SUMMARYUsing both semiparametric and parametric estimation methods, this paper corroborates earlier findings of fractionally integrated behaviour in the forward premium. Two new explanations are also proposed to help reconcile earlier conflicting empirical evidence on the time series properties of the forward premium. Traditional regression approaches used to test the forward rate unbiasedness hypothesis are then evaluated, including regression in levels, in returns (Fama's, 1984, regression), and in error-correction format. Interesting statistical and/or interpretive implications are found in all three cases. For example, the predictions of the appropriate nonstandard limit theory are consistent with many of the standard empirical results reported from Fama's regression, including the commonly occurring, yet puzzling negative correlations between spot returns and the forward premium. It is suggested that the principal failure of unbiasedness, may be due instead to the difference in persistence between these two series.

Several recent empirical studies have been forced to reject exact 1:1 cointegration between spot and forward exchange rates. Theoretically, this is shown to provide a possible explanation for the puzzling negative estimates reported from spot-return-forward-premium regressions. In particular, the coefficient in this regression has a unit root component in its limit distribution that imparts a bias and skewness to the estimator. Simulations are used to demonstrate how even very small deviations from 1:1 cointegration can result in substantial bias. The empirical evidence suggests that the implied Dickey-Fuller-type terms do exhibit a downward bias, yet are of insufficient magnitude to fully account for the puzzling regression coefficients mentioned above. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

SUMMARYThis paper employs a recently developed instrumental quantile regression method to investigate the effect of Medicaid on household savings across different wealth groups. It finds that the disincentive effect of Medicaid on household savings is heavily concentrated in the middle net-worth households. In contrast, the effects on the bottom and top net-worth households are quite small and insignificant. These heterogeneous incentive effects are partly explained by Medicaid asset tests. Our findings hold regardless of whether affluence is measured in terms of wealth or income. They suggest that despite generating substantial crowd-out for the mean household, Medicaid expansions are unlikely to discourage the savings of the poorest households.

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