Forecasting Employment and Unemployment in US: A Comparison between models

Orientador(a): Marcelo Medeiros

Forecasting employment and unemployment is of great importance for mainly all agents in the economy. Employment is one of the main variables analyzed as an an economic indicator, and unemployment serves to policy makers as a guide to their actions. On this essay, I study what features of both series we can use on data treatment and methods used to add to the forecasting predictive power. Using an AR model as a benchmark, I compare machine (Random Forest) and deep (Long Short Term Memory) learning methods, seeking to capture non linearities of both series dynamics. Our findings shows that an AR model with a Random Forest on residuals (as a way to separate linear and non linear part) is the best model for employment forecast, and LSTM is the best for unemployment forecast in longer horizons