A monthly reported rate that demonstrates unemployment change in the U.S. It is calculated by taking the average of daily unemployment rate for each month. Daily unemployment rate is calculated as dividing the total number of unemployed populations by the total number of populations, either employed or unemployed, in the labor market.
Published in Chapter:
U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression
Zichen Zhao (Yale University, USA) and Guanzhou Hou (Johns Hopkins University, USA)
Copyright: © 2022
|Pages: 22
DOI: 10.4018/978-1-7998-8455-2.ch010
Abstract
Artificial neural network (ANN) has been showing its superior capability of modeling and prediction. Neural network model is capable of incorporating high dimensional data, and the model is significantly complex statistically. Sometimes, the complexity is treated as a Blackbox. However, due to the model complexity, the model is capable of capture and modeling an extensive number of patterns, and the prediction power is much stronger than traditional statistical models. Random forest algorithm is a combination of classification and regression trees, using bootstrap to randomly train the model from a set of data (called training set) and test the prediction by a testing set. Random forest has high prediction speed, moderate variance, and does not require any rescaling or transformation of the dataset. This study validates the relationship between the U.S. unemployment rate and economic indices during the COVID-19 pandemic and constructs three different predictive modeling for unemployment rate by economic indices through neural network, random forest, and generalized linear regression model.