Econometric and Neural Network Analysis of the Labor Productivity and Average Gross Earnings Indices in the Romanian Industry

Madalina Ecaterina Andreica (), Nicolae Cataniciu and Mugurel Ionut Andreica ()
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Madalina Ecaterina Andreica: CSIE ASE - The Faculty of Economic Cybernetics, Statistics and Informatics, Academia de Studii Economice din Bucureşti - A.S.E. - The Bucharest University of Economic Studies / Academia de Studii Economice din Bucureşti, National Scientific Research Institute for Labor and Social Protection - National Scientific Research Institute for Labor and Social Protection
Nicolae Cataniciu: National Scientific Research Institute for Labor and Social Protection - National Scientific Research Institute for Labor and Social Protection
Mugurel Ionut Andreica: Parallel and Distributed Systems Laboratory [Bucarest] - UPB - Polytechnic University of Bucharest [Romania] = Université Politehnica de Bucarest [Roumanie] = Universitatea Națională de Știință și Tehnologie Politehnica București [România]
Authors registered in the RePEc Author Service: Madalina Ecaterina Popescu

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Abstract: This paper focuses on the identification and short term forecast of the correlation between the Labor Productivity Index (LPI) and the Average Gross Earnings Index (AGEI) in the Romanian Industry. The tools and models that were used consist of several lag econometric models, ARIMA processes, as well as feed forward neural networks. The results proved that the models were suitable and showed a strong correlation between AGEI and LPI.

Keywords: labor productivity; econometric model; ARIMA; VAR; neural network; forecast (search for similar items in EconPapers)
Date: 2009-03-23
Note: View the original document on HAL open archive server: https://hal.science/hal-00768783v1
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Citations: View citations in EconPapers (1)

Published in Proceedings of the 10th WSEAS International Conference on Mathematics and Computers in Business and Economics (MCBE) (ISBN: 978-960-474-063-5 / ISSN: 1790-5109), Mar 2009, Prague, Czech Republic. pp.106-111

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