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Coincident Indicators (coincident + indicator)
Selected AbstractsBusiness cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicatorsJOURNAL OF FORECASTING, Issue 1-2 2010Ataman Ozyildirim Abstract Clusters of cyclical turning points in the coincident indicators help us identify and date euro area recessions and recoveries in the past several decades. In the USA and some other countries, composite indexes of coincident indicators (CEI) are used to date classical business cycle turning points; also indexes of leading indicators (LEI) are used to help in the difficult task of predicting these turning points. This paper reviews a selection of the available data for monthly and quarterly euro area coincident and leading indicators. From these data, we develop composite indexes using methods analogous to those tested in the US CEI and LEI published by The Conference Board. We compare the resulting business cycle chronology with the existing alternatives and evaluate our selection of leading indicators in the context of how well they predict current economic activity and its major fluctuations for the euro area. Copyright © 2009 John Wiley & Sons, Ltd. [source] Traditional versus unobserved components methods to forecast quarterly national account aggregatesJOURNAL OF FORECASTING, Issue 2 2007Gustavo A. Marrero Abstract We aim to assess the ability of two alternative forecasting procedures to predict quarterly national account (QNA) aggregates. The application of Box,Jenkins techniques to observed data constitutes the basis of traditional ARIMA and transfer function methods (BJ methods). The alternative procedure exploits the information of unobserved high- and low-frequency components of time series (UC methods). An informal examination of empirical evidence suggests that the relationships between QNA aggregates and coincident indicators are often clearly different for diverse frequencies. Under these circumstances, a Monte Carlo experiment shows that UC methods significantly improve the forecasting accuracy of BJ procedures if coincident indicators play an important role in such predictions. Otherwise (i.e., under univariate procedures), BJ methods tend to be more accurate than the UC alternative, although the differences are small. We illustrate these findings with several applications from the Spanish economy with regard to industrial production, private consumption, business investment and exports.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] Nowcasting quarterly GDP growth in a monthly coincident indicator modelJOURNAL OF FORECASTING, Issue 8 2005Luis C. NunesArticle first published online: 20 DEC 200 Abstract This paper presents an extension of the Stock and Watson coincident indicator model that allows one to include variables available at different frequencies while taking care of missing observations at any time period. The proposed procedure provides estimates of the unobserved common coincident component, of the unobserved monthly series underlying any included quarterly indicator, and of any missing values in the series. An application to a coincident indicator model for the Portuguese economy is presented. We use monthly indicators from business surveys whose results are published with a very short delay. By using the available data for the monthly indicators and for quarterly real GDP, it becomes possible to produce simultaneously a monthly composite index of coincident indicators and an estimate of the latest quarter real GDP growth well ahead of the release of the first official figures. Copyright © 2005 John Wiley & Son, Ltd. [source] A Coincident Index, Common Factors, and Monthly Real GDP,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 1 2010Roberto S. Mariano Abstract The Stock,Watson coincident index and its subsequent extensions assume a static linear one-factor model for the component indicators. This restrictive assumption is unnecessary if one defines a coincident index as an estimate of monthly real gross domestic products (GDP). This paper estimates Gaussian vector autoregression (VAR) and factor models for latent monthly real GDP and other coincident indicators using the observable mixed-frequency series. For maximum likelihood estimation of a VAR model, the expectation-maximization (EM) algorithm helps in finding a good starting value for a quasi-Newton method. The smoothed estimate of latent monthly real GDP is a natural extension of the Stock,Watson coincident index. [source] | ||||||||||