Forecasting GDP at the regional level with many predictors

Lehmann, Robert und Wohlrabe, Klaus (14. September 2013): Forecasting GDP at the regional level with many predictors. Münchener Wirtschaftswissenschaftliche Beiträge (VWL) 2013-13

Abstract

In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a ’data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single– indicator, multi–indicator, pooled and factor forecasts in a pseudo real–time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.

• Dokumententyp: Paper
• Keywords: regional forecasting, forecast combination, factor models, model confidence set, data–rich environment
• Fakultät: Volkswirtschaft > Munich Discussion Papers in Economics
• Themengebiete: 300 Sozialwissenschaften > 330 Wirtschaft
• JEL Classification: C32, C52, C53, E37, R11
• Sprache: Englisch

Literaturliste

Abberger, K. and Wohlrabe, K. (2006). Einige Prognoseeigenschaften des ifo Geschäftsklimas – Ein Überblick über die neuere wissenschaftliche Literatur. ifo Schnelldienst, 59 (22), 19–26.

Bandholz, H. and Funke, M. (2003). Die Konstruktion und Schätzung eines Konjunkturfrühindikators für Hamburg. Wirtschaftsdienst, 83 (8), 540–548.

Banerjee, A., Marcellino, M. and Masten, I. (2005). Leading Indicators for Euro-area Inflation and GDP Growth. Oxford Bulletin of Economics and Statistics, 67 (Supplement s1), 785–813.

—,—and—(2006). Forecasting macroeconomic variables for the new member states. In M. J. Artis, A. Banerjee and M. Marcellino (eds.), The central and eastern European countries and the European Union, Cambridge: Cambridge University Press, pp. 108–134.

Bates, J. M. and Granger, C. W. J. (1969). The Combination of Forecasts. Operational Research Quarterly, 20 (4), 451–468.

Bodo, G., Golinelli, G. and Parigi, G. (2000). Forecasting industrial production in the Euro area. Empirical Economics, 25 (4), 541–561.

Brautzsch, H. U. and Ludwig, U. (2002). Vierteljährliche Entstehungsrechnung des Bruttoinlandsprodukts für Ostdeutschland: Sektorale Bruttowertschöpfung. IWH Discussion Papers No. 164.

Breitung, J. and Schumacher, C. (2008). Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. International Journal of Forecasting, 24 (3), 386–398.

Brisson, M., Campbell, B. and Galbraith, J. W. (2003). Forecasting some lowpredictability time series using diffusion indices. Journal of Forecasting, 22 (6-7), 515–531.

Carstensen, K., Wohlrabe, K. and Ziegler, C. (2011). Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production. Journal of Economics and Statistics, 231 (1), 82–106.

Chow, G. C. and Lin, A. (1971). Best linear unbiased interpolation, distribution and exploration of time series by related series. The Review of Economics and Statistics, 53 (4), 372–375.

Clements, M. P. and Galvão, A. B. (2009). Forecasting US output growth using Leading Indicators: An appraisal using MIDAS models. Journal of Applied Econometrics, 24 (7), 1187–1206.

Costantini, M. and Pappalardo, C. (2010). A hierarchical procedure for the combination of forecasts. International Journal of Forecasting, 26 (4), 725–743.

Diebold, F. X. and Pauly, P. (1987). Structural change and the combination of forecasts. Journal of Forecasting, 6 (1), 21–40. Doz, C., Giannone, D. and Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164 (1), 188–205.

—, — and — (2012). A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models. The Review of Economics and Statistics, 94 (4), 1014–1024.

Drechsel, K. and Maurin, L. (2011). Flow of Conjunctural Information and Forecast of Euro Area Economic Activity. Journal of Forecasting, 30 (3), 336–354.

— and Scheufele, R. (2012a). Bottom-up or Direct? Forecasting German GDP in a Datarich Environment. Swiss National Bank Working Papers 2012-16.

—and—(2012b). The performance of short-term forecasts of the german economy before and during the 2008/2009 recession. International Journal of Forecasting, 28 (2), 428–445.

Dreger, C. and Kholodilin, K. A. (2007). Prognosen der regionalen Konjunkturentwicklung. Quarterly Journal of Economic Research, 76 (4), 47–55.

Eickmeier, S. and Ng, T. (2011). Forecasting national activity using lots of international predictors: An application to New Zealand. International Journal of Forecasting, 27 (2), 496–511.

—and Ziegler, C. (2008). How Successful are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach. Journal of Forecasting, 27 (3), 237–265.

Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2003). Do financial variables help forecasting inflation and real activity in the euro area? Journal of Monetary Economics, 50 (6), 1243–1255.

—, —, — and — (2005). The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting. Journal of the American Statistical Association, 100 (471), 830–840.

Giannone, D., Reichlin, L. and Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55 (4), 665–676.

Hansen, P. R. (2005). A Test for Superior Predictive Ability. Journal of Business and Economic Statistics, 23 (4), 365–380.

—, Lunde, A. and Nason, J. M. (2011). The Model Confidence Set. Econometrica, 79 (2), 453–497.

Kholodilin, K. A., Kooths, S. and Siliverstovs, B. (2008). A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder. Spatial Economic Analysis, 3 (2), 195–207. — and Siliverstovs, B. (2006). On the forecasting properties of the alternative leading indicators for the German GDP: recent evidence. Journal of Economics and Statistics, 226 (3), 234–259.

Kopoin, A., Moran, K. and Paré, J.-P. (2013). Forecasting regional GDP with many factor.models: How useful are national and international data? Economics Letters, forthcoming.

Kuzin, V., Marcellino, M. and Schumacher, C. (2013). Pooling versus Model Selection for Nowcasting GDP with Many Predictors: Empirical Evidence for Six Industrialized Countries. Journal of Applied Econometrics, 28 (3), 392–411.

Lehmann, R., Speich, W. D., Straube, R. and Vogt, G. (2010). Funktioniert der ifo Konjunkturtest auch wirtschaftlichen Krisenzeiten? Eine Analyse der Zusammenhänge zwischen ifo Geschäftsklima und amtlichen Konjunkturdaten für Sachsen. ifo Dresden berichtet, 17 (3), 8–14.

Longhi, S. and Nijkamp, P. (2007). Forecasting Regional Labor Market Developments under Spatial Autocorrelation. International Regional Science Review, 30 (2), 100–119.

Marcellino, M., Stock, J. H. andWatson, M. W. (2003). Macroeconomic forecasting in the Euro area: Country specific versus area-wide information. European Economic Review, 47 (1), 1–18.

Nierhaus, W. (2007). Vierteljährliche Volkswirtschaftliche Gesamtrechnungen für Sachsen mit Hilfe temporaler Disaggregation. ifo Dresden berichtet, 14 (4), 24–36.

Ragnitz, J. (2009). East Germany Today: Successes and Failures. CESifo Dice Report, 7 (4), 51–58.

Schanne, N., Wapler, R. and Weyh, A. (2010). Regional unemployment forecasts with spatial interdependencies. International Journal of Forecasting, 26 (4), 908–926.

Schirwitz, B., Seiler, C. and Wohlrabe, K. (2009). Regionale Konjunkturzyklen in Deutschland – Teil II: Die Zyklendatierung. ifo Schnelldienst, 62 (14), 24–31.

Schumacher, C. (2007). Forecasting German GDP Using Alternative Factor Models Based on Large Datasets. Journal of Forecasting, 26 (4), 271–302.

—(2010). Factor forecasting using international targeted predictors: The case of German GDP. Economics Letters, 107 (2), 95–98.

Stock, J. H. and Watson, M. W. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business & Economic Statistics, 20 (2), 147–162.

— and — (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23 (6), 405–430.

— and — (2006). Forecasting with many Predictors. In G. Elliott, C. W. J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, vol. 1, 10, Elsevier, pp. 515–554.

Timmermann, A. (2006). Forecast Combinations. In G. Elliott, C. W. J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, vol. 1, 4, Elsevier, pp. 135–196.

Vogt, G. (2009). Konjunkturprognose in Deutschland – Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene. ifo Beiträge zur Wirtschaftsforschung Nr. 36, Ifo Institute – Leibniz-Institute for Economic Research at the University of Munich.

— (2010). VAR-Prognose-Pooling: Ein Ansatz zur Verbesserung der Informationsgrundlage der ifo Dresden Konjunkturprognosen. ifo Dresden berichtet, 17 (2), 32–40.

Vullhorst, U. (2008). Zur indikatorgestützten Berechnung des vierteljährlichen Bruttoinlandsprodukts für Baden-Württemberg. Statistisches Monatsheft Baden-Württemberg, 6 (9), 32–35.

Wallis, K. F. (1986). Forecasting with an econometric model: The ’ragged edge’ problem. Journal of Forecasting, 5 (1), 1–13.

Wenzel, L. (2013). Forecasting Regional Growth in Germany: A panel approach using Business Survey Data. HWWI Research Paper 133.

White, H. (2000). A Reality Check for Data Snooping. Econometrica, 68 (5), 1097–1126. Working Group Regional Accounts VGRdL (2011). Gross domestic product, gross value added in Germany by Bundesland and East-West-Regions. Working Group Regional Accounts VGRdL, Series 1, State results Volume 1, Date of calculation: August 2010 / February 2011, Stuttgart 2011.