Munich Personal RePEc Archive
Login | Create Account

A monthly indicator of employment in the euro area: real time analysis of indirect estimates

Moauro, Filippo (2010): A monthly indicator of employment in the euro area: real time analysis of indirect estimates. Unpublished.

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
513Kb

Abstract

The paper presents the results of an extensive real time analysis of alternative model-based approaches to derive a monthly indicator of employment for the euro area. In the experiment the Eurostat quarterly national accounts series of employment is temporally disaggregated using the information coming from the monthly series of unemployment. The strategy benefits of the contribution of the information set of the euro area and its 6 larger member states, as well as the split into the 6 sections of economic activity. The models under comparison include univariate regressions of the Chow and Lin' type where the euro area aggregate is directly and indirectly derived, as well as multivariate structural time series models of small and medium size. The specification in logarithms is also systematically assessed. The largest multivariate setups, up to 49 series, are estimated through the EM algorithm. Main conclusions are the following: mean revision errors of disaggregated estimates of employment are overall small; a gain is obtained when the model strategy takes into account the information by both sector and member state; the largest multivariate setups outperforms those of small size and the strategies based on classical disaggregation methods.

Item Type:MPRA Paper
Language:English
Keywords:temporal disaggregation methods, multivariate structural time series models, mixed-frequency models, EM algorithm, Kalman filter and smoother
Subjects:C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models; Dynamic Quantile Regressions
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation and Selection
C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions
ID Code:27797
Deposited By:Filippo Moauro
Deposited On:02. Jan 2011 20:04
Last Modified:02. Jan 2011 20:04
References:

Chow, G. & Lin, A. L. (1971), ‘Best linear unbised interpolation, distribution and extrapolation of time series by related series’, The Review of Economics and Statistics 53(4), 372-375

de Jong, P. (1991), ‘The di¤use kalman …lter’, The Annals of Statistics 19(2), 1073-83

Durbin, J. & Koopman, S. J. (2001), Time Series Analysis by State Space Methods, Oxford University Press

Fahrmeir, L. (1992), ‘Posterior mode estimation by extended kalman …fltering for multivariate dynamic generalised linear models’, Journal of the American Statistical Association 97, 501-509

Fernández, F. & Harvey, A. (1990), ‘Seemingly unrelated time series equations and a test for homogeneity’, Journal of Business and Economic Statistics 8, 71-81

Frale, C., Marcellino, M., Mazzi, G. L. & Proietti, T. (2010a), ‘Euromind: A monthly indicator of the euro area economic conditions’, Journal of the Royal Statistical Society (Series A - Statistics in Society) . Forthcoming

Frale, C., Marcellino, M., Mazzi, G. L. & Proietti, T. (2010b), ‘Survey data as coicident or leading indicators’, Journal of Rorecasting 29, 109-131

Harvey, A. C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press

Harvey, A. C. & Chung, C. (2000), ‘Estimating the underlying change in unemployment in the uk’, Journal of the Royal Statistical Society A 163, 303-39

Harvey, A. & Koopman, S. (1997), Multivariate structural time series models (with comments), in B. H. C. Heij, J.M. Shumacher & C. Praagman, eds, ‘System Dynamics in Economic and Financial Models’, Chichester: John Wiley and Sons Ltd., pp. 269-98

Koopman, S. (1993), ‘Disturbance smoother for state space models’, Biometrika 80, 117-26

Koopman, S. J., Harvey, A. C., Doornik, J. A. & Shephard, N. (2009), STAMP 8.2: Structural Time Series Analyser, Modeller and Predictor, Timberlake Consultants Press

Litterman, R. B. (1983), ‘A random walk, markov model for the distribution of time series’, Journal of Business and Economic Statistics 1, 169-173

Moauro, F. & Savio, G. (2005), ‘Temporal disaggregation using multivariate structural time series models’, The Econometrics Journal 8, 214-234

Proietti, T. (2006), ‘On the estimation of nonlinearly aggregated mixed models’, Journal of Computational and Graphical Statistics 15(1), 18-38

Proietti, T. (2006), ‘Temporal disaggregation by state space methods: Dynamic regression methods revisited’, Econometrics Journal 9, 357-372

Proietti, T. (2008), Estimation of common factors under cross-sectional and temporal aggregation constraints: Nowcasting monthly gdp and its main components. MPRA Paper No. 6860, http://mpra.ub.uni-meunchen.de/6860/

Proietti, T. & Moauro, F. (2006), ‘Dynamic factor analysis with non-linear temporal aggregation constraints’, Applied Statistics 55(2), 281-300

Stock, J. H. & Watson, M. (1991), A probability model of the coincident economic indicators, in G. Moore & K. Lahiri, eds, ‘Leading Economic Indicators’, Cambridge University Press

Repository Staff Only: item control page

LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.