Using non-linear estimation strategies to test an extended version of the Goodwin model on the US economy

Abstract

There is a growing empirical literature that seeks to determine country growth and demand. One possible shortcoming of those studies is their sensitivity to the estimation strategy, which is linked to the time horizon. A profit-led regime has been found to be the most likely outcome when adopting an aggregative approach, while a wage-led regime is the most likely outcome when utilizing a structural estimation. Another potential weakness is that the empirical approaches adopted are mostly linear. To overcome these criticisms, we adopt wavelet analysis, which allows the decomposition of a time series into short- and long-term components, thereby enabling investigation of whether the growth regime switches over time. This paper tests an extended version of the Goodwin model for the US economy using data from 1967 to 2016. The results show that both the growth and demand regimes are sensitive to time and are profit-led in the short term and wage-led in the long term, thereby confirming Blecker's (2016) insight.

The distributive cycle: Evidence and current debates

“…Similarly, Santos and Araujo (2020, fig. 1, p. 277) report wavelet coherencies, although here again in a neo‐Goodwinian context.…”

“…The authors thus demonstrate that high labor shares coincide with high periods of growth. Santos and Araujo (2020), on the other hand, base the conclusion on bivariate Granger tests on wavelet components, which Barrales and von Arnim (2017) also reported (without the associated claims). Kiefer and Rada (2015) and Kiefer et al (2020) investigate the question of the long-run association between distribution and growth conditional on the short-run Goodwin pattern.…”

“…The authors thus demonstrate that high labor shares coincide with high periods of growth. Santos and Araujo (2020), on the other hand, base the conclusion on bivariate Granger tests on wavelet components, which Barrales and von Arnim (2017) also reported (without the associated claims).…”

A Goodwin Model Modification and Its Interactions in Complex Networks

“…This simple model has been extended in many different contributions (a summary of these can be found in [ 23 , 24 , 25 ]) aiming to expand its applicability by considering different scenarios. In these extensions, different dynamics were included and even empirical data seem to be described.…”