Trends and random walks in macroeconmic time series: Some evidence and implications

Fabio Arcangeli, Paul David, Frank Engelman, Christopher Freeman, Massimo Moggi, Richard Nelson, Luigi Orsenigo, Nathan Rosenberg, Michele Salvati, G. N. von Tunzelman, two anonymous referees, and the participants at the meeting of the Committee on Distribution, Growth, and Technical Progress of the Italian National Research Council (CNR), Rome, November 16, 1985, have helped with various redraftings. A particularly grateful acknowledgment is for the insightful and patient help of Moses Abramovitz. This work has been undertaken at the Science Policy Research Unit (SPRU), University of Sussex, as part of the research program of the Designated Research Centre, sponsored by the Economic and Social Research Council (ESRC). Earlier support to the research that led to this paper by the Italian National Research Council (CNR) is also gratefully acknowledged. The statistical research has been undertaken with the assistance of Stephano Brioschi, Ilaria Fornari, and Giovannu Prennushi.

https://www.jstor.org/stable/info/2726526

Sources, Procedures, and Microeconomic Effects of Innovation

This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, and 24-monthahead forecasts for eight monthly U.S. macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998. During this sample period these new forecasts outperformed univariate autoregressions, small vector autoregressions, and leading indicator models.

/pdf/macroeconomic-forecasting-using-diffusion-indexes-45bcfj9ibk.pdf

Macroeconomic Forecasting Using Diffusion Indexes

A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘business cycle’☆

Band-pass filters are useful in a wide range of economic contexts. This paper develops a set of approximate band-pass filters and illustrates their application to measuring the business-cycle component of macroeconomic activity. Detailed comparisons are made with several alternative filters commonly used for extracting business-cycle components.

/pdf/measuring-business-cycles-approximate-band-pass-filters-for-4mds5vsnaq.pdf

Arthur F. Burns

Papers

Measuring Business Cycles

Measuring Business Cycles.

Measuring business cycles

Measuring Business Cycles

Statistical indicators of cyclical revivals