C13 Estimation
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- Asset pricing (1)
- Cross-section of expected returns (1)
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When estimating misspecified linear factor models for the cross-section of expected returns using GMM, the explanatory power of these models can be spuriously high when the estimated factor means are allowed to deviate substantially from the sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. The mathematically correct global minimum of the GMM objective function can be obtained at a parameter vector that is far from the true parameters of the data-generating process. This property is not restricted to small samples, but rather holds in population. It is a feature of the GMM estimation design and applies to both strong and weak factors, as well as to all types of test assets.
The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.
This paper evaluates the effects of job creation schemes on the participating individuals in Germany. Since previous empirical studies of these measures have been based on relatively small datasets and focussed on East Germany, this is the first study which allows to draw policy-relevant conclusions. The very informative and exhaustive dataset at hand not only justifies the application of a matching estimator but also allows to take account of threefold heterogeneity. The recently developed multiple treatment framework is used to evaluate the effects with respect to regional, individual and programme heterogeneity. The results show considerable differences with respect to these sources of heterogeneity, but the overall finding is very clear. At the end of our observation period, that is two years after the start of the programmes, participants in job creation schemes have a significantly lower success probability on the labour market in comparison to matched non-participants.