Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model
It addresses labor market forecasting for policymakers and stakeholders, but is incremental as it applies an existing deep learning method to economic data.
This thesis tackled forecasting U.S. labor demand by predicting JOLT job openings using an LSTM model, finding it outperformed traditional autoregressive methods like ARIMA and SARIMA.
This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey 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 openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, 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 variables trends but also harmonized with key economic factors. These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies