Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts

Milas, Costas; Rothman, Philip

doi:10.1016/j.ijforecast.2007.12.003

Cited by 37 publications

(23 citation statements)

References 50 publications

(61 reference statements)

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“…The out-of-sample results confirm the forecasting superiority of the NN approaches against traditional linear and non-linear autoregressive models. Smooth transition vector error-correction models are also used to forecast the unemployment rates, as in the non-Euro G7 countries' study of Milas and Rothman (2008). The proposed model outperforms the linear autoregressive benchmark and improves significantly the forecasts of the US and UK unemployment rate during business cycle expansions.…”

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

“…The accuracy was greater for the case of SVR approach. Smooth transition vector error-correction models were used by Milas and Rothman (2008) to predict the unemployment rate in numerous countries; for the United States, the pooled predictions based on the median value of point forecasts generated by the linear and STVECM forecasts outperformed the naïve predictions. Proietti (2003) compared the accuracy of several predictions based on linear unobserved components models for the monthly unemployment rate in the United States, concluding that the shocks are not persistent during the business cycle.…”

Section: Literaturementioning

confidence: 99%

Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

Simionescu

2015

Naše Gospodarstvo/Our Economy

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This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR) models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations) determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component).

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“…The benefit of this approach, as opposed to a fixed-length rolling window approach (e.g. Milas and Rothman, 2008), is that at any point of time we employ the maximum historical information available. On the other hand, the potential drawback of this approach is the de-emphasized possibility of structural breaks (see Swanson, 1998;Terasvirta et al, 2005).…”

Section: Datamentioning

confidence: 99%

Forecasting ENSO with a smooth transition autoregressive model

Ubilava

Helmers

2013

Environmental Modelling & Software

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This study examines the benefits of nonlinear time series modelling to improve forecast accuracy of the El Niño Southern Oscillation (ENSO) phenomenon. The paper adopts a smooth transition autoregressive (STAR) modelling framework to assess the potentially regime-dependent dynamics of sea surface temperature anomaly. The results reveal STAR-type nonlinearities in ENSO dynamics, resulting in superior out-of-sample forecast performance of STAR over the linear autoregressive models. The advantage of nonlinear models is especially apparent in the short-and intermediate-term forecasts. These results are of interest to researchers and policy makers in the fields of climate dynamics, agricultural production, and environmental management.

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Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts

Cited by 37 publications

References 50 publications

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

Forecasting ENSO with a smooth transition autoregressive model

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