Real Wages and the Business Cycle in Germany

This paper establishes stylized facts about the cyclicality of real consumer wages and real producer wages in Germany. As detrending methods we apply the deterministic trend model, the Beveridge-Nelson decomposition, the Hodrick-Prescott filter, the Baxter-King filter and the structural time series model. The detrended data are analyzed both in the time domain and in the frequency domain. The great advantage of an analysis in the frequency domain is that it allows to assess the relative importance of particular frequencies for the behavior of real wages. In the time domain we find that both real wages display a procyclical pattern and lag behind the business cycle. In the frequency domain the consumer real wage lags behind the business cycle and shows an anticyclical behavior for shorter time periods, whereas for longer time spans a procyclical behavior can be observed. However, for the producer real wage the results in the frequency domain remain inconclusive.real wages, business cycle, frequency domain, time domain, Germany, trend-cycle decomposition, structural time series model, phase angle

Monthly US business cycle indicators : a new multivariate approach based on a band-pass filter

This article proposes a new multivariate method to construct business cycle indicators. The method is based on a decomposition into trend-cycle and irregular. To derive the cycle, a multivariate band-pass filter is applied to the estimated trend-cycle. The whole procedure is fully model-based. Using a set of monthly and quarterly US time series, two monthly business cycle indicators are obtained for the US. They are represented by the smoothed cycles of real GDP and the industrial production index. Both indicators are able to reproduce previous recessions very well. Series contributing to the construction of both indicators are allowed to be leading, lagging or coincident relative to the business cycle. Their behavior is assessed by means of the phase angle and the mean phase angle after cycle estimation. The proposed multivariate method can serve as an attractive tool for policy making, in particular due to its good forecasting performance and quite simple setting. The model ensures reliable realtime forecasts even though it does not involve elaborate mechanisms that account for, e.g., changes in volatility

Outlier detection in structural time series models : the indicator saturation approach

Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The generaltospecific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unitroot autoregressions. By focusing on impulse and stepindicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries

Cyclicality of real wages in the USA and Germany : new insights from wavelet Analysis

This article provides new insights into the cyclical behavior of consumer and producer real wages in the USA and Germany. We apply two methods for the estimation of the cyclical components from the data: the approach based on the structural time series models and the ARIMA?model?based approach combined with the canonical decomposition and a band?pass filter. We examine the extracted cycles drawing on two wavelet concepts: wavelet coherence and wavelet phase angle. In contrast to the analysis in the time or frequency domains, wavelet analysis allows for the identification of possible changes in cyclical patterns over time. From the findings of our study, we can infer that the USA and Germany differ with respect to the lead?lag relationship of real wages and the business cycle. In the USA, both real wages are leading the business cycle in the entire time interval. The German consumer real wage is, on the other hand, lagging the business cycle. For the German producer real wage, the lead?lag pattern changes over time. We also find that real wages in the USA as well in Germany are procyclical or acyclical until 1980 and countercyclical thereafter

SPECTRAN, a set of Matlab programs for spectral analysis

Spectral analysis is one of the most important areas of time series econometrics. The use of spectral measures is widespread in different science fields such as economics, physics, engineering, geology. The SPECTRAN toolbox has been developed to facilitate the application of spectral concepts to univariate as well as to multivariate series. It offers a variety of frequency-domain techniques and supports the statistical inference. It also provides convenient tools for the examination of the results, e.g.functions for writing the output to a file or functions specially designed for plotting the estimated spectral measures. The key feature of SPECTRAN is the user-friendliness embodied in, e.g., the central function spectran which performs the whole analysis with default settings, but also gives the user the possibility to adjust them. This document sets out the most relevant spectral concepts and their implementation in SPECTRAN. Finally, three examples shall illustrate the application of different toolbox function to macroeconomic data

Bidirectional relationship between investor sentiment and excessreturns : new evidence from the wavelet perspective

This paper sheds new light on the mutual relationship between investor sentiment and excess returns corresponding to the bubble component of stock prices. We propose to use the wavelet concept of the phase angle to determine the leadlag relation between these variables. The wavelet phase angle allows for decoupling short and longrun relations and is additionally capable of identifying timevarying comovement patterns. By applying this concept to excess returns of the monthly S&P500 index and two alternative monthly US sentiment indicators we find that in the short run (until 3 months) sentiment is leading returns whereas for periods above 3 months the opposite can be observed

