Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques

Olmedo, Elena

doi:10.1007/s10614-013-9371-1

Cited by 16 publications

(8 citation statements)

References 54 publications

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“…Finally, Olmedo (2013) performs a competition between non-linear models to forecast different European unemployment rate time series. The best results are provided by a vector autoregressive and baricentric predictor, but as the forecasting horizon lengthens the performance deteriorates.…”

Section: Introductionmentioning

confidence: 99%

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Sermpinis

Stasinakis

Theofilatos

et al. 2014

Journal of Forecasting

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In this paper a hybrid Genetic Algorithm -Support Vector Regression (GA-SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting the US inflation and unemployment. The GA-SVR genetically optimizes the SVR parameters and adapts to the optimal feature subset from a feature space of potential inputs. The feature space includes a wide pool of macroeconomic variables that might affect the two series under study.

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Section: Introductionmentioning

confidence: 99%

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Sermpinis

Stasinakis

Theofilatos

et al. 2014

Journal of Forecasting

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show abstract

“…As regards the Spanish case, García-Cintado et al (2014), Altuzarra (2015) and Cuestas and Gil-Alana (2017) have analyzed the statistical properties of the unemployment rate over several decades starting from 1976. Interesting proposals to forecast the aggregate unemployment rate have been made by Olmedo (2014) and Vicente et al (2015). However, an aggregate forecast is not enough to take into account the different dynamics of unemployment across demographic groups.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Composition of Spain’s Unemployed Population by Genetic Algorithms

Hernandez-Lopez¹,

Hernández²

2018

ECECSR

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“…It is, thus, not surprising that NNs continue to receive a great deal of attention in the literature (Huang et al 2013;Özkan 2013;Fernandes et al 2014;Olmedo 2014). …”

Section: Introductionmentioning

confidence: 99%

“…The proposed model outperforms the linear autoregressive benchmark and improves significantly the forecasts of the US and UK unemployment rate during business cycle expansions. Olmedo (2014) performs a competition between non-linear models, including NNs and Nearest Neighbour algorithms, to forecast different European unemployment rate time series. The best results are provided by a vector autoregressive and baricentric predictor.…”

Section: Introductionmentioning

confidence: 99%