Threshold model with a time‐varying threshold based on Fourier approximation

Yang, Lixiong; Lee, Chingnun; Chen, I-Po

doi:10.1111/jtsa.12574

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…11 Based on vehicle speed feature recognition, the SMM in Section 2.1 is decomposed into four SMMs corresponding to different vehicle speed features which is called the modified Markov model in this work. Figure 2 shows the transition probability matrix of SMM at the speed range of [30,35), and Figure 3 shows the transition probability matrices of the modified Markov model at the speed range of [30,35). Comparing Figures 2 and 3, it can be seen clearly the evolution of transition probability matrix from the SMM to the modified Markov model.…”

Section: Modified Markov Model Based On Vehicle Speed Feature Recogni...mentioning

confidence: 92%

“…Fourier approximation pointed out that the use of sine and cosine functions as basic functions can effectively fit complex waveforms. 35 Thus, Fourier approximation is utilized as the curve fitting algorithm in this work. The basic formulas of Fourier approximation are shown in equations ( 8), (9), and (10).…”

Section: Modified Markov Model Based On Vehicle Speed Feature Recogni...mentioning

confidence: 99%

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Approximate optimal energy management with a high-precision vehicle speed prediction algorithm

Feng

Han

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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A predictive energy management strategy is proposed for plug-in hybrid electric vehicles, which adopts a new high-precision vehicle speed prediction algorithm and a real-time reference state of charge (SOC) trajectory planning method. The speed prediction algorithm includes a modified Markov model based on vehicle speed features recognized by the self-organizing map neural network method, Fourier approximation, and moving average filtering. The prediction accuracy of the algorithm, measured by the root mean square error, is improved by 32.28% and 23.33%, respectively, over that of the standard Markov model when the prediction time is 5 and 10 s. To approach the optimal management strategy for improved vehicle fuel economy, the radial basis function neural network method is utilized to correlate the 10 s predicted vehicle speed with the trajectory slope of the suboptimal SOC for obtaining the reference SOC trajectory, which is applied to the adaptive equivalent consumption minimization strategy based on SOC feedback. The simulation indicates that the fuel consumption by using the proposed energy management strategy is very close to that by the theoretical suboptimal strategy with the shooting method, which leads to a superior fuel economy among the strategies studied.

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Section: Modified Markov Model Based On Vehicle Speed Feature Recogni...mentioning

confidence: 92%

Section: Modified Markov Model Based On Vehicle Speed Feature Recogni...mentioning

confidence: 99%

Approximate optimal energy management with a high-precision vehicle speed prediction algorithm

Feng

Han

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…However, when financial development lies below the threshold value (that is, 0.151), its impact on economic growth is negative. Yang et al [33] extend Hansen's [34] constant threshold regression model by allowing for a time-varying threshold which is approximated by a Fourier function. Least-square estimation of regression slopes and the time-varying threshold is proposed, and test the existence of threshold effect and find there is little efficiency loss by the allowance for…”

Section: The Literature Reviewmentioning

confidence: 99%

Threshold analysis regarding the optimal tax rate and tax evasion. Empirical evidence from Taiwan

Wang¹,

Zhang²

2023

PLoS ONE

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For a long time, governments of all countries have attached great importance to the development of underground economic activities. The reason is that the characteristics of the underground economy are hidden and the information disclosure is not sufficient, which not only distorts the economic data indicators, but more importantly, the existence of the underground economy has led to the loss of a large amount of tax base, affecting the long-term economic development of the country. Whether raising the tax burden rate boosts the tax revenue or expand the scale of the underground economy. In this paper, we use Kuznet Tax Curve (KTC) method to analyze the relationship between GDP and TTR/DTR/ITR. We find that the tax base erosion rate of indirect tax is lower than that of direct tax. In addition, we explore the relationship among economic growth, tax rate and tax revenue and adopt SUR-OLS method and Threshold approach to estimate the response of economic growth on total tax revenue(TTR), direct tax revenue(DTR) and indirect tax revenue (ITR) in Taiwan from 1991-2020. Our empirical research shows that when DTR tax rates are between 12.59% and 13%, an increase in income leads to a decrease, not an increase, in DTR, leading to severe tax base erosion. That is, the relationship between GDP and DTR presents a N-shaped relationship. However, ITR does not exist any tax rate threshold effect. Obviously, with the increase of GDP, ITR also increases. This reflects that the difference of tax structure between direct tax and indirect tax plays a key role in the relationship between tax rate and tax base erosion.

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“…Chen et al (2012) consider a threshold regression model with two separate exogenous threshold variables, while further allow two thresholds to be endogenous. Yang et al (2021) extend Hansen (2000) by allowing for a time-varying threshold. Miao et al (2020) study a panel threshold model with latent group structures.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Inference for the Threshold Model with Hybrid Stochastic Local Unit Root Regressors

Chen

Stengos

2022

JRFM

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In this paper, we study the estimation and inference of the threshold model with hybrid local stochastic unit root regressors. Our main contribution is to propose an estimator that generalizes the threshold model with various forms of nonstationary regressors and to obtain its limiting distribution theory. In particular, our proposed model generalizes the threshold model with unit root, local-to-unity, and stochastic unit root regressors. We provide the estimation strategy for the least squares estimator and derive the asymptotic results for the proposed estimator. Depending on the diminishing rate of the threshold effect, we find that the limiting distribution of the threshold estimator takes different forms. Monte Carlo simulations are used to assess our proposed estimator’s finite sample performance, which is found to perform well.

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Threshold model with a time‐varying threshold based on Fourier approximation

Cited by 11 publications

References 27 publications

Approximate optimal energy management with a high-precision vehicle speed prediction algorithm

Approximate optimal energy management with a high-precision vehicle speed prediction algorithm

Threshold analysis regarding the optimal tax rate and tax evasion. Empirical evidence from Taiwan

Estimation and Inference for the Threshold Model with Hybrid Stochastic Local Unit Root Regressors

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