A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis
Document Type
Article
Publication Date
6-1-2024
Abstract
This paper proposes a nonlinear fractional unit root approach which is known as the autoregressive neural network-fractional integration (ARNN-FI) test. This new fractional integration test is based on a new multilayer perceptron of a neural network process, proposed in Yaya et al. (Oxf Bull Econ Stat 83(4):960-981, 2021). The asymptotic theory and the properties of the proposed test are given. By setting up a Monte Carlo simulation experiment, the simulation results reveal that as the number of observations increases, size and power distortions would disappear in the test. The empirical application based on this new test reveals that the unemployment rates of three European countries are neither stationary nor mean-reverting in line with the hysteresis hypothesis.
Keywords
Autoregressive neural network, Fractional integration, Hysteresis, Unemployment, C15, C22, C45
Divisions
aei,Faculty_of_Business_and_Accountancy
Funders
Ministerio de Ciencia e Innovacin
Publication Title
Empirical Economics
Volume
66
Issue
6
Publisher
Springer
Publisher Location
PO BOX 10 52 80, 69042 HEIDELBERG, GERMANY