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Esade Employment Target (EAPS)

Esade

Esade Employment Target (EAPS)

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The Jobs Target complements the Esade Economic Target and uses the same methodology. It analyses the job market forecasts made by organisations compiled by the Funcas panel in the autumn before the year studied – and compares these forecasts with the average of the four working population known as the Encuesta de Población Activa (EPA) for this year. Deviations are always calculated in absolute value (positive sign) and each point on the graph represents a deviation point on the official unemployment rate (EPA).

Esade Jobs Target Data (EAPS)

The forecasts used are compiled by the FUNCAS panel each September for the year ahead.

The “real unemployment rate” is determined by averaging the unemployment rates recorded in the four economically active population surveys (EAPS) published by the Spanish National Institute of Statistics for the respective year of the forecasts.

Each publication of the Esade Economic Target also analyses:

The degree of deviation in the forecasts for the current year.

The accuracy ranking of the forecasts for the current year and a comparison with the previous year.

An historical comparison of the overall average of forecast deviations.

Calculation of the penalized difference (MSE or mean square error) for each organization that identifies the most accurate prediction models over time.

Calculation of the alpha or directional bias of each organization. This is used to analyze prediction errors over time and identify in which direction they consistently lean.

Evolution vs Deviation

The average deviation is the average of the deviations for all organisations for the year.

 

Forecast vs Reality by institutions

Penalized Difference

For the first time the Esade Target calculates the penalized difference (MSE or mean square error) for each organization that identifies the most accurate prediction models over time.

 

Directional Bias

For the first time the Esade Target calculates the directional bias of each organization. This is used to analyze prediction errors over time and identify in which direction they consistently lean.