Conditional Threshold Autoregression (CoTAR)

2021 ◽  
Author(s):  
Kaiji Motegi ◽  
Jay Dennis ◽  
Shigeyuki Hamori
Keyword(s):  
Threshold Autoregression

scholarly journals Modeling mechanism of economic growth using threshold autoregression models

2018 ◽  
Vol 58 (3) ◽  
pp. 1381-1430 ◽  
Author(s):  
Magdalena Osińska ◽  
Tadeusz Kufel ◽  
Marcin Błażejowski ◽  
Paweł Kufel
Keyword(s):  
Economic Growth ◽  
Threshold Autoregression ◽  
Autoregression Models

scholarly journals The Making of a National Currency: Spatial Transaction Costs and Money Market Integration in Spain (1825–1874)

2019 ◽  
Vol 79 (4) ◽  
pp. 1094-1128 ◽  
Author(s):  
Pilar Nogues-Marco ◽  
Alfonso Herranz-Loncán ◽  
Nektarios Aslanidis
Keyword(s):  
Nineteenth Century ◽  
Transaction Costs ◽  
Economic Geography ◽  
Market Integration ◽  
Money Market ◽  
Threshold Autoregression ◽  
Postal Services ◽  
Heterogeneous Behavior ◽  
Autoregression Model ◽  
Market Convergence

This article analyzes the integration of the Spanish money market in the nineteenth century. We use a Band-Threshold Autoregression model of prices of bills-of-exchange in ten cities to measure market convergence and efficiency in 1825–1875. While price gaps generally decreased during the period, progress in efficiency was limited to a small group of cities. We suggest that convergence was associated to the reduction in transaction costs, which started well before the railways through improvements in roads and postal services. By contrast, the heterogeneous behavior of efficiency might be associated to economic geography changes and their effects on monetary leadership.

EMPIRICAL ANALYSIS OF BITCOIN PRICES USING THRESHOLD TIME SERIES MODELS

2018 ◽  
Vol 13 (04) ◽  
pp. 1850017
Author(s):  
RODOLFO ANGELO MAGTANGGOL III DE GUZMAN ◽  
MIKE K. P. SO
Keyword(s):  
Empirical Analysis ◽  
Threshold Model ◽  
Time Series Models ◽  
Network Characteristics ◽  
Threshold Autoregression ◽  
Block Number ◽  
Threshold Time ◽  
History Of ◽  
Threshold Time Series ◽  
The Empirical Analysis

This paper proposes the use of threshold heteroskedastic models which integrate threshold nonlinearity [Tong, H (1978). On a Threshold Model, pp. 575–586. Netherlands: Sijthoff & Noordhoff; Tong, H and KS Lim (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 3, 245–292.] and GARCH-type conditional variance for modeling Bitcoin returns to provide an understanding on the huge volatility that Bitcoin has been famous for. Specifically, the model attempts to identify different regimes throughout the history of Bitcoin using the different available Bitcoin network characteristics, such as cost per transaction, number of transactions per block, number of active addresses and number of transactions. Estimation and diagnostic checks are performed using Markov chain Monte Carlo methods. In the empirical analysis, we show that our model is able to identify periods of crashes as one of these regimes, which is also a period of declining returns and declining number of active users. We also find that the number of users and the number of transactions determine the magnitude or persistence of a crash period.

scholarly journals Dissecting Tether’s Nonlinear Dynamics during Covid-19

2020 ◽  
Vol 6 (4) ◽  
pp. 161
Author(s):  
Moinak Maiti ◽  
Zoran Grubisic ◽  
Darko B. Vukovic
Keyword(s):  
Time Series ◽  
Regime Switching ◽  
Return Time ◽  
The Other ◽  
Smooth Transition ◽  
Daily Average ◽  
Star Models ◽  
Average Return ◽  
Threshold Autoregression ◽  
Highly Nonlinear

The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.

scholarly journals Testing for Threshold Autoregression

1990 ◽  
Vol 18 (4) ◽  
pp. 1886-1894 ◽  
Author(s):  
K. S. Chan
Keyword(s):  
Threshold Autoregression
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