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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.
The report summarizes the present survey practices in Europe and some countries on other continents. The study was launched on behalf of the European Commission and the OECD in April-July 2006. The aim of the project was to identify the state of present survey practices of the institutions that conduct business tendency surveys world-wide. The questionnaire that was sent to the executive institutions in 45 countries focused on the questions how and to what extent the Internet has been integrated in the institutes' survey practices. 32 institutes have responded to the questionnaire and provided information on their present survey techniques. Among them were all European Union countries (Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom), two European non- EU countries Switzerland and Norway, as well as some non-European countries (Japan, South Africa and Brazil). The research findings indicate that Internet mode is becoming more and more imperative in Business Tendency Surveys, being a preferable survey mode by a significant proportion of companies, particularly in the service and the manufacturing sectors. The results of the study confirm that the Internet offers a well-suited platform for the harmonization of the data collection methods in the European Union and beyond.
A spectral analysis of the Australian time series for the investment and savings ratios on quarterly data over the period from September 1959 to December 2005 reveals that the major cyclical components of the savings and investment ratios cohere strongly. This suggests there is a medium to long term relationship between investment and savings. Further, the investment and saving ratios cohere strongly with the business cycle suggesting a procyclical pattern of investment and saving behaviour on Australian data. A subsequent long memory analysis reveals that the saving and investment ratios, the investment ratio and real GDP and the savings ratio and real GDP are fractionally cointegrated. The policy implications are explained.
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.
A large majority of summary indicators derived from the individual responses to qualitative Business Tendency Surveys (which are mostly three-modality questions) result from standard aggregation and quantification methods. This is typically the case for the indicators called balances of opinion, which are currently used in short term analysis and considered by forecasters as explanatory variables in many models. In the present paper, we discuss a new statistical approach to forecast the manufacturing growth from firm-survey responses. We base our predictions on a forecasting algorithm inspired by the random forest regression method, which is known to enjoy good prediction properties. Our algorithm exploits the heterogeneity of the survey responses, works fast, is robust to noise and allows for the treatment of missing values. Starting from a real application on a French dataset related to the manufacturing sector, this procedure appears as a competitive method compared with traditional algorithms.
The forecasting profession, especially when producing forecasts intended to support economic policy, does not currently enjoy a good reputation. Complaints are sometimes voiced about its lack of scientific discipline, which in turn implies that the forecast results may be viewed as arbitrary. At other times, it is the excessively mechanical nature of the forecasting process which is criticised, on the grounds that it prevents a proper evaluation of any information concerning changes that alter the functioning of the economic system. Moreover, the use of structural models is often deemed superfluous, or even dangerous, and reduced forms are suggested as a preferable alternative. Drawing on the actual forecasting experience at the Bank of Italy, this paper argues that these views stem largely from a biased perception of how forecasting works, what it consists of and which goals it pursues. In particular, forecasting does not simply amount to producing a set of figures: rather, it aims at assembling a fullyfledged view – one may call it a "story behind the figures" – of what could happen: a story that has to be internally consistent, whose logical plausibility can be assessed, whose structure is sufficiently articulated to allow one to make a systematic comparison with the wealth of information that accumulates as time goes by. This implies that the forecasts are not the result of a black-box process that completely lacks discipline; neither are they the outcome of a purely mechanical process that cannot take new information into account. This paper tries to show that forecasting can be rigorous, not mechanical, informative, and useful even in the face of unprecedented situations.
This paper presents a comprehensive analysis of the current period performance of the OECD composite leading indicators (CLIs) for 21 OECD Member countries and three zone aggregates for which CLIs are available for a longer time period. The main aim of the current analysis on CLIs is to further evaluate the quality of the indicator in order to identify areas where their reliability could be improved. The results show that first estimates of CLIs are revised frequently but the size of revisions is rather small for most countries and almost neglectable for zone aggregates and there is no evidence of bias. The OECD CLI is, however, designed to provide early signals of turning points (peaks and troughs) between expansions and slowdowns of economic activity. Forecasting turning points is one of the main objectives of the leading indicator technique, because predicting the timing of cyclical turning points is one of the least reliable activities in economic forecasting. The results provide evidence that first and second estimates of year-on-year growth rates give reliable signals of approaching cyclical turning points. Finally, the importance of smoothness of components in the calculation of first and second estimates of the CLI and the overall smoothness of the CLI itself is noted in the findings. The results support the argument that it is not enough to have timely components they also need to be smooth to guarantee small revisions. Overall, this study has shown that whilst it could be dangerous to draw conclusions on the directions up or down in growth rates from one or two months figures for several countries, the first and second estimates of the CLIs give early signals of approaching turning points which in most cases are not revised later.
This paper evaluates the performances of Japanese leading indicators in predicting business cycle turning points. We extract the business cycle component in leading indicators using the frequency selective filter proposed by Baxter and King (1999), and we try to clarify empirically whether or not the leading composite index and its component series truly lead the business cycle turning point dates officially determined by the Japanese government. We argue that if we utilize the evaluated properties of the component series, we may construct a composite leading indicator which has some desirable properties as requested. As an illustration we provide one such example.
This paper assesses the leading indicator properties of the Economic Sentiment Indicator (ESI) of the European Commission, as well as two of its subcomponents, for industrial production growth. For this purpose we perform correlation analysis, Granger causality tests, an assessment on the ability to predict turning points and an out-of-sample forecasting exercise. Within a panel setting we compare the characteristics of these indicators for two subgroups of EU countries: the EU-15 and the new EU member states. We show that the forecasting quality and the leading indicator properties are still slightly lagging behind in the group of new EU member states. This may be related to the general problem of data quality and the undergone history of structural change in these countries that makes the assessment of future economic prospects particularly difficult.
The paper looks at an often debated issue – the decline observed in business cycle volatility – using qualitative data derived from Business Tendency Surveys. It concentrates on the manufacturing sector, providing evidence that volatility slowdown is attributable to a break in the Data Generating Process (Cecchetti, Flores-Lagunes and Krause, 2006) rather than to a long trend decline (Blanchard and Simon, 2001). Moreover, it shows that lower variance of the ISAE Confidence Indicator is mostly explained by the behaviour of firms' assessments of demand and inventories. In particular, inventories volatility has decreased, while volatility of production has instead increased with respect to that of demand. Both of these results are consistent with the claim that better inventories management should have a specific role in shaping the production decisions of the firms (Wen, 2005).
We decompose variations in the aggregate exit rate from unemployment to employment into two factors: i) Changes in the arrival rate of acceptable job offers; and ii) changes in the composition of the unemployment pool in terms of average employability. We argue that the former of these factors provides the basis for an informative labour market tightness indicator, while the latter yields valuable information regarding the design of optimal labour market policies across the cycle. Based on Norwegian register data, we find that individual monthly exit rates tend to double from a cyclical trough to a cyclical peak, ceteris paribus, but that crosssectional heterogeneity nevertheless explains 88 per cent of the overall variation in individual monthly exit probabilities during the period from 1989 to 2002.