and continuous index of the latent health status from qualitative health status indicators. The endogenous relationship between health status and household income status is taken into account by using IV estimators. The findings reveal a significant effect of individual household income on health before and after endogeneity is taken into account and after a host of other factors which is known to influence health, including hereditary factors and the individual‘s locus of control. Importantly, it is also shown that the childhood socioeconomic position of the individual has long lasting effects on health as it appears to play a significant role in determining health during the later stages of working life.

The Difference, System and ‘Double-D’ GMM Panel Estimators in the Presence of Structural Breaks

The effects of structural breaks in dynamic panels are more complicated than in time series models as the bias can be either negative or positive. This paper focuses on the effects of mean shifts in otherwise stationary processes within an instrumental variable panel estimation framework. We show the sources of the bias and a Monte Carlo analysis calibrated on United States bank lending data demonstrates the size of the bias for a range of auto-regressive parameters. We also propose additional moment conditions that can be used to reduce the biases caused by shifts in the mean of the data.