دانلود رایگان مقاله پیش بینی شناسایی و زمان واقعی چرخه کسب و کار نروژی

abstract

We define and forecast classical business cycle turning points for the Norwegian economy. When defining reference business cycles, we compare a univariate and a multivariate Bry–Boschan approach with univariate Markov-switching models and Markov-switching factor models. On the basis of a receiver operating characteristic curve methodology and a comparison of the business cycle turning points of Norway’s main trading partners, we find that a Markov-switching factor model provides the most reasonable definition of Norwegian business cycles for the sample 1978Q1–2011Q4. In a real-time out-of-sample forecasting exercise, focusing on the last recession, we show that univariate Markovswitching models applied to surveys and a financial conditions index are timely and accurate in calling the last peak in real time. However, the models are less accurate and timely in calling the trough in real time. © 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

5. Conclusion

We have compared alternative business cycle turning points for the Norwegian economy from 1978Q1 to 2011Q4, defined by Markov-switching models and the nonparametric Bry–Boschan method. Based on business cycle statistics and comparisons with the business cycles of some of Norway’s main trading partners, supported by the results from two earlier studies applied to the Norwegian economy and evidence from the ROC curve methodology, we found that the peak and trough dates provided by a quarterly Markow-switching factor model provided the most reasonable definition of reference Norwegian business cycles. In a real-time out-of-sample forecasting exercise, we then studied the timeliness and accuracy of the various methods, in order to predict the peak and trough of the recession in 2008–2009. It is clear that MS models are both more timely and more accurate than the BB method when predicting the peak quarter. We show that applying the MS approach to surveys and a monthly financial conditions index can provide additional gains by detecting peaks in real time at an earlier date than through the application of MS to more traditional factor models or GDP itself.