Modelling and Forecasting Unemployment with Nonlinear Time Series Models

Sammanfattning

Accurate forecasting of unemployment rates is important for economic policy, labour market planning, and macroeconomic analysis. However, unemployment time series often show characteristics such as persistence, asymmetry, structural changes, and regime-dependent behaviour that may not be adequately described by traditional linear models. The aim of this thesis is to investigate whether nonlinear time series models can improve unemployment forecasting performance relative to linear approaches. Monthly unemployment data from the United States, the United Kingdom, and Sweden were analysed. Following seasonal adjustment and preliminary data exploration, stationarity and nonlinearity tests were conducted to assess the suitability of nonlinear modelling techniques. Forecasting performance was then evaluated using Seasonal Autoregressive Integrated Moving Average (SARIMA), Self-Exciting Threshold Autoregressive (SETAR), Smooth Transition Autoregressive (STAR), and Markov Switching (MS) models. Forecasts were generated over three horizons: one-step-ahead, one year ahead, and a period corresponding to the final 10% of each dataset. The nonlinearity tests provided strong evidence of nonlinear dynamics in all three unemployment series, supporting the use of nonlinear modelling techniques. The forecasting results showed that nonlinear models can improve forecast accuracy, although their performance varied across countries and forecast horizons. SETAR models achieved the strongest overall performance for the longest forecast horizon, STAR models performed best for one-step-ahead forecasting, while Markov Switching models generally provided the most accurate one-year-ahead forecasts and produced strong results for the Swedish unemployment series. Nevertheless, the SARIMA benchmark remained competitive and often performed comparably to the nonlinear alternatives. Overall, the results suggest that nonlinear models can improve forecasting performance. However, no single model consistently outperformed all others across countries and horizons. The findings therefore indicate that unemployment forecasting benefits from a flexible modelling strategy in which both linear and nonlinear models are considered and evaluated according to the characteristics of the data and the forecasting length.