Testing for Non-Linear Dependence in Univariate Time Series: An Empirical Investigation of the Austrian Unemployment Rate

The modelling of univariate time series is a subject of great importance in a variety of fields, in regional science and economics, and beyond. Time series modelling involves three major stages:model identification, model%0D estimation and diagnostic checking. This current paper focuses its attention on the model identification stage in general and on the issue of testing for non-linear dependence in particular. If the null hypothesis of independence is rejected, then the alternative hypothesis implies the existence of linear or non-linear dependence. The test of this hypothesis is of crucial importance. If the data are linearly dependent, the linear time series models have to be specified (generally within the SARIMA methodology). If the data are non-linearly dependent, then non-linear time series modelling (such as ARCH, GARCH and autoregressive neural network models) must be employed. Several tests have recently been developed for this purpose. In this paper we make a modest attempt to investigate the power of five competing tests (McLeod-Li-test, Hsieh-test, BDS-test, Terävirta''''s neural network test) in a real world application domain of unemployment rate prediction in order to determine what kind of non-linear specification they have good power against, and which not. The results obtained indicate that that all the tests reject the hypothesis of mere linear dependence in our application. But if interest is focused on predicting the conditional mean of the series, the neural network test is most informative for model identification and its use is therefore highly%0D recommended.

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Series: Research Reports of the Institute for Economic Geography and GIScienc

Testing for Non-Linear Dependence in Univariate Time Series: An Empirical Investigation of the Austrian Unemployment Rate

In recent years interest has been growing in testing for stochastic non-linearity in macroeconomic time series. There are several inference procedures available. But not much is known about their behaviour on real world small-sized settings. This paper surveys some of these tests. Their performance is compared using monthly Austrian unemployment data that cover the period January 1960 to December 1997. It is found that the test procedures surveyed are complementary rather than competing. Several useful guidelines are provided for applying the increasingly complex test procedures in practice.Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Prognose makrooekonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen

In dieser Arbeit wird die Eignung des Instrumentariums der neuronalen Netze, im Konkreten der autoregressiven Neuronale-Netz-Modelle (ARNN), zur Modellierung und Prognose von makroökonomischen Zeitreihen untersucht und mit jenen der autoregressiven (AR) und autoregressiven Moving-Average-Modelle (ARMA) verglichen. Als beispielhaftes Anwendungsgebiet werden die beiden monatlichen Zeitreihen der österreichischen Arbeitslosenrate und des österreichischen Industrieproduktionsindex herangezogen. Die Arbeit beinhaltet eine Reihe von Erweiterungen an den Methoden und Algorithmen im Zusammenhang mit der ARNN-Modellierung, die durch die besonderen Herausforderungen bei der Modellierung und Prognose von makroökonomischen Zeitreihen motiviert sind. Eine Evaluationsstudie zum Vergleich der Güte von Mehr-Schritt-Prognosen verschiedener Modellierungsstrategien wird durchgeführt

Prognose makrooekonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen

In dieser Arbeit wird die Eignung des Instrumentariums der neuronalen Netze, im Konkreten der autoregressiven Neuronale-Netz-Modelle (ARNN), zur Modellierung und Prognose von makroökonomischen Zeitreihen untersucht und mit jenen der autoregressiven (AR) und autoregressiven Moving-Average-Modelle (ARMA) verglichen. Als beispielhaftes Anwendungsgebiet werden die beiden monatlichen Zeitreihen der österreichischen Arbeitslosenrate und des österreichischen Industrieproduktionsindex herangezogen. Die Arbeit beinhaltet eine Reihe von Erweiterungen an den Methoden und Algorithmen im Zusammenhang mit der ARNN-Modellierung, die durch die besonderen Herausforderungen bei der Modellierung und Prognose von makroökonomischen Zeitreihen motiviert sind. Eine Evaluationsstudie zum Vergleich der Güte von Mehr-Schritt-Prognosen verschiedener Modellierungsstrategien wird durchgeführt

