Improving the Timeliness of Turning Signals for Business Cycles with Monthly Data

The conventional growth rate measures (such as month‐on‐month, year‐on‐year growth rates and 6‐month smoothed annualized rate adopted by the US Bureau of Labor Statistics and Economic Cycle Research Institute) are popular and can be easily obtained by computing the growth rate for monthly data based on a fixed comparison benchmark, although they do not make good use of the information underlying the economic series. By focusing on the monthly data, this paper proposes the k‐month kernel‐weighted annualized rate (k‐MKAR), which includes most existing growth rate measures as special cases. The proposed k‐MKAR measure involves the selection of smoothing parameters that are associated with the accuracy and timeliness for detecting the change in business turning points. That is, the comparison base is flexible and is likely to vary for different series under consideration. A data‐driven procedure depending upon the stepwise multiple reality check test for choosing the smoothing parameters is also suggested in this paper. The simple numerical evaluation and Monte Carlo experiment are conducted to confirm that our measures (in particular the two‐parameter k‐MKAR) improve the timeliness subject to a certain degree of accuracy. The business cycle signals issued by the Council for Economic Planning and Development over the period from 1998 to 2009 in Taiwan are taken as an example to illustrate the empirical application of our method. The empirical results show that the k‐MKAR‐based score lights are more capable of reflecting turning points earlier than the conventional year‐on‐year measure without sacrificing accuracy.