Abstract
The rapid growth of the emerging markets and of China in particular has changed the economic landscape: emerging Asias share of world trade has grown from about 13% in 1990 to almost 23% in 2008, and its aggregate GDP now accounts for more than 25% of world output, compared with less than 12% in 1990. In this paper we focus on the consequences for the assessment of the global outlook and the specification of forecasting equations. Our main results are that (1) the rise of the emerging countries has led to a sharp change in the correlation of growth rates among main economic areas; (2) this is clearly detectable in forecasting equations too, as a structural break occurring in the 1990s; (3) hence, inferences about global developments based solely on the industrialized countries are highly unreliable; (4) the otherwise cumbersome task of monitoring many and little-known countries can be tackled by resorting to very simple bridge models (BM); (5) BM performance is in line with that of the most widely quoted predictions (WEO, Consensus Forecasts) both before and during the recent crisis; and (6) for some emerging economies, BMs would have provided even better forecasts during the recent crisis.
The opinions expressed here do not reflect those of the Bank of Italy. The usual disclaimer applies. PRIN funding is gratefully acknowledged (R. Golinelli).
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- 1.
Acronym derived from the initials of Brazil, Russia, India and China.
- 2.
- 3.
The International Monetary Fund (IMF) decided to publish two updates of its World Economic Outlook (WEO) projections, in January and July, to bridge the complete WEO projections released in April and October, in conjunction with the semi-annual meetings of the Fund.
- 4.
- 5.
In comparing GDP levels and growth rates, as well as in weighting trade flows and correlation patterns, we focused on the period prior to the world economic crisis (i.e. before 2009). We turn to an analysis of the impact of the financial turmoil on economic performance of the main areas and its predictability in the last section of the paper.
- 6.
- 7.
Country groupings (JEU, ASE and BRRU) are defined in the introduction. Details regarding GDP and other data sources are in the Appendix A1; GDP growth is given by the first differences of log-levels. We found that \( y_t^W - {w^{{JEU}}}y_t^{{JEU}} - {w^{{ASE}}}y_t^{{ASE}} - {w^{{BRRU}}}y_t^{{BRRU}} \)~ \( I(1) \) hence a stable co-integrating relationship cannot be found owing to pervasive and significant parameter (weight) changes over the sample period, as one would expect given the evidence in Sect. 2.1.
- 8.
The first-order dynamics is enough to have non-autocorrelated reduced-form residuals.
- 9.
Even though we do not consider data revisions this fact does not necessarily lead to an artificial improvement in our model’s forecasting ability. In fact, Croushore and Stark (2001, 2002), modelling US GDP growth, do not find a significant difference between the forecast errors generated using real-time data or latest-available data. The same result is broadly confirmed for other countries (see e.g. Golinelli and Parigi 2008, for Italy).
- 10.
For a comparison and a discussion of BM and DF approaches see Bulligan et al. (2010).
- 11.
- 12.
- 13.
This intuition is confirmed by comparing – over the common sample 2000q1–2003q4 – the forecasting performance of our raw BMs with that of the carefully specified BMs for the advanced countries reported in Golinelli and Parigi (2007).
- 14.
Four more parsimonious models, nested in (3), can be obtained by imposing parameter restrictions: (3-i) the ARDL(3,3) in log-levels; (3-ii) the ARDL(2,2) in log-levels; (3-iii) the ARDL(1,1) in differences (i.e. which omits all log-levels); and (3-iv) the static model in differences ARDL(0,0). We select the best model out of these five alternatives by minimizing the Schwarz criterion.
- 15.
The size of the rolling widow to estimate AR models parameters is set to 7 years (84 months) for all countries, as in Bulligan et al. (2010). To estimate BM model parameters we set windows of 20 years (80 quarters) for the JEU countries while, to avoid the effects of possible breaks, in the ASE and BRRU specifications we choose a shorter window of 15 years (60 quarters).
- 16.
In each of the 120 monthly rounds and for each country, the benchmark AR models for first-difference log-GDP are selected by using the Schwarz criterion over a range of lags from 0 to 4.
- 17.
BM forecasts of Chinese GDP have a lower RMSE with respect to the other Asian economies and improve markedly with respect to the AR benchmark.
- 18.
WEO projections are released in April and October of each year. A more detailed description of these exercises, and a complete documentation of the results are reported in a previous version of this work, available at http://www.bancaditalia.it/studiricerche/convegni/atti/chinese-economy/sessione1/borini/Borin_1.pdf
- 19.
Obviously, what is said here for the WBM can be replicated for the single BMs of countries and country groups.
- 20.
During this period the IMF published forecast updates every other quarter, thus effectively providing a new scenario for the world outlook every 3 months.
- 21.
As Consensus does not publish world output growth, we computed it as the weighted sum of the following countries: USA, Japan, Germany, France, United Kingdom, Italy and Spain (for JEU), and the four single BRIC countries. Weights – constant over time – are derived from IMF (2010), World Economic Outlook, April, p. 148.
- 22.
The NBER dating committee has recently agreed to pinpoint June 2009 as the trough month in the US for the recession that started in December 2007, according to the same institution (see http://www.nber.org/cycles/sept2010.html).
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Appendix A: Additional Tables and Graphs CORREZIONI: Source Con Maiuscolo Ovunque
Appendix A: Additional Tables and Graphs CORREZIONI: Source Con Maiuscolo Ovunque
Comparison of monthly forecast patterns of world GDP growth for 2009 between different WBM specifications, WEO and Consensus predictions
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Borin, A., Cristadoro, R., Golinelli, R., Parigi, G. (2012). Forecasting World Output: The Rising Importance of Emerging Asia. In: Gomel, G., Marconi, D., Musu, I., Quintieri, B. (eds) The Chinese Economy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28638-4_2
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