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The Journal of Business Cycle Measurement and Analysis has been discontinued as of 24 June 2016. This journal was published jointly with CIRET from 2004 to 2015. For more information see www.ciret.org/jbcy.
This paper presents a new methodology for the quantification of qualitative survey data. Traditional conversion methods, such as the probability approach of Carlson and Parkin (1975) or the time-varying parameters model of Seitz (1988), require very restrictive assumptions concerning the expectations formation process of survey respondents. Above all, the unbiasedness of expectations, which is a necessary condition for rationality, is imposed. Our approach avoids this assumptions. The novelty lies in the way the boundaries inside of which survey respondents expect the variable under consideration to remain unchanged are determined. Instead of deriving these boundaries from the statistical properties of the reference time-series (which necessitates the unbiasedness assumption), we directly queried them from survey respondents by a special question in the Ifo World Economic Survey. The new methodology is then applied to expectations about the future development of inflation obtained from the Ifo World Economic Survey.
In this paper, we consider a threshold time series model in order to take into account certain stylized facts of the business cycle, such as asymmetries in the phases of the cycle. Our aim is to point out some thresholds under (over) which a signal of turning point could be given in real-time. First, we introduce the various threshold models and we discuss both their statistical theoretical and empirical properties. Especially, we review the classical techniques to estimate the number of regimes, the threshold, the delay and the parameters of the model. Then we apply these models to the Euro-zone industrial production index to detect in real-time, trough a dynamic simulation approach, the dates of peaks and throughs in the business cycle.
Spatial and Temporal Time Series Conversion - A Consistent Estimator of the Error Variance-Covariance Matrix. Abstract: We focus on the problem of time series conversion from low to high frequency satisfying the twofold temporal and spatial constraint. We offer a simple solution to variance-covariance matrix estimation of the error terms. Since the residuals of high frequency equations of the indicated indicator model are not observable, we inferred the characteristics of their stochastic process through both a specific hypothesis (VAR 1 process) and estimation of the related annual model. We derive a consistent estimator of the variance-covariance matrix and we prove that Di Fonzo's (1990) estimator based on this matrix is asymptotically equivalent to a GLS estimator.
The extraction of a common signal from a group of time series is generally obtained using variables recorded with the same frequency or transformed to have the same frequency (monthly, quarterly, etc.). The econometric literature has not paid a great deal of attention to this topic. In this paper we extend an approach based on the use of dummy variables to the well known trend plus cycle model, in a multivariate context, using both quarterly and monthly data. This procedure is applied to the Italian economy, using the variables suggested by an Italian Institution (ISAE) to provide a national dating, and compared with the equivalent multivariate and univariate approaches with monthly data. We note that the contemporaneous use of quarterly and monthly data provides results more consistent with the official ones with respect to the other approaches.
Business tendency surveys are a popular instrument for business cycle analysis. The survey results are used to calculate leading business cycle indicators. For Germany the Ifo Institute publishes the Ifo business climate, which consists of the results of two questions: one question about the current situation of the respondents and a second question about their expectations for the coming months. The business climate combines the answers on both questions to a single indicator. This indicator is closely watched by the public. To visualize the interactions between the two components behind this indicator, the Ifo Institute developed a graphical representation called the Ifo business cycle clock. This article shows, with the help of circular-linear correlation analysis, that this illustration is indeed valuable. But it also shows that the clock times should be interpreted carefully.