Evaluating probability forecasts in terms of refinement and strictly proper scoring rules

This note gives an easily verified necessary and sufficient condition for one probability forecaster to empirically outperform another one in terms of all strictly proper scoring rules. --probability forecasts,scoring rules,refinement

Long memory with Markov-Switching GARCH

The paper considers the Markov-Switching GARCH(1,1)-model with time-varying transition probabilities. It derives su?cient conditions for the square of the process to display long memory and provides some additional intuition for the empirical observation that estimated GARCH-parameters often sum to almost one. --Markov switching,GARCH,long memory

The cult of statistical significance. What economists should and should not do to make their data talk

This article takes issue with a recent book by Ziliak and McCloskey (2008) of the same title. Ziliak and McCloskey argue that statistical significance testing is a barrier rather than a booster for empirical research in economics and should therefore be abandoned altogether. The present article argues that this is good advice in some research areas but not in others. Taking all issues which have appeared so far of the German Economic Review and a recent epidemiological meta-analysis as examples, it shows that there has indeed been a lot of misleading work in the context of significance testing, and that at the same time many promising avenues for fruitfully employing statistical significance tests, disregarded by Ziliak and McCloskey, have not been used.

More on the F-test under nonspherical disturbances

We show that the F-test can be both liberal and conservative in the context of a particular type of nonspherical behaviour induced by spatial autocorrelation, and that the conservative variant is more likely to occur for extreme values of the spatial autocorrelation parameter. In particular, it will wipe out the progressive one as the sample size increases. --F-test,spatial autocorrelation

How to confuse with statistics or: the use and misuse of conditional probabilities

The article shows by various examples how consumers of statistical information may be confused when this information is presented in terms of conditional probabilities. It also shows how this confusion helps others to lie with statistics, and it suggests how either confusion or lies can be avoided by using alternative modes of conveying statistical information. --

OLS-based estimation of the disturbance variance under spatial autocorrelation

We investigate the OLS-based estimator s2 of the disturbance variance in the standard linear regression model with cross section data when the disturbances are homoskedastic, but spatially correlated. For the most popular model of spatially autoregressive disturbances, we show that s2 can be severely biased in finite samples, but is asymptotically unbiased and consistent for most types of spatial weighting matrices as sample size increases. --regression,spatial error correlation,bias,variance

The Power of the KPSS-Test for Cointegration when Residuals are Fractionally Integrated

We show that the power of the KPSS-test against integration, as measured by divergence rates of the test statistic under the alternative, remains the same when residuals from an OLS-regression rather than true observations are used. This is in stark contrast to residual based tests of the null of integration in a cointegration setting, where power is drastically reduced when residuals are used. --cointegration,power,long memory,KPSS-Test

Comparing the accuracy of default predictions in the rating industry: The case of Moody's vs. S&P

We consider 1927 borrowers from 54 countries who had a credit rating by both Moodys and S&P as of the end of 1998, and their subsequent default history up to the end of 2002. Viewing bond ratings as predicted probabilities of default, we show that it is unlikely that both agencies are well calibrated, and that the ranking of the agencies depends crucially on the way in which probability predictions are compared. --credit rating,probability forecasts,calibration

Large-scale disasters and the insurance industry

We investigate the effect of the 20 largest – in terms of insured losses – man-made or natural disasters on the insurance industry. We show via an event study that insurance markets worldwide are quite resilient to unexpected losses to capital and are even outperforming the general market subsequent to great disasters. --disaster,insurance industry,event-study

Structural Change and long memory in the GARCH(1,1)-model

It has long been known that the estimated persistence parameter in the GARCH(1,1) - model is biased upwards when the parameters of the model are not constant throughout the sample. The present paper explains the mechanics of this behavior for a particular class of estimates of the model parameters. It gives sufficient conditions for the estimated persistence to tend to one when the mean of the process changes, both for a given sample size (as the size of the structural change increases), and as sample size increases, extending previous results that were concerned with changes in the volatility parameters. --structural change,long memory,GARCH