Real wages and the business cycle in Germany

This paper establishes stylized facts about the cyclicality of real consumer wages and real producer wages in Germany. As detrending methods we apply the deterministic trend model, the Beveridge-Nelson decomposition, the Hodrick-Prescott filter, the Baxter-King filter and the structural time series model. The detrended data are analyzed both in the time domain and in the frequency domain. The great advantage of an analysis in the frequency domain is that it allows to assess the relative importance of particular frequencies for the behavior of real wages. In the time domain we find that both real wages display a procyclical pattern and lag behind the business cycle. In the frequency domain the consumer real wage lags behind the business cycle and shows an anticyclical behavior for shorter time periods, whereas for longer time spans a procyclical behavior can be observed. However, for the producer real wage the results in the frequency domain remain inconclusive

Four essays in the empirical analysis of business cycles and structural breaks

Die Analyse von Konjunkturzyklen hat eine lange Geschichte in der makroökonomischen Literatur und seit ihren Anfängen stellt sie eine Herausforderung sowohl für die empirische als auch für die theoretische Forschung dar. Das beständige Interesse an diesem Forschungsbereich kann mit seiner hohen Relevanz für die Wirtschaftspolitik erklärt werden. Zuverlässige Informationen über die Wirtschaftslage spielen eine entscheidende Rolle für die Beobachtung der Wirtschaft und den politischen Entscheidungsprozess. Dies erfordert die Wahl einer Methode für die Gewinnung eines geeigneten Konjunkturindikators. Außerdem muss ein Konjunkturforscher auch andere Aspekte berücksichtigen, die Eigenschaften eines Konjunkturindikators beeinflussen können, wie Strukturbrüche oder Schwankungen mit saisonalen und höheren Frequenzen. Ein weiterer Grund für das große Interesse an der empirischen Forschung zu Konjunkturzyklen kann in der Notwendigkeit gesehen werden, theoretische Ansätze zu überprüfen. Als ein prominentes Beispiel kann die Debatte über das Verhalten von Reallöhnen im Konjunkturzyklus genannt werden, die sich zu einer der am längsten andauernden Debatten in der Makroökonomie entwickelt hat. Die vorliegende Dissertation versucht, unter den oben erwähnten Gesichtspunkten einen Beitrag zur Literatur zu leisten. Zum einen werden in der Arbeit neue methodologische Ansätze vorgeschlagen, um einen Konjunkturindikator zu generieren, beziehungsweise Strukturbrüche aufzudecken. Zum anderen wird versucht, aus einer empirischen Perspektive Aufschlüsse über das zyklische Verhalten von Reallöhnen zu erhalten. In einem ersten Aufsatz wird für die Erzeugung eines Konjunkturindikators ein neues multivariates Modell vorgeschlagen, das auf einem Bandpass (Bandbreitenfilter) basiert. Mit der Anwendung dieser Methode auf einen Datensatz mit Monats- und Quartalsdaten werden zwei Konjunkturindikatoren für die USA erhalten. Der vorgeschlagene Ansatz erweist sich als geeignet, sowohl historische Rezessionen zu reproduzieren, als Prognosen durchzuführen. In einem zweiten Aufsatz werden zum ersten Mal in der Literatur zwei Ansätze kombiniert: Indicator-Saturation als General-to-Specific-Ansatz zur Aufdeckung von Ausreißern und Strukturbrüchen und das strukturelle Zeitreihenmodell zum Zweck der Saisonbereinigung. Die Leistungsfähigkeit der Impulse- und Step-Indicator-Saturation zur Aufdeckung von Ausreißern und Niveauverschiebungen wird sowohl in einer umfangreichen Monte-Carlo-Simulation als auch in einer empirischen Anwendung für Industrieproduktionsreihen in fünf europäischen Ländern untersucht. Der Schwerpunkt der Anwendung liegt auf der Frage, ob die Rezession, die Ende 2008 angefangen hat, mit der Modelldynamik alleine erklärt werden kann, oder ob sie einen bedeutenden Strukturbruch darstellt. In einem dritten Aufsatz werden stilisierte Fakten zum zyklischen Verhalten von Konsumenten- und Produzentenreallöhnen in Deutschland ermittelt. Zunächst werden verschiedene Trendbereinigungsmethoden angewandt, um einen Konjunkturzyklus und Reallohnzyklen zu erzeugen. Die Comovements zwischen den Reallohnzyklen und dem Konjunkturzyklus werden dann im Zeitbereich und im Frequenzbereich mit Hilfe des Phasenwinkels analysiert. Die Ergebnisse im Frequenzbereich weisen darauf hin, dass der Konsumentenreallohn dem Konjunkturzyklus nacheilt. Zudem zeigt er kurzfristig ein antizyklisches Verhalten, während in der längeren Frist ein eher prozyklischen Verhalten zu beobachten ist. Die Ergebnisse für den Produzentenreallohn sind dagegen nicht so eindeutig. Ein vierter Aufsatz vergleicht das zyklische Muster von Konsumenten- und Produzentenreallöhen in den USA und Deutschland. Diese Studie ist die erste, die eine Wavelet-Analyse zur Messung von Comovements im Kontext der untersuchten Fragestellung einsetzt. Aus den Ergebnissen dieser Studie folgt, dass sich die USA und Deutschland im Hinblick auf das Vor- und Nacheilen von Reallöhnen unterscheiden. In