Prediction of stocks: a new way to look at it.

While the traditional $R^{2}$ value is useful to evaluate the quality of a fit, it does not work when it comes to evaluating the predictive power of estimated financial models in finite samples. In this paper we introduce a validated $R_{V}^{2}$ value that is Taylor made for prediction. Based on data from the Danish stock market, using this measure we find that the dividend-price ratio has good predictive power for time horizons between one year and five years. We explain how the $R_{S}^{2}$ s for different time horizons could be compared, respectively, how they must not be interpreted. For our data we can conclude that the quality of prediction is almost the same for the five different time horizons. This is in contradiction to earlier studies based on the traditional $R^{2}$ value, where it has been argued that the predictive power increases with the time horizon up to a horizon of about five or six years. Furthermore, we find that while inflation and interest rate do not add to the predictive power of the dividend-price ratio then last years excess stock return does

Non-uniformity of job-matching in a transition economy- a nonparametric analysis for the czech republic

In this study, we explore the properties and development of the matching technology in the Czech Republic during the transition to a market economy. Nonparametric additive modelling allows us assess flexible functional forms, which comprise for instance CES and translog specifications. This enable us to evaluate the matching process locally for each combination of unemployment vacancies rather than being restricted to global coefficients. Special interest is devoted to analysis and economic determinants of regional variation in the returns to scale of the marching function. We find non-linearities in the partial adjustment process of unemployment outflows, and a negative coefficient on vacancies in some years. Moreover, we find locally increasing returns to scale in job-marching. Returns to scale are found to be negatively correlated to the share in employment in services and to outmigration, positively correlated to the employment share in industry, the unemployment rate and various measures of active labor market policies

Estimation of a semiparametric transformation model

This paper proposes consistent estimators for transformation parameters in semiparametric models. The problem is to find the optimal transformation into the space of models with a predetermined regression structure like additive or multiplicative separability. We give results for the estimation of the transformation when the rest of the model is estimated non- or semi-parametrically and fulfills some consistency conditions. We propose two methods for the estimation of the transformation parameter: maximizing a profile likelihood function or minimizing the mean squared distance from independence. First the problem of identification of such models is discussed. We then state asymptotic results for a general class of nonparametric estimators. Finally, we give some particular examples of nonparametric estimators of transformed separable models. The small sample performance is studied in several simulations.Comment: Published in at http://dx.doi.org/10.1214/009053607000000848 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Structural adaptive dimension reduction

The paper introduces and discusses different estimation methods for multi index models where the indices are parametric and the link function is nonparametric. More specific, the here introduced methods follow the idea of Hristache et al. (2001), modify and try to improve it. Moreover, they constitute alternatives to the so called MAVE-based methods (Xia et al, 2002). We concentrate on an intuitive presentation of what each procedure is doing to the data and its implementation. All methods considered here we have made freely available in R. We conclude with a comparative simulation study based on the provided package EDR

Estimation of Derivates for Additive Separable Models

Additive regression models have a long history in nonparametric regression. It is well known that these models can be estimated at the one dimensional rate. Until recently, however, these models have been estimated by a backfitting procedure. Although the procedure converges quickly, its iterative nature makes analyzing its statistical properties difficult. Furthermore it is unclear how to estimate derivatives with this approach since it does not give a closed form for the estimator. Recently, an integration approach has been studied that allows for the derivation of a closed form for the estimator. This paper extends this approach to the simultaneous estimation of both the function and its derivatives by combining the integration procedure with a local polynomial approach. Finally the merits of this procedure with respect to the estimation of a production function subject to separability conditions are discussed. The procedure is applied to livestock production data from Wisconsin. It is shown that there is some evidence of increasing return to scale for larger farms

Comparison of separable components in different samples

Imagine we have two different samples and are interested in doing semi- or nonparametric regression analysis in each of them, possibly on the same econometric model. In this article we consider the problem of testing whether a specific covariate has different impacts on the regression curve in these two samples. So we compare regression curves of different samples but being interested in specific differences instead of testing for equality of the whole regression function. Our procedure does not only allow for random designs and different sample sizes but also for different variance functions, different sets of regressors with different impact functions, etc. Actually, it is as general as the comparison of particular coefficients in different parametric regression models but now on the level of (nonparametric) functionals. As we use the marginal integration approach this method can be applied to any strong, weak or latent separable model to compare the (lower dimensional) separable components between the different samples. Thus, in the case of separable models our procedure includes the possibility of comparing the whole regression curves avoiding the curse of dimensionality that otherwise would render such a task impractical. In practice, resampling methods are necessary for applying our test to real data. However, it will be shown that for our approach bootstrap fails in practice and theory. Instead, we propose a subsampling procedure with automatic parameter choice. We give complete asymptotic theory, and its excellent performance is demonstrated by an extensive simulation study. --comparison of regression curves,nomparametric testing,subsampling,bootstrap,marginal effects,separable models