Evaluating Forecasting Models for Unemployment Rates by Gender in Selected European Countries (original) (raw)
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The evaluation and improvement of forecasts accuracy generate growth in the quality of decisional process. In Romania, the most accurate predictions for the unemployment rate on the forecasting horizon 2001-2012 were provided by the Institute for Economic Forecasting (IEF) that is followed by European Commission and National Commission for Prognosis (NCP). The result is based on U1, but if more indicators are taken into consideration at the same time using the multi-criteria ranking, the conclusion remains the same. A suitable strategy for improving the degree of accuracy for these forecasts is represented by the combined forecasts. The accuracy of NCP predictions can be improved on the horizon 2001-2012, if the initial values are smoothed using Holt-Winters technique and Hodrick-Prescott filter. The use of Monte Carlo method to simulate the forecasted unemployment rate proved to be the best way to improve the predictions accuracy. Starting from an AR(1) model for the interest varia...
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Combining forecast values based on simple univariate models may produce more favourable results than complex models. In this study, the results of combining the forecast values of Naïve model, Single Exponential Smoothing Model, The Autoregressive Moving Average (ARIMA) model, and Holt Method are shown to be superior to that of the Error Correction Model (ECM).Malaysia's unemployment rates data are used in this study. The independent variable used in the ECM formulation is the industrial production index. Both data sets were collected for the months of January 2004 to December 2010. The selection criteria used to determine the best model, is the Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Initial findings showed that both time series data sets were not influenced by the seasonality effect.
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This study aims to refine unemployment forecasts by incorporating the degree of consensus in consumers’ expectations. With this objective, we first model the unemployment rate in eight European countries using the step-wise algorithm proposed by Hyndman and Khandakar (J Stat Softw 27(3):1–22, 2008). The selected optimal autoregressive integrated moving average (ARIMA) models are then used to generate out-of-sample recursive forecasts of the unemployment rates, which are used as benchmark. Finally, we replicate the forecasting experiment including as predictors both an indicator of unemployment, based on the degree of agreement in consumer unemployment expectations, and a measure of disagreement based on the dispersion of expectations. In both cases, we obtain an improvement in forecast accuracy in most countries. These results reveal that the degree of agreement in consumers’ expectations contains useful information to predict unemployment rates, especially for the detection of turning points.