Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model

This thesis studies the effectiveness of Long Short Term Memory (LSTM) model in forecastingfuture Job Openings and Labor Turnover Survey (JOLT) data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openingsin subsequent periods. The performance of the LSTM model is compared with conventional autoregressiveapproaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM model outperforms these traditional models in predicting JOLT job openings, as it not only captures the dependent variable’strends but also harmonized with key economic factors. These results highlight the potential of deep learningtechniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies.Keywords: Labor Market Forecasting, JOLT Job Openings, Economic Indicators, Machine Learning in Economics, Time Series Prediction, LSTM(Long Short Term Memory).