The Predictive Content of Business Survey Indicators: evidence from SIGE
| Autor: | Tatiana Cesaroni, Stefano Iezzi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Inflation
Continuous dynamic Economics and Econometrics media_common.quotation_subject 0502 economics and business Synchronization (computer science) Econometrics Economics Business cycle Economic analysis Autoregressive integrated moving average 050207 economics Business and International Management Marketing 050205 econometrics media_common National accounts 05 social sciences Nonparametric statistics jel:E32 jel:C32 Business cycle Business survey data Turning points cyclical analysis Forecast accuracy Macroeconomic forecasts Term (time) Econometric model Single equation Survey data collection Statistics Probability and Uncertainty Volatility (finance) Finance Public finance |
| Popis: | Business surveys indicators represent an important tool in economic analysis and forecasting practices. While there is wide consensus on the coincident properties of such data, there is mixed evidence on their ability to forecast macroeconomic developments in the short term. In this study we extend the previous research on business surveys predictive content by examining for the first time the leading properties of the main business survey indicators coming from the Italian survey on inflation and growth expectations (SIGE). To this end we provide a complete characterization of the business cycle leading/coincident properties of SIGE data (turning points, average duration, synchronization etc.) with respect to the National Accounts reference series using both non parametric approaches (i.e. Harding and Pagan in J Monet Econ 49(2):365–381, 2002) and econometric models (discrete and continuous dynamic single equation models). Overall the results indicate that in both the approaches SIGE business indicators are able to early detect turning points of their corresponding national account reference series in almost all cases. Overall, the average lead of troughs is found to be higher than the average lead of peaks. |
| Databáze: | OpenAIRE |
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