Modeling mechanism of economic growth using threshold autoregression models

Abstract

We propose to apply a time series-based nonlinear mechanism in the threshold autoregression form in order to examine the possible relationship between economic growth rate and its potential determinants included debt-to-GDP indicator. Our approach employs threshold variables instead of exogenous variables and time-series data instead of panel data to reveal the economic instruments that have determined the business cycle in European countries for the last 2 decades-starting from 1995. The purpose of the study is to reveal the mechanism of growth (measured in terms of GDP growth rate and industrial production growth rate) given different macroeconomic indicators, such as public debt, rate of inflation, interest rate, and rate of unemployment with the level of growth itself serving as the threshold variables. We identify that the monetary mechanism played an important role in diagnosing the phases of business cycle in most European economies which is in line with liberal economic policy dominating in the observed period. The initial level of debt-to-GDP ratio as its increase within the recession period was of no value for the economic growth pattern.

Research on Carbon Allowance Price Non-Linear Structure Characteristics and Regime Switching Mechanism in China's Carbon Market

“…TAR model sets a particular point in time where the motion of the time series jumps from one regime to another, while this jump is discrete. TAR model has better properties in fitting the actual data, compared with the linear regression model, due to its advantages in effectively identifying the nonlinear dynamic adjustment characteristics and regime transition of the time series [42]. The three-regime multi-order TAR model has the following form:…”

Finding the Tipping Point: When Heterogeneous Evaluations in Social Media Converge and Influence Organizational Legitimacy

“…Third, methodologically, our study captures the longitudinal discursive evolution of legitimacy evaluations by combining new digital methods such as semantic network analysis (Bonini et al, 2016;De Nooy et al, 2005;Illia et al, 2016Illia et al, , 2021 with threshold vector autoregressive model (TVAR) analysis, the latter being widely used in economics to capture business cycles (Beaudry & Koop, 1993;Osińska et al, 2020;Pesaran & Potter, 1997), interest rates (Anderson, 1997;Nyberg, 2018;Pfann et al, 1996), stock returns (Chen & Yang, 2019;Domian & Louton, 1997), prices (Aslan et al, 2018;Yadav et al, 1994), and exchange rates (Balke & Wohar, 1998;Taylor, 2001). Thereby, our study contributes to studies on organizational legitimacy in social media (Etter et al, 2018) and more broadly to studies on social approval of organizations (Bundy & Pfarrer, 2015;Wang et al, 2021) by suggesting means of combining different methods to measure the point at which a critical mass of evaluations in social media will or will not influence organizational news media legitimacy.…”