An important aspect of income distribution is the modelling of the data using an appropriate parametric model. This involves estimating the parameters of the models, given the data at hand. Income data are typically in grouped form. Moreover, they are not always reliable in that they may contain...

We present a unified treatment of different types of one-step M-estimation in regression models which incorporates the Newton–Raphson, method of scoring and iteratively reweighted least squares forms of one-step estimator. We use higher order expansions to distinguish between the different...

Using a latent variables approach, we estimate the dynamics of dividends and returns in a tractable present-value model with time-varying risks. Expected returns imply a similar return predictability as under homoskedasticity, while expected dividend growth is more persistent and explains a...

Conventional tests of present-value models over-reject the null of no predictability. In order to better account for the intrinsic probability of detecting predictive relations by chance alone, we develop a new nonparametric Monte Carlo testing method, which does not rely on distributional...

Asymptotic tests over-reject the null of no predictability in present-value models. We develop a nonparametric testing approach in state space models, implying reliable finite sample inference under weak assumptions on price-dividend ratio and dividend shocks. We find sharp evidence of return...

Asymptotic tests over-reject the null of no predictability in present-value models. We develop a nonparametric testing approach in state space models, implying reliable finite sample inference under weak assumptions on price-dividend ratio and dividend shocks. We find sharp evidence of return...