den USA eilen beide Reallöhne im ganzen Zeitintervall dem Konjukturzyklus vor. Demgegenüber eilt der Konsumentenreallohn in Deutschland dem Konjunkturzyklus nach. Das Muster für den deutschen Produzentenreallohn ändert sich im Zeitablauf. Darüber hinaus zeigen die Ergebnisse, dass sich die Reallöhne in den USA und Deutschland bis 1980 prozyklisch oder azyklisch und danach antizyklisch verhalten.Business cycle analysis has a long history in the macroeconomics literature and since its origins it poses a challenge for both empirical and theoretical research. The enduring interest in this research area is dictated by its high relevance for economic policy. Reliable information on the state of the economy plays a crucial role in the monitoring of the economy and in the policy-making process. This involves the choice of the method for extraction of a proper business cycle indicator. Moreover, the business cycle analyst also has to take account of structural breaks as well as seasonal and higher frequency movements of the series that can affect the properties of a business cycle indicator. Another reason for the keen interest in empirical business cycle research can be seen in the need to validate theoretical approaches. A prominent example is the debate on the cyclical behavior of real wages which evolved to one of the most lively and long--lasting debates in macroeconomics. This thesis tries to contribute to the literature under the aforementioned aspects. It offers a new methodological perspective with respect to the extraction of business cycles and detection of structural breaks. Furthermore, it sheds some light on the question of real wage cyclicality from the empirical point of view. The first essay proposes a new multivariate model based on a band-pass filter to construct business cycle indicators. Using this method and a dataset with monthly and quarterly US time series, two monthly business cycle indicators are obtained for the US. It is shown that the proposed method not only reproduces historical recessions very well, but it also performs good in terms of forecasting. The second essay for the first time in the literature combines indicator saturation as a general-to-specific approach to detect outliers and structural breaks with the structural time series model for the purpose of seasonal adjustment. The performance of the impulse-indicator and step-indicator saturation for detecting additive outliers and level shifts is tested in both a comprehensive Monte Carlo simulation exercise and an empirical application. The latter involves five European industrial production series. Its focus lies on the question whether the recessionary episode starting towards the end of 2008 can be described by the inherent model dynamics, or whether it represents a major structural change. In the third essay, stylized facts about the cyclicality of real consumer wages and real producer wages in Germany are established. First, various detrending methods are applied to estimate a business cycle and real wage cycles. The comovements between real wage cycles and the business cycle are then examined both in the time domain and in the frequency domain by resorting to the concept of the phase angle. According to the frequency domain results, the consumer real wage lags behind the business cycle. Moreover, it exhibits an anticyclical behavior in the short run, whereas in the longer run a procyclical behavior can be observed. For the producer real wage, in contrast, the results in the frequency domain are not clear-cut. The fourth essay compares the cyclical behavior of consumer and producer real wages in the USA and Germany. This study is the first one which employs wavelet analysis as a comovement tool in the context of the examined research question. From the findings of this study it can be inferred that the USA and Germany differ with respect to the lead-lag relationship of real wages and the business cycle. In the USA, both real wages are leading the business cycle in the entire time interval. The German consumer real wage is, on the other hand, lagging the business cycle. For the German producer real wage, the lead-lag pattern changes over time. In addition, the results show that real wages in the USA as well in Germany are procyclical or acyclical until 1980 and countercyclical thereafter

A datacleaning augmented Kalman filter for robust estimation of state space models

This article presents a robust augmented Kalman filter that extends the data cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their onestepahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an Mtype estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the com- parative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series

EuroMInd-D : a density estimate of monthly gross domestic product for the Euro area

EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottomup approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and raggededged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules