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Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?

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Abstract

In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral sub-components at the regional level. With an autoregressive distributed lag model we forecast total and sectoral GVA for one German state (Saxony) with more than 300 indicators from different regional levels (international, national and regional) and additionally make usage of several forecast pooling strategies and factor models. Our results show that we are able to increase forecast accuracy of GVA for every sector and for all forecast horizons (one up to four quarters) compared to an autoregressive process. Finally, we show that sectoral forecasts contain more information in the short term (one quarter), whereas direct forecasts of total GVA are preferable in the medium (two and three quarters) and long term (four quarters).

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Notes

  1. Germany consists of 16 different states which are categorized as NUTS 1 for statistics of the European Union. In comparison, Germany is classified as NUTS 0.

  2. Eastern Germany is the aggregation of five German states: Brandenburg, Mecklenburg-West Pomerania, the Free State of Saxony, Saxony-Anhalt and the Free State of Thuringia.

  3. The data are available upon request from dresden@ifo.de.

  4. These six sectors describe the whole economy so that the sum of these sectors equals total GVA.

  5. Drechsel and Scheufele (2012a) state that in most cases simple averages are used for weighting sub-components. In contrast, they use a moving average over the last four quarters to obtain their estimated weights. Since the shares in our sample are relatively persistent, the results should not differ dramatically by applying another approach.

  6. In this paper we denote one quarter (h=1) as short term, two and three quarters (h=2,3) as medium term and four quarters (h=4) as long term. These definitions are in line with the forecasting literature and do not reflect time horizons in macroeconomic theory.

  7. The literature has not found a consesus yet about the level of the discount rate. We apply different values (δ∈{0,0.1,0.2,…,1}) and find similar results. Because of this and to avoid long tables, we only report the outcome for a discount rate equal to 0.1.

  8. We do not take into account the ragged edge problem (see Wallis, 1986) and extract the factors from the information set up to t−1.

  9. To understand the notation in the results section, the following example should make it clear. Imagine a factor model is abbreviated with QML1QSOLS. Then one common factor (1) is extracted via quasi-maximum likelihood (QML) from quarterly data (Q) and the forecast is generated from an OLS estimation. In this case, the factors are obtained from the set of Saxon indicators (S).

  10. Detailed results for all sectors are available upon request.

  11. See Abberger and Wohlrabe (2006) for a recent survey for Germany. For an analysis for Saxony, see Lehmann et al. (2010).

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Correspondence to Robert Lehmann.

Appendix

Appendix

Table 4 Indicators, acronyms and transformations

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Lehmann, R., Wohlrabe, K. Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?. Rev Reg Res 34, 61–90 (2014). https://doi.org/10.1007/s10037-013-0083-8

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