Analysis of Austrian stocks: Testing for stability and randomness

This paper is concerned with subjecting two popular assumptions about the behavior of stock market prices to empirical tests: first, the random walk hypothesis developed by Bachelier (1900), Osborne (1959), and Mandelbrot (1963); second, the stable distributions hypothesis by Mandelbrot (1963) and Fama (1965). For this purpose, ten time series from the Vienna Stock Exchange were used. The first hypothesis was tested using both non-parametric and parametric methods. To obtain evidence with regard to the seond hypothesis, a graphical procedure and statistical estimation on the basis of the empirical characteristic function were applied. On analysis of our data, it turned out that, at least for the time period under consideration (1985–1990), severe doubts are cast on the above assumptions.We gratefully acknowledge the help of Peter Mitter, Institute for Advanced Studies, and Franz Köstl, Österreichische Kontrollbank, who provided us with the necessary data, and the comments of the anonymous referees.     

Analyzing a panel of seasonal time series: Does seasonality in industrial production converge across Europe?

In this paper we consider deterministic seasonal variation in quarterly industrial production for several European countries, and we address the question whether this variation has become more similar across countries over time. Due to economic and institutional factors, one may expect convergence across business cycles. When these have similar characteristics as seasonal cycles, one may perhaps also find convergence in seasonality. To this aim, we propose a method that is based on treating the set of production series as a panel. By testing for the relevant parameter restrictions for moving window samples, we examine the hypothesis of convergence in deterministic seasonality while allowing for seasonal unit roots. Our main empirical finding is that there is no evidence for convergence in seasonality.     

A forecasting comparison of some var techniques

Higher dimensional multivariate time series models suffer from the problem of over-parametrisation which impairs their forecasting performance. Starting from such unrestricted vector autoregressive models the paper discusses two ways to cope with this difficulty. The first approach reduces the number of free parameters by applying a subset modelling strategy. The second approach takes a Bayesian point of view by formulating ‘priors’ which are then combined with sample information, but leaving the original specification unaltered. Using Austrian quarterly macroeconomic time series a comparative study is undertaken by running alternative forecasting exercises. Both methods improve out-of-sample forecasting performance substantially at the cost of some bias in ex-post simulations. Comparing the ex-ante predictions of the two approaches, the former does better at short horizons whereas the latter gains as the forecast horizon lengthens.     

Testing for Seasonal Unit Roots in Monthly Panels of Time Series*

We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly cross‐sectionally augmented Hylleberg–Engle–Granger–Yoo (CHEGY) test to the monthly case. This parametric test is contrasted with a new non‐parametric test, which is the panel counterpart to the univariate record unit–root seasonal (RURS) test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data.     

The dynamic interrelations between unequal neighbors: an Austro-German case study

This article investigates the effects and the transmission of shocks between asymmetric neighboring countries. In particular, we investigate Austria and Germany which are highly integrated due to their common language and common membership of the European Monetary Union. Generalized impulse response functions reveal large and significant effects of shocks to the German economy on Austria. By contrast, the effects of shocks to the Austrian economy on Germany are barely significant and if they are, their magnitude is small. Furthermore, we can show that multiplier effects exist in Germany but not in Austria and we identify hysteretic properties in Austrian unemployment.     

On the Role of Seasonal Intercepts in Seasonal Cointegration

In the paper we consider the role of seasonal intercepts in seasonal cointegration analysis. For the nonseasonal unit root, such intercepts can generate a stochastic trend with a drift common to all observations. For the seasonal unit roots, however, we show that unrestricted seasonal intercepts generate trends that are different across the seasons. Since such seasonal trends may not appear in economic data, we propose a modified empirical method to test for seasonal cointegration. We evaluate our method using Monte Carlo simulations and using a four-dimensional data set of Austrian macroeconomic variables.     

Competing specifications of the gravity equation: a three-way model,bilateral interaction effects,or a dynamic gravity model with time-varying country effects?

We investigate bilateral trade flows across the EU-15 countries from 1962 to 2003 by different specifications of the gravity model. We augment the basic gravity model with population and exchange rate variables, and then include time-varying country fixed effects, to account for Anderson and van Wincoop (Am Econ Rev 93(1):170–192, 2003) multilateral resistance terms. Then, following the previous theoretical derivations of the gravity model in the presence of panel data in a dynamic setting we change the specification of our gravity model. We compare the results of different specifications showing the improvement in each case. We claim the comparative superiority of the dynamic gravity model with time-varying exporter and importer fixed effects due to its higher explanatory power. Finally, we compare out-of-sample forecasting performance of different specifications of the gravity model.