Comparing Sentiment- and Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail?

Seip Seip,Yilmaz Yilmaz,Schröder Schröder

doi:10.3390/economies7040104

Abstract

We apply a relatively novel leading–lagging (LL) method to four leading and one lagging indexes for industrial production (IP) in Germany. We obtain three sets of results. First, we show that the sentiment-based ifo index performs best in predicting the general changes in IP (−0.596, range −1.0 to 1.0, −1.0 being best). The ZEW index is very close (−0.583). In third place comes, somewhat unexpectedly, the behavioral-based unemployment index (−0.564), and last comes order flow, OF (−0.186). Second, we applied the LL method to predefined recession and recovery time windows. The recessions were best predicted (−0.70), the recoveries worst (−0.32), and the overall prediction was intermediate (−0.48). Third, the method identifies time windows automatically, even for short time windows, where the leading indexes fail. All indexes scored low during time windows around 1997 and 2005. Both periods correspond to anomalous periods in the German economy. The 1997 period coincides with “the great moderation” in the US at the end of a minor depression in Germany. Around 2005, oil prices increased from $10 to $60 a barrel. There were few orders, and monetary supply was low. Our policy implications suggest that the ZEW index performs best (including recessions and recoveries), but unemployment and monetary supply should probably be given more weight in sentiment forecasting.

Highlights

We compare the accuracy and timing of four candidate indexes in Germany for the period January1991 to September 2016 with a novel rolling local application of the leading–lagging (LL)method based on a method developed by Seip and McNown (2007)
(ii) Second, the method can be used to “clean” learning sets used for forecasting, for example, forecasts with the simplex method (Sugihara and May 1990). (iii) Third, in real time, when a new observation is obtained, the LL strength is updated and an increase or decrease will show if the forecasting indexes
We discuss the results for different degrees of smoothing of the time series, and thereafter we compare the performance of the two survey-based indexes, ifo and ZEW, with the order flow index

Summary

Introduction

We compare the accuracy and timing of four candidate indexes in Germany for the period January1991 to September 2016 with a novel rolling (running) local application of the leading–lagging (LL)method based on a method developed by Seip and McNown (2007). We compare the accuracy and timing of four candidate indexes in Germany for the period January. The method estimates LL strengths, rolling cycle times, and rolling phase shifts for paired cyclic time series. The LL method offers a rapid and detailed screening of component series for the construction of composite leading indicators. The LL method can be applied in three modes: (i) In its first mode, the method will show which leading index is best and under which economic conditions, e.g., before a recession or before a recovery. (iii) Third, in real time, when a new observation is obtained, the LL strength is updated and an increase or decrease will show if the forecasting indexes. Economies 2019, 7, 104 increases or decreases in forecasting skill. The LL method is not itself a forecasting method

Results

Discussion

Conclusion