Macroeconomic nowcasting using Google probabilities
Koop, Gary and Onorante, Luca (2018) Macroeconomic nowcasting using Google probabilities. Advances in Econometrics. ISSN 0731-9053 (In Press)
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Abstract
Many recent papers have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These papers construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coe¢ cient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call ìGoogle probabilitiesî: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we Önd DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS.
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Item type: Article ID code: 66415 Dates: DateEvent5 November 2018Published5 November 2018AcceptedSubjects: Social Sciences > Economic Theory Department: Strathclyde Business School > Economics Depositing user: Pure Administrator Date deposited: 18 Dec 2018 12:55 Last modified: 11 Apr 2024 01:36 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/66415