Diffusion of language change: Accommodation to a moving target

The paper focuses on motivations for the spread of new features within a speech community, and on the trajectories the changes follow during diffusion. One set of data represents language use, and here focus is on two changes which have been going on in Danish over the past 40 years, one grammatical and one phonetic. The other set of data are results from a nationwide speaker evaluation experiment which tests the subconscious attitudes to different types of speech among the youth in five different places covering Denmark from east to west. Results show that changes spread centrifugally from Copenhagen, even to the extent that reversal of changes spreads from Copenhagen. Furthermore, the attitudes reflected in the speaker evaluation experiment support the theory that language change is motivated by social psychological factors. Finally, it is argued that it is worthwhile considering the possibility of media being involved in processes of linguistic change.     

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of meteorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric approach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assumption is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive estimation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covariance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomial regression with orthogonal fit to accurately approximate the target regression, even though it may hardly be visible when calculating error criteria against corrupted data.     

Estimation in the Positive Stable Shared Frailty Cox Proportional Hazards Model

Shared frailty models are of interest when one has clustered survival data and when focus is on comparing the lifetimes within clusters and further on estimating the correlation between lifetimes from the same cluster. It is well known that the positive stable model should be preferred to the gamma model in situations where the correlated survival data show a decreasing association with time. In this paper, we devise a likelihood based estimation procedure for the positive stable shared frailty Cox model, which is expected to obtain high efficiency. The proposed estimator is provided with large sample properties and also a consistent estimator of standard errors is given. Simulation studies show that the estimation procedure is appropriate for practical use, and that it is much more efficient than a recently suggested procedure. The suggested methodology is applied to a dataset concerning time to blindness for patients with diabetic retinopathy.     

Modeling and Forecasting Realized Volatility

We provide a framework for integration of high–frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Building on the theory of continuous–time arbitrage–free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen/Dollar spot exchange rates, we find that forecasts from a simple long–memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal–normal mixture distribution produces well–calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation, and financial risk management applications.     

Sampling Adjusted Analysis of Dynamic Additive Regression Models for Longitudinal Data

We consider a modelling approach to longitudinal data that aims at estimating flexible covariate effects in a model where the sampling probabilities are modelled explicitly. The joint modelling yields simple estimators that are easy to compute and analyse, even if the sampling of the longitudinal responses interacts with the response level. An incorrect model for the sampling probabilities results in biased estimates. Non-representative sampling occurs, for example, if patients with an extreme development (based on extreme values of the response) are called in for additional examinations and measurements. We allow covariate effects to be time-varying or time-constant. Estimates of covariate effects are obtained by solving martingale equations locally for the cumulative regression functions. Using Aalen's additive model for the sampling probabilities, we obtain simple expressions for the estimators and their asymptotic variances. The asymptotic distributions for the estimators of the non-parametric components as well as the parametric components of the model are derived drawing on general martingale results. Two applications are presented. We consider the growth of cystic fibrosis patients and the prothrombin index for liver cirrhosis patients. The conclusion about the growth of the cystic fibrosis patients is not altered when adjusting for a possible non-representativeness in the sampling, whereas we reach substantively different conclusions about the treatment effect for the liver cirrhosis patients.     

Is there a trend towards global value chain specialization? — An examination of cross border sales of US foreign affiliates

In this study we discuss and empirically test the assertion that over the last two decades multinational enterprises' (MNEs') configuration of value-adding activities has shifted from a sparse and simple (host–home) international division of labor among the foreign affiliates to a more specialized and ‘advanced’ global value chain configuration in which MNEs locate fine-sliced parts of the value chain at the most efficient locations. Using data on trade flows of U.S. affiliates in 56 host countries between 1983 and 2003 we find some indications of a trend in the direction of global value chain specialization. In particular among US affiliates in developing countries the proportion of host–host, intra-firm trade has increased significantly during the observed period of time. Conversely, the proportion of host–home and inter-firm trade has diminished. We interpret this as indicating both value chain disaggregation (vertical specialization) and MNEs' systematic exploitation of factor cost differentials across countries. We also find that the absolute levels of all types of trade flows have increased. Hence, it is the relative, and not the absolute, changes in the trade flow patterns of US affiliates that gives credibility to the global value chain assertion.     

Ownership Structure and Value of the Largest European Firms: The Importance of Owner Identity

The paper examines the relationship betweenownership structure and value of the largestEuropean firms. Using simultaneous estimationand controlling for nation and industry effectswe find that ownership concentration (measuredby the fraction of ``closely held' shares) hasa positive effect on firm value (market-to-bookvalue of equity), when the largest owner is afinancial institution or another corporation. If the largest owner is a family or a singleindividual, ownership concentration has noeffect on firm value, and the effect isnegative if the largest owner is a governmentorganisation. Firm value is found to have apositive feedback effect on ownershipconcentration except for governments, whichhold higher stakes in low-value firms. Inother words, owner-identity matters,particularly in a Continental Europeaninstitutional setting where ownershipconcentration is high and minority investorprotection is low.     

Appeals to social norms as policy instruments to address consumption externalities

We consider appeals to social norms as a policy instrument to address consumption externalities. We explore whether appeals to social norms can be an efficient policy instrument and compare the efficiency of such appeals to the efficiency of taxation in addressing consumption externalities. We find that when the existing norm helps to shift consumption towards the socially optimal level of consumption, taxation welfare dominates appeals to social norms as a policy tool. While previous studies have found that economic instruments are superior to information in other contexts, we arrive at a different conclusion for situations where the norm shifts behavior away from what is socially optimal. In such cases appeals to social norms can be a better policy instrument than taxation.     

Testing the Intransitivity Explanation of the Allais Paradox

This paper uses a two-dimensional version of a standard common consequence experiment to test the intransitivity explanation of Allais-paradox-type violations of expected utility theory. We compare the common consequence effect of two choice problems differing only with respect to whether alternatives are statistically correlated or independent. We framed the experiment so that intransitive preferences could explain violating behavior when alternatives are independent, but not when they are correlated. We found the same pattern of violation in the two cases. This is evidence against intransitivity as an explanation of the Allais Paradox. The question whether violations of expected utility are mainly due to intransitivity or to violation of independence is important since it is exactly on this issue the main new decision theories differ.