This work investigates in a forecast application whether the forecast accuracy of the US unemployment growth rate can be improved when the time series model is augmented with Google Trends data. The empirical analysis is based on up to 10 distinct Google Trends search terms, which show a sufficiently high correlation with the target variable US unemployment rate. A Google index from the 10 Google search terms is derived by estimating a Factor model with the method of principal component analysis. By using a VAR model it is empirically shown that the forecast accuracy of the US unemployment growth rate can be improved by augmenting the model with the Google search terms.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work investigates in a forecast application whether the forecast accuracy of the US unemployment growth rate can be improved when the time series model is augmented with Google Trends data. The empirical analysis is based on up to 10 distinct Google Trends search terms, which show a sufficiently high correlation with the target variable US unemployment rate. A Google index from the 10 Google search terms is derived by estimating a Factor model with the method of principal component analysis. By using a VAR model it is empirically shown that the forecast accuracy of the US unemployment growth rate can be improved by augmenting the model with the Google search terms. 88 pp. Englisch. Codice articolo 9786200478412
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Da: Books Puddle, New York, NY, U.S.A.
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Da: Majestic Books, Hounslow, Regno Unito
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Da: moluna, Greven, Germania
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yousefi DjamilDjamil Yousefi, M.Sc. University of Konstanz, Major in Econometrics and Applied EconomicsThis work investigates in a forecast application whether the forecast accuracy of the US unemployment growth rate can be impro. Codice articolo 497105853
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This work investigates in a forecast application whether the forecast accuracy of the US unemployment growth rate can be improved when the time series model is augmented with Google Trends data. The empirical analysis is based on up to 10 distinct Google Trends search terms, which show a sufficiently high correlation with the target variable US unemployment rate. A Google index from the 10 Google search terms is derived by estimating a Factor model with the method of principal component analysis. By using a VAR model it is empirically shown that the forecast accuracy of the US unemployment growth rate can be improved by augmenting the model with the Google search terms.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch. Codice articolo 9786200478412
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Da: AHA-BUCH GmbH, Einbeck, Germania
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work investigates in a forecast application whether the forecast accuracy of the US unemployment growth rate can be improved when the time series model is augmented with Google Trends data. The empirical analysis is based on up to 10 distinct Google Trends search terms, which show a sufficiently high correlation with the target variable US unemployment rate. A Google index from the 10 Google search terms is derived by estimating a Factor model with the method of principal component analysis. By using a VAR model it is empirically shown that the forecast accuracy of the US unemployment growth rate can be improved by augmenting the model with the Google search terms. Codice articolo 9786200478412
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Da: preigu, Osnabrück, Germania
Taschenbuch. Condizione: Neu. Using Google Trends Data in Forecasting Economic Variables | Time Series Analysis | Djamil Yousefi | Taschenbuch | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200478412 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 120469724
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