Channel Estimation based on Gaussian Mixture Models with Structured Covariances

In this work, we propose variations of a Gaussian mixture model (GMM) based channel estimator that was recently proven to be asymptotically optimal in the minimum mean square error (MMSE) sense. We account for the need of low computational complexity in the online estimation and low cost for training and storage in practical applications. To this end, we discuss modifications of the underlying expectation-maximization (EM) algorithm, which is needed to fit the parameters of the GMM, to allow for structurally constrained covariances. Further, we investigate splitting the 2D time and frequency estimation problem in wideband systems into cascaded 1D estimations with the help of the GMM. The proposed cascaded GMM approach drastically reduces the complexity and memory requirements. We observe that due to the training on realistic channel data, the proposed GMM estimators seem to inherently perform a trade-off between saving complexity/parameters and estimation performance. We compare these low-complexity approaches to a practical and low cost method that relies on the power delay profile (PDP) and the Doppler spectrum (DS). We argue that, with the training on scenario-specific data from the environment, these practical baselines are outperformed by far with equal estimation complexity

Price Competitiveness in the European Monetary Union: A Decomposition of Inflation Differentials based on the Leontief Input-Output Price Model for the Period 2000 to 2014

This paper studies the persistent producer price inflation differentials within the European Monetary Union. By applying a decomposition procedure within the input-output framework, the drivers of sectoral producer price inflation in a representative sample of member states are re-vealed. We find that in the pre-crisis period (2001-2008) the inflation differentials in manufactur-ing and market services of all countries vis-à-vis Germany were consistently positive resulting in a loss of price competitiveness for all economies. Manufacturing and market service sectors of many countries continued to lose price competitiveness, though to a lesser extent, also during the crisis period (2009-2014). We observe that differences in unit labour cost developments across countries constitute an important driver, especially in the pre-crisis period. Other drivers, such as import costs, intermediate input costs and operating surpluses also contribute, in particular dur¬ing the crisis period

Economy 4.0: Employment effects by occupation, industry, and gender

The aim of this study is to investigate how the diffusion of the new digital technologies (Economy 4.0-technologies) effects the magnitude and composition of employment in Austria. For this purpose, an input-output framework is adopted taking into account direct as well as indirect effects of the new technologies by industry, occupation and gender. These employment effects are estimated as the difference between a base economy (as represented by the most recent Austrian input-output table) and the same economy after an assumed 10-year transformation period with the introduction of new production technologies and devel-opment of new products for final demand. Based on substitution potentials estimated on de-tailed occupational level available from previous research, we model the changes in labour productivity. Combining two different scenarios of labour productivity change with two dif-ferent assumptions about collective wage bargaining outcomes gives us four possible scenari¬os of macroeconomic paths of Economy 4.0. The results show that due to Economy 4.0 dur¬ing the next 10 years job displacement will probably be greater than job creation and aggre¬gate employment will decline by 0.80% to 4.81% relative to total present employment. Fur-thermore, the results indicate that occupations gaining in employment are highly skilled while the occupations losing in employment are medium-skilled. Hereby, the female workers are adversely affected in terms of employment and labour income

Economic drivers of greenhouse gas-emissions in small open economies: A hierarchical structural decomposition analysis

The Paris agreement has prescribed strict Greenhouse Gas (GHG) reduction targets for participating countries. Implementation of climate protection policies is challenging, especially if the economy is export driven. We introduce a hierarchical structural decomposition model in order to investigate the effects of exports, imports, economic structure, consumption patterns, consumption level, outsourcing and insourcing on national GHG emissions. This model is applied to the data of national environmental accounts and to a harmonized and price-deflated series of national input-output tables of Austria for the years 1995, 2000, 2005 and 2010. Over the whole time period, the results indicate that the final demand effect was the main driver of GHG emissions, with exports as most important factor. Surprisingly, emission intensity contributed to an increase of GHG emissions during the period 2000-2005 as well, mostly due to increasing emission intensity in the transport sector