Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/263252 
Year of Publication: 
2015
Citation: 
[Journal:] Applied Economics [ISSN:] 1466-4283 [Volume:] 47 [Issue:] 33 [Publisher:] Taylor & Francis [Place:] London [Year:] 2015 [Pages:] 3540-3558
Publisher: 
Taylor & Francis, London
Abstract: 
The paper investigates the predictive power of a new survey implemented by the Federal Employment Agency (FEA) for forecasting German unemployment in the short run. Every month, the CEOs of the FEA's regional agencies are asked about their expectations of future labor market developments. We generate an aggregate unemployment leading indicator that exploits serial correlation in response behavior through identifying and adjusting temporarily unreliable predictions. We use out-of-sample tests suitable in nested model environments to compare forecasting performance of models including the new indicator to that of purely autoregressive benchmarks. For all investigated forecast horizons (1, 2, 3 and 6 months), test results show that models enhanced by the new leading indicator significantly outperform their benchmark counterparts. To compare our indicator to potential competitors we employ the model confidence set. Results reveal that models including the new indicator perform very well at the 10 percent level.
Subjects: 
Survey Data
Forecast Evaluation
Nested Models
Model Confidence Set
Unemployment
JEL: 
C22
C52
C53
E24
Published Version’s DOI: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article
Document Version: 
Accepted Manuscript (Postprint)

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