Constructing leading indicators from non-balanced sectoral business survey series

Abstract This paper considers the construction of leading indicators based on monthly survey data from the Ifo Institute, Munich. The three main points covered in the paper are: (a) The use of survey data at the sectoral level results in a longer leading indicator. By taking a non-balanced form of the survey answers and exploiting the information contained in ‘no change’ responses through the use of canonical coherence, regressions on certain wave lengths lead to higher cross-spectral coherencies between the survey data and the actual business cycle, (b) Comparisons of frequency domain and time domain results for lead-lag relationships highlight the roles of seasonal and business cycles, (c) Out of sample forecasts reveal that the traditional balance concept is dominated by a weighted average of ‘worse’ and ‘equal’ responses. Surprisingly, the best results come from using the ‘worse’ share.

[1]  Ray C. Fair,et al.  The Informational Context of Ex Ante Forecasts , 1989 .

[2]  Finn E. Kydland,et al.  Time to Build and Aggregate Fluctuations , 1982 .

[3]  Finn E. Kydland,et al.  Business cycles: real facts and a monetary myth , 1990 .

[4]  T. Teräsvirta.  Model selection using business survey data: Forecasting the output of the Finnish metal and engineering industries , 1986 .

[5]  Lars-Erik Öller,et al.  Forecasting the business cycle using survey data , 1990 .

[6]  H. Entorf.  Real business cycles: is neglecting demand shocks justified? , 1992 .

[7]  G. H. Moore,et al.  The Leading Indicator Approach to Economic Forecasting--Retrospect and Prospect , 1982 .

[8]  David F. Hendry,et al.  On the Formulation of Empirical-models in Dynamic Econometrics , 1982 .

[9]  J. MacKinnon,et al.  Several Tests for Model Specication in the Pres-ence of Alternative Hypotheses , 1981 .

[10]  Spyros Makridakis,et al.  The art and science of forecasting An assessment and future directions , 1986 .

[11]  Jean-Francois Richard,et al.  The Encompassing Principle and Its Application to Testing Non-nested Hypotheses , 1986 .

[12]  J. Stock,et al.  Testing for Common Trends , 1988 .

[13]  C. Nelson,et al.  Trends and random walks in macroeconmic time series: Some evidence and implications , 1982 .

[14]  Marc Nerlove,et al.  Expectations, Plans, and Realizations in Theory and Practice , 1983 .

[15]  Heejoon Kang,et al.  Pitfalls in the Use of Time as an Explanatory Variable in Regression , 1983 .

[16]  H. Entorf.  Real business cycles under test: A multi-country, multi-sector exercise , 1991 .

[17]  E. J. Hannan,et al.  Multiple time series , 1970 .

[18]  M. Eichenbaum.  Real Business Cycle Theory: Wisdom or Whimsy? , 1990 .

[19]  Henri Theil,et al.  On the Time Shape of Economic Microvariables and the Munich Business Test , 1952 .

[20]  G. Hansen.  Indivisible Labor and the Business Cycle , 1985 .

[21]  C. Nelson,et al.  Spurious Periodicity in Inappropriately Detrended Time Series , 1981 .

[22]  Ray C. Fair,et al.  Comparing Information in Forecasts from Econometric Models , 1990 .

[23]  P. Perron,et al.  Trends and random walks in macroeconomic time series : Further evidence from a new approach , 1988 .

[24]  Marc Nerlove,et al.  On the formation of price expectations: An analysis of business test data by log-linear probability models , 1981 .

[25]  K. Wallis.  Seasonal Adjustment and Relations Between Variables , 1974 .

[26]  Jeffrey A. Miron,et al.  The Seasonal Cycle and the Business Cycle , 1988, Journal of Political Economy.