|Carstensen, Kai and Wohlrabe, Klaus and Ziegler, Christina (2010): Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production. Discussion Papers in Economics 2010-16|
In this paper we assess the information content of seven widely cited early indicators for the euro area with respect to forecasting area-wide industrial production. To this end, we use various tests that are designed to compare competing forecast models. In addition to the standard Diebold-Mariano test, we employ tests that account for specific problems typically encountered in forecast exercises. Specifically, we pay attention to nested model structures, we alleviate the problem of data snooping arising from multiple pairwise testing, and we analyze the structural stability in the relative forecast performance of one indicator compared to a benchmark model. Moreover, we consider loss functions that overweight forecast errors in booms and recessions to check whether a specific indicator that appears to be a good choice on average is also preferable in times of economic stress. We find that on average three indicators have superior forecast ability, namely the EuroCoin indicator, the OECD composite leading indicator, and the FAZ-Euro indicator published by the Frankfurter Allgemeine Zeitung. If one is interested in one-month forecasts only, the business climate indicator of the European Commission yields the smallest errors. However, the results are not completely invariant against the choice of the loss function. Moreover, rolling local tests reveal that the indicators are particularly useful in times of unusual changes in industrial production while the simple autoregressive benchmark is difficult to beat during time of average production growth.
|Item Type:||Paper (Discussion Paper)|
|Keywords:||weighted loss, leading indicators, euro area, forecasting|
Economics > Discussion Papers in Economics
Economics > Chairs > CESifo-Professorship for Business Cycle Analysis and Surveys
|Subjects:||300 Social sciences > 300 Social sciences, sociology and anthropology|
300 Social sciences > 330 Economics
|JEL Classification:||C32, C53, E32|
|Deposited On:||22. Mar 2010 22:00|
|Last Modified:||16. Apr 2014 00:00|
Andrews, Donald W. (1993), “Tests for parameter instability and structural change with unknown change point,” Econometrica, 61, 821–856.
Bodo, G., R. Golinelli, and G. Parigi (2000), “Forecasting industrial production in the euro area,” Empirical economics, 25(4), 541–561.
Breitung, J¨org, and Doris Jagodzinski (2001), “Prognoseeigenschaften alternativer Indikatoren f¨ur die Konjunkturentwicklung in Deutschland,” Konjunkturpolitik, 47, 292–314.
Caggiano, G., G. Kapetanios, and V. Labhard (2009), “Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK,” Working paper series, European Central Bank.
Clark, T.E., and M.W. McCracken (2009), “Averaging forecasts from VARs with uncertain instabilities,” Journal of Applied Econometrics, 25(1), 5–21.
Clark, Todd E., and Michael W. McCracken (2001), “Tests of Equal Forecast Accuracy and Encompassing for Nested Models,” Journal of Econometrics, 105, 85–110.
Clark, Todd E., and Kenneth D. West (2005), “Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis,” Journal of Econometrics, 135, 155–186.
Clark, Todd E., and Kenneth D. West (2007), “Approximately normal tests for equal predictive accuracy in nested models,” Journal of Econometrics, 138, 291–311.
Diebold, Francis X., and Roberto S. Mariano(1995), “Comparing Predictive Accuracy,”Journal of Business and Economic Statistics, 13(3), 253–263.
Fichtner, F., R. Ruffer, and B. Schnatz (2009), “Leading indicators in a globalised world, ”Working paper series, European Central Bank.
Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2003), “Do financial variables help forecasting inflation and real activity in the euro area?” Journal of Monetary Economics, 50(6), 1243–1255.
Giacomini, Raffaella, and Barbara Rossi (2008), “Forecasting Comparisons in Unstable Environments,” Working Paper 08–04, Duke University, Department of Economics.
Hansen, Peter Reinhard (2005), “A Test for Superior Predictive Ability,” Journal of Business and Economic Statistics, 23(4), 365–380.
Harvey, David I., Stephen J. Leybourne, and Paul Newbold (1997), “Testing the equality of prediction mean squared errors,” International Journal of Forecasting, 13, 281–291.
Marcellino, Massimiliano (2008), “A linear benchmark for forecasting GDP growth and inflation?” Journal of Forecasting, 27(4), 305–340.
Marcellino, Massimiliano, James H. Stock, and MarkW.Watson (2003), “Macroeconomic forecasting in the Euro area: Country specific versus area-wide information,” European Economic Review, 47(1), 1–18.
Milas, Costas, and Philip Rothman (2008), “Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts,” International Journal of Forecasting, 24(1), 101–121.
Politis, Dimitris N., and Joseph P. Romano (1994), “The Stationary Bootstrap,” Journal of the American Statistical Association, 89(428), 1303–1313.
Rossi, B., and T. Sekhposyan (2010), “Have economic models’ forecasting performance for US output growth and inflation changed over time, and when?” International Journal of Forecasting, In press.
van Dijk, Dick, and Philip Hans Franses (2003), “Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy,” Oxford Bulletin of Economics and Statistics, 65, 727–744.
White, Halbert (2000), “A reality check for data snooping,” Econometrica, 68(5), 1097–1126.