Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/216677
Authors: 
Claveria, Oscar
Year of Publication: 
2019
Citation: 
[Journal:] Journal for Labour Market Research [ISSN:] 2510-5027 [Volume:] 53 [Year:] 2019 [Issue:] 3 [Pages:] 1-10
Abstract: 
This study aims to refine unemployment forecasts by incorporating the degree of consensus in consumers' expectations. With this objective, we first model the unemployment rate in eight European countries using the step-wise algorithm proposed by Hyndman and Khandakar (J Stat Softw 27(3):1-22, 2008). The selected optimal autoregressive integrated moving average (ARIMA) models are then used to generate out-of-sample recursive forecasts of the unemployment rates, which are used as benchmark. Finally, we replicate the forecasting experiment including as predictors both an indicator of unemployment, based on the degree of agreement in consumer unemployment expectations, and a measure of disagreement based on the dispersion of expectations. In both cases, we obtain an improvement in forecast accuracy in most countries. These results reveal that the degree of agreement in consumers' expectations contains useful information to predict unemployment rates, especially for the detection of turning points.
Subjects: 
Unemployment
Forecasting
Leading indicator
Economic tendency surveys
Consumer expectations
JEL: 
C51
C52
C53
D12
E24
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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