Extracting the Italian output gap: a Bayesian approach

During the last decades particular effort has been directed towards understanding and predicting the relevant state of the business cycle with the objective of decomposing permanent shocks from those having only a transitory impact on real output. This trend--cycle decomposition has a relevant impact on several economic and fiscal variables and constitutes by itself an important indicator for policy purposes. This paper deals with trend--cycle decomposition for the Italian economy having some interesting peculiarities which makes it attractive to analyse from both a statistic and an historical perspective. We propose an univariate model for the quarterly real GDP, subsequently extended to include the price dynamics through a Phillips curve. This study considers a series of the Italian quarterly real GDP recently released by OECD which includes both the 1960s and the recent global financial crisis of 2007--2008. Parameters estimate as well as the signal extraction are performed within the Bayesian paradigm which effectively handles complex models where the parameters enter the log--likelihood function in a strongly nonlinear way. A new Adaptive Independent Metropolis--within--Gibbs sampler is then developed to efficiently simulate the parameters of the unobserved cycle. Our results suggest that inflation influences the Output Gap estimate, making the extracted Italian OG an important indicator of inflation pressures on the real side of the economy, as stated by the Phillips theory. Moreover, our estimate of the sequence of peaks and troughs of the Output Gap is in line with the OECD official dating of the Italian business cycle

Business Cycles in Emerging market Economies: A New View of the Stylised Facts

This paper builds on an earlier work in business cycle theory - explicitly in the classical cycle tradition of Burns and Mitchell (1946) and the more recent work by Harding and Pagan (e.g.: 2002a; 2005b; 2005a) - to identify and analyse business cycles in emerging market economies. The goal is to revisit the work of for example Agénor, McDermott and Prasad (2000), whom have established a set of stylised facts for business cycle fluctuations in developing countries. Agénor, et. al. (2000) established these stylised facts using the presently standard method of analysing the features of serially correlated deviations from trends (idenified with statistical techniques such as the Hodrick-Prescott filter) in certain macroeconomic time series, including real GDP, the price level, and components of final demand. The alternative method, implemented in this paper, uses an algorithm of Bry and Boschan (1971), and the recent work of Harding and Pagan to identify the various stylised facts regarding the duration, steepness, amplitude and concordance of these fluctuations in emerging market economies.business cycles, turning points, emerging market economies, quantitative analysis of business cycles, time series econometrics, regression with binary variables

Econometric Methods of Signal Extraction

The Wiener-Kolmogorov signal extraction filters, which are widely used in econometric analysis, are constructed on the basis of statistical models of the processes generating the data. In this paper, such models are used mainly as heuristic devices that are to be specified in whichever ways are appropriate to ensure that the filters have the desired characteristics. The digital Butterworth filters, which are described and illustrated in the paper, are specified in this way. The components of an econometric time series often give rise to spectral structures that fall within well-defined frequency bands that are isolated from each other by spectral dead spaces. We find that the finite-sample Wiener-Kolmogorov formulation lends itself readily to a specialisation that is appropriate for dealing with band-limited components.Signal extraction, Linear filtering, Frequency-domain analysis, Trend estimation

Reconsidering the evidence: Are Eurozone business cycles converging

This paper, using 40 years of monthly industrial production data, examines the relationship between the business cycles of the 12 Eurozone countries. Since estimates of the business cycle have been found to be sensitive to how the cycle is measured, a range of alternative measures are considered. We focus on both parametric and nonparametric univariate measures of the ‘classical’ and ‘growth’ cycles. We then investigate whether Eurozone business cycles have converged. This is based on an analysis of the distribution of bivariate correlation coefficients between the 12 countries’ business cycles. This extends previous work that has tested for convergence, in a similar manner by focusing on correlation, but has not considered the entire distribution, instead focusing on the mean correlation coefficient or particular bivariate correlation coefficients. Although empirical inference about individual Eurozone business cycles is found to be sensitive to the measure of the business cycle considered, our measure of convergence between the Eurozone business cycles exhibits common features across the alternative measures of the business cycle. Interestingly, we find that there have been periods of convergence, identified by the distribution tending to unity, and periods of divergence. Although further data are required to corroborate the story, there is evidence to suggest that the Euro-zone has entered a period of convergence after the clear period of divergence in the early 1990s in the aftermath of German unification and at the time of the currency crises in Europe. This is encouraging for the successful operation of a common monetary policy in the Eurozone. --

Dating EU15 Monthly Business Cycle Jointly Using GDP and IPI

This paper aims at the production of a chronology for the EU15 business cycle by comparing parametric and non-parametric procedures on monthly and quarterly data as well in a combined approach. The main innovation is the joint use of the monthly series for the EU15 Gross Domestic Product (GDP) and the EU15 Industrial Production Index (IPI) from 1970 to 2003. The monthly IPI and the quarterly GDP at the EU15 level have been reconstructed starting from the available national series. The monthly GDP has then been computed using temporal disaggregation techniques. The obtained chronology is directly comparable to ones produced by several authors for the euro area.Business cycle, Chronology, Historical reconstruction, Monthly GDP

Forecasting Quarter-on-Quarter Changes of German GDP with Monthly Business Tendency Survey Results

Results from business tendency surveys are often used to construct leading indicators. The indicators are then, for example, employed to forecast GDP growth. In this article more detailed results of business tendency surveys are used to forecast quarter-onquarter GDP growth. The target series is very challenging because this type of growth rate leads to quite volatile time series. The present study focuses on German GDP data and survey results provided by the Ifo Institute. Since numerous time series of possible indicators result from the surveys, methods that can handle this setting are applied. One candidate method is principal component analysis, which is used to reduce dimensionality. On the other hand, subset selection procedures are applied. For the present setting the latter method seems more successful than principal components. But this is not a statement about the two types of procedures in general. Which method should be favoured depends very much on the aims of the specific study.Business tendency surveys, business cycle analysis, principal component regression, subset selection.

Exploring ICA for time series decomposition

In this paper, we apply independent component analysis (ICA) for prediction and signal extraction in multivariate time series data. We compare the performance of three different ICA procedures, JADE, SOBI, and FOTBI that estimate the components exploiting either the non-Gaussianity, or the temporal structure of the data, or combining both, non-Gaussianity as well as temporal dependence. Some Monte Carlo simulation experiments are carried out to investigate the performance of these algorithms in order to extract components such as trend, cycle, and seasonal components. Moreover, we empirically test the performance of those three ICA procedures on capturing the dynamic relationships among the industrial production index (IPI) time series of four European countries. We also compare the accuracy of the IPI time series forecasts using a few JADE, SOBI, and FOTBI components, at different time horizons. According to the results, FOTBI seems to be a good starting point for automatic time series signal extraction procedures, and it also provides quite accurate forecasts for the IPIs.ICA, Signal extraction, Multivariate time series, Forecasting