L. Vanessa Smith
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199670086
- eISBN:
- 9780191749469
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199670086.003.0008
- Subject:
- Economics and Finance, Macro- and Monetary Economics
This chapter considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end the global vector autoregressive (GVAR) model ...
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This chapter considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end the global vector autoregressive (GVAR) model introduced in Chapter 2, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1-2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. The effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, the heterogeneity of the economies considered, as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output and inflation.Less
This chapter considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end the global vector autoregressive (GVAR) model introduced in Chapter 2, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1-2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. The effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, the heterogeneity of the economies considered, as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output and inflation.
