Identifying US business cycle regimes using dynamic factors and neural network models

Abstract

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Then, dynamic factors are extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in sample and out of sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.

GA-BP neural network modeling for project portfolio risk prediction

“…Then, the errors between the predicted output results and the expected ones are calculated (Kim and Chung, 2020). The reverse propagation process starts if the errors do not satisfy the predefined condition, going from the output layer back to the hidden layer and finally to the input layer (Soybilgen, 2020). The weights and thresholds of the BPNN are continuously adjusted through this loop until the error satisfies the imposed requirements (Li et al, 2011).…”

Addressing Concerns about Single Path Analysis in Business Cycle Turning Points: The Case of Learning Vector Quantization

“…Goodwin [7] explored Hamilton's model on eight developed market economies and made smoothing period and probability threshold decisions based only on the data being evaluated in his study. Some authors explored extensions or alternatives to Hamilton's Markov switching approach, including Birchenhall et al [8], Chauvet and Piger [9], Giusto and Piger [3], Soybilgen [10], and others. These authors may use different criteria for selecting design parameters; however, they all based their selections on the single set of data they used in their study.…”