Abstract
This paper proposes a new version of the Spain-STING (Spain, Short-Term INdicator of Growth), a dynamic factor model used by the Banco de España for the short-term forecasting of the Spanish economy. The extended and revised version of the Spain-STING presented in this document includes a forecast for each of the demand components of the National Accounts. In order to select the indicators that best estimate the Spanish GDP and its demand components, several models are considered. Following this strategy, the selected models are those in which the common factor explains the highest proportion of the variance of the GDP. These models allow us to forecast GDP, private consumption, public expenditure, investment in capital goods, construction investment, exports and imports in a consistent way. We assess the predictive power of the models for GDP and its demand components for the period 2005–2017. With regard to the GDP forecast, we find some improvement of the predictive power compared to the previous version of Spain-STING. As for the demand components, we show that our proposal has better predictive power than other possible time series models.
Highlights
The analysis of the economy’s short-term situation, and the projection of its future course, are fundamental tasks of central banks and national and international institutions
With regard to the GDP forecast, we find some improvement on the previous version of Spain-STING in terms of explained GDP
The Spain-STING model captures the dynamics of each indicator and distinguishes between a common part, captured in the factor, and an idiosyncratic part, which determines the movements of each of the indicators not explained by the dynamics of that common factor
Summary
The analysis of the economy’s short-term situation, and the projection of its future course, are fundamental tasks of central banks and national and international institutions. The Spain-STING (Spain, Short-Term INdicator of Growth) model is a short-term forecasting tool (e.g. one or two quarters ahead) for the quarterly growth rate of the Spanish economy’s in real time GDP [see Camacho and Perez-Quiros (2009, 2011)], i.e., as new data on the explanatory variables are published This model is made up of GDP, in a quarterly frequency, and ten monthly economic indicators that offer information on recent economic developments. The estimation of all the macroeconomic aggregates allows us to forecast real-time GDP and to incorporate information on the components that explain the forecast, making it possible to deepen the analysis of the causes behind changes in GDP forecasts
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