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

About sense and nonsense of non- and semiparametric analysis in applied econometrics

The discussion about the use of semiparametric analysis in empirical research in economics is as old as the methods are. This article can certainly not be more than a small contribution to the polemic question how useful is non- or semiparametric statistics for applied econometrics. The goal is twofold: to illustrate that the use of these methods have their justification in economics, and to highlight what might be reasons for the lack of its application in empirical research. We do not give a survey of available methods and procedures. Since we discuss the question of the use of non- or semiparametric methods (in economics) in general, we believe that it is fair enough to stick to kernel smoothing methods. It might be that we will face some deficiencies that are more typical in the context of kernel smoothing than it is for other methods. However, the different smoothing methods share mainly the same advantages and disadvantages we will discuss. Even though many points of this discussion hold also true for other research fields, all our examples are either based on economic data sets or concentrate on models that are typically motivated from economic or econometric theory

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

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

Observation of bi-polarons in blends of conjugated copolymers and fullerene derivatives

From a fundamental and application point of view it is of importance to understand how charge carrier generation and transport in a conjugated polymer (CP):fullerene blend are affected by the blend morphology. In this work light-induced electron spin resonance (LESR) spectra and transient ESR response signals are recorded on non-annealed and annealed blend layers consisting of alkyl substituted thieno[3,2-b]thiophene copolymers (pATBT) and the soluble fullerene derivative [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) at temperatures ranging from 10 to 180 K. Annealing of the blend sample leads to a reduction of the steady state concentration of light-induced PCBM anions within the blend at low temperatures (T = 10 K) and continuous illumination. This is explained on the basis of the reducing interfacial area of the blend composite on annealing, and the high activation energy for electron diffusion in PCBM blends leading to trapped electrons near the interface with the CP. As a consequence, these trapped electrons block consecutive electron transfer from an exciton on a CP to the PCBM domain, resulting in a relatively low concentration charge carriers in the annealed blend. Analysis of the transient ESR data allows us to conclude that in annealed samples diamagnetic bi-polaronic states on the CPs are generated at low temperature. The formation of these states is related to the generation and interaction of multiple positive polarons in the large crystalline polymer domains present in the annealed sample

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