A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis
| Autor: | Carlos Poza, Manuel Monge |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Google trends
Fractional cointegration Cointegration 020209 energy 05 social sciences Monetary policy Leading economic indicators Real estate Business cycle Wavelet analysis 02 engineering and technology General Business Management and Accounting Wavelet Economy Economic indicator Secondary sector of the economy 0502 economics and business 0202 electrical engineering electronic engineering information engineering Mean reversion Economics 050207 economics General Economics Econometrics and Finance |
| Zdroj: | DDFV: Repositorio Institucional de la Universidad Francisco de Vitoria Universidad Francisco de Vitoria DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria instname |
| ISSN: | 2110-7017 |
| DOI: | 10.1016/j.inteco.2020.02.002 |
| Popis: | The main aim of this paper is to build a Real Time Leading Economic Indicator (RT-LEI) that improves Composite Leading Indicators (CLI)’s performance to anticipate GDP trends and turning points for the Spanish economy. The indicator has been constructed using a Factor Analysis and is composed of 21 variables concerning motor vehicle activity, financial activity, real estate activity, economic sentiment, and industrial sector. The data sources used are Google Trends and Thomson Reuters Eikon-Datastream. This work contributes to the literature, studying the dynamics of GDP, CLI and RT-LEI using Fractional Cointegration VAR (FCVAR model) and Continuous Wavelet Transform (CWT) for its resolution. The results show that the model does not present mean reversion and it is expected the RT-LEI reveals a bear trend in the next two years, alike IMF and Consensus FUNCAS′ forecasts. The reasons are mostly associated with escalating global protectionism, uncertainty related to Catalonia and faster monetary policy normalization. pre-print 990 KB |
| Databáze: | OpenAIRE |
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