Testing for Granger causality in the presence of measurement errors

In this paper a potential problem with tests for Granger-causality is investigated. If one of the two variables under study, but not the other, is measured with error the consequence is that tests of forecastablity of the variable without measurement error by the variable with measurement error will be rejected less often than it should. Since this is not the case for the test of forecastability of the variable with measurement error by the one without there is a danger of concluding that one variable leads the other while it is in fact a feed-back relationship. The problem is illustrated by an example.Granger causality

Searching for the DGP when forecasting - Is it always meaningful for small samples?

In this paper the problem of choosing a univariate forecasting model for small samples is investigated. It is shown that, a model with few parameters, frequently, is better than a model which coincides with the data generating process (DGP) (with estimated parameter values). The exponential smoothing algorithms are, once more, shown to perform remarkably well for some types of data generating processes, in particular for short-term forecasts. All this is shown by means of Monte Carlo simulations and a time series of realized volatility from the CAC40 index. The results speaks in favour of a negative answer to the question posed in the title of this paper.Forecasting

A regression surprise resolved

In this note we explore the following surprising fact: In regression with trend and seasonality, the prediction risk is constant for all seasons of a new cycle, despite the fact that it increases with time when the seasons are left out. Awareness of this may be useful to both the practicing statistician and to teachers of statistics. The challenge of resolving the issue may also be given to students of statistics as a research project.Trend and seasonality; Prediction risk; Paradox

Treating missing values in INAR(1) models

Time series models for count data have found increased interest in recent days. The existing literature refers to the case of data that have been fully observed. In the present paper, methods for estimating the parameters of the first-order integer-valued autoregressive model in the presence of missing data are proposed. The first method maximizes a conditional likelihood constructed via the observed data based on the k-step-ahead conditional distributions to account for the gaps in the data. The second approach is based on an iterative scheme where missing values are imputed in order to update the estimated parameters. The first method is useful when the predictive distributions have simple forms. We derive in full details this approach when the innovations are assumed to follow a finite mixture of Poisson distributions. The second method is applicable when there are not closed form expressions for the conditional likelihood or they are hard to derive. Simulation results and comparisons of the methods are reported. The proposed methods are applied to a data set concerning syndromic surveillance during the Athens 2004 Olympic Games.Imputation; Markov Chain EM algorithm; mixed Poisson; discrete valued time series

Some aspects of random utility, extreme value theory and multinomial logit models

In this paper we give a survey on some basic ideas related to random utility, extreme value theory and multinomial logit models. These ideas are well known within the field of spatial economics, but do not appear to be common knowledge to researchers in probability theory. The purpose of the paper is to try to bridge this gap.Random utility theory; extreme value theory; multinomial logit models; entropy.

A simple improvement of the IV estimator for the classical errors-in-variables problem

Two measures of an error-ridden explanatory variable make it possible to solve the classical errors-in-variable problem by using one measure as an instrument for the other. It is well known that a second IV estimate can be obtained by reversing the roles of the two measures. We explore a simple estimator that is the linear combination of these two estimates, that minimizes the asymptotic mean squared error. In a Monte Carlo study we show that the gain in precision is significant compared to using only one of the original IV estimates. The proposed estimator also compares well with full information maximum likelihood under normality.Measurement errors; Classical Errors-in-Variables; multiple indicator method; Instrumental variable techniques

Journal Staff

The aim of this study was to examine the association between autobiographical memory specificity and future thinking in a depressed sample. A total of 88 individuals who meet the DSM-IV criteria of major depression were included and completed the autobiographical memory test (AMT) and the future thinking task (FTT). The FTT was an index of number of future plausible events, rating of likelihood and emotional valence. The results showed that positive future thinking was significantly correlated with retrieval of specific positive autobio-graphical memories (r = 0.23). Moreover, correlational analyses showed that positive autobiographical memo-ries were negatively correlated with extended autobiographical memories, repeated autobiographical memories, semantic associations and non-responses on the AMT. Self-report measures of depression and anxiety were not correlated with either the FTT or the AMT. The results of this cross-sectional study only give weak support for an association between autobiographical memory specificity and future thinking

Services on the Mobile Internet Service-Driven Technology Development

The purpose of this thesis is to describe and analyze the services on the mobile Internet and to identify when, where, and how these service offerings can be profitable. The thesis is a qualitative study based on in-depth interviews with key stakeholders in the service value chain and with relevant experts. Case methodology have been used to gather and structure the empirical material from three cases, i-mode in Japan, SMS in Europe, and BlackBerry in the USA to be able to generate new theory as well as expand on existing theory. The analysis, based on the three cases, generates and validates a model for successful service strategy on the mobile Internet. The service strategy is called service-driven technology development. Service-driven technology development explains the success of the three cases and provides a framework to successfully develop, implement, and maintain profitable services on the mobile Internet

Grid Integration Costs of Fluctuating Renewable Energy Sources

The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule uncertainty that the system operator incurs due to the obligation to always provide sufficient control reserve capacity for power imbalance mitigation. This paper studies the situation for the German power system and the existing German RES support schemes. The profile service costs incurred by German Transmission System Operators (TSOs) are quantified and means for cost reduction are discussed. In general, profile service costs are dependent on the RES prediction error and the specific workings of the power markets via which the prediction error is balanced. This paper shows both how the prediction error can be reduced in daily operation as well as how profile service costs can be reduced via optimization against power markets and/or active curtailment of RES generation.Comment: Accepted for SUSTECH 2014, Portland, Oregon, USA, July 201