Threshold models with time-varying threshold values and their application in estimating regime-sensitive Taylor rules

2018 ◽  
Vol 23 (5) ◽  
Author(s):  
Yanli Zhu ◽  
Haiqiang Chen ◽  
Ming Lin
Keyword(s):  
Monte Carlo ◽  
Regime Switching ◽  
Latent Variable ◽  
Threshold Model ◽  
Threshold Value ◽  
Time Varying ◽  
Bayesian Approaches ◽  
Taylor Rules ◽  
Homogeneity Assumption ◽  
Proposed Model

Abstract The literature of time series models with threshold effects makes the assumption of a constant threshold value over different periods. However, this time-homogeneity assumption tends to be too restrictive owing to the fact that the threshold value that triggers regime switching could possibly be time-varying. This study herein proposes a threshold model in which the threshold value is assumed to be a latent variable following an autoregressive (AR) process. The newly proposed model was estimated using a Markov Chain Monte Carlo (MCMC) algorithm under a Bayesian framework. The Monte Carlo simulations are presented to assess the effectiveness of the Bayesian approaches. An illustration of the model was made through an application to a regime-sensitive Taylor rule employing U.S. data.

A mathematical model for volatility flocking with a regime switching mechanism in a stock market

2015 ◽  
Vol 25 (07) ◽  
pp. 1299-1335 ◽  
Author(s):  
Hyeong-Ohk Bae ◽  
Seung-Yeal Ha ◽  
Yongsik Kim ◽  
Sang-Hyeok Lee ◽  
Hyuncheul Lim ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Stock Market ◽  
Regime Switching ◽  
Market Volatility ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Financial Applications ◽  
Proposed Model ◽  
The Common ◽  
Geometric Brownian Motions

We present a mathematical model for stock market volatility flocking. Our proposed model consists of geometric Brownian motions with time-varying volatilities coupled with Cucker–Smale (C–S) flocking and regime switching mechanisms. For all-to-all interactions, we assume that all assets' volatilities are coupled to each other with a constant interaction weight, and we show that the common volatility emerges asymptotically and discuss its financial applications. We also provide several numerical simulations and compare them to existing analytical results.

Dynamics of a stochastic susceptible-infective-recovered (SIRS) epidemic model with vaccination and nonlinear incidence under regime switching and Lévy jumps

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Junna Hu ◽  
Buyu Wen ◽  
Ting Zeng ◽  
Zhidong Teng
Keyword(s):  
Epidemic Model ◽  
Regime Switching ◽  
Markov Switching ◽  
Threshold Value ◽  
Open Problems ◽  
Nonlinear Incidence ◽  
Sirs Epidemic Model ◽  
Stochastic Epidemic ◽  
Lévy Jumps ◽  
White Noises

Abstract In this paper, a stochastic susceptible-infective-recovered (SIRS) epidemic model with vaccination, nonlinear incidence and white noises under regime switching and Lévy jumps is investigated. A new threshold value is determined. Some basic assumptions with regard to nonlinear incidence, white noises, Markov switching and Lévy jumps are introduced. The threshold conditions to guarantee the extinction and permanence in the mean of the disease with probability one and the existence of unique ergodic stationary distribution for the model are established. Some new techniques to deal with the Markov switching, Lévy jumps, nonlinear incidence and vaccination for the stochastic epidemic models are proposed. Lastly, the numerical simulations not only illustrate the main results given in this paper, but also suggest some interesting open problems.

On modeling and dynamics of a multiple launch rocket system

2021 ◽  
pp. 095441002098224
Author(s):  
Bo Li ◽  
Xiaoting Rui ◽  
Guoping Wang ◽  
Jianshu Zhang ◽  
Qinbo Zhou
Keyword(s):  
Monte Carlo ◽  
Multibody Systems ◽  
Euler Method ◽  
Dynamics Analysis ◽  
Analysis Problem ◽  
Dynamic Design ◽  
Proposed Model ◽  
And Control ◽  
Rocket System ◽  
Complete Process

Dynamics analysis is currently a key technique to fully understand the dynamic characteristics of sophisticated mechanical systems because it is a prerequisite for dynamic design and control studies. In this study, a dynamics analysis problem for a multiple launch rocket system (MLRS) is developed. We particularly focus on the deductions of equations governing the motion of the MLRS without rockets by using a transfer matrix method for multibody systems and the motion of rockets via the Newton–Euler method. By combining the two equations, the differential equations of the MLRS are obtained. The complete process of the rockets’ ignition, movement in the barrels, airborne flight, and landing is numerically simulated via the Monte Carlo stochastic method. An experiment is implemented to validate the proposed model and the corresponding numerical results.

scholarly journals Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Xu ◽  
Yiwen Wang ◽  
Fang Wang ◽  
Yuxi Liao ◽  
Qiaosheng Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Point Process ◽  
Goodness Of Fit ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Neuronal Response ◽  
Position Estimation ◽  
Monte Carlo Algorithm ◽  
Sequential Monte Carlo Method ◽  
Proposed Model

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

scholarly journals Evaluating Sensitivity of Parameters of Interest to Measurement Invariance in Latent Variable Models

2014 ◽  
Vol 22 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Daniel L. Oberski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Measurement Invariance ◽  
Latent Variable ◽  
Measurement Equivalence ◽  
Latent Variable Models ◽  
Parameter Change ◽  
Electronic Appendix ◽  
Invariance Model ◽  
Parameters Of Interest

Latent variable models can only be compared across groups when these groups exhibit measurement equivalence or “invariance,” since otherwise substantive differences may be confounded with measurement differences. This article suggests examining directly whether measurement differences present could confound substantive analyses, by examining the expected parameter change (EPC)-interest. The EPC-interest approximates the change in parameters of interest that can be expected when freeing cross-group invariance restrictions. Monte Carlo simulations suggest that the EPC-interest approximates these changes well. Three empirical applications show that the EPC-interest can help avoid two undesirable situations: first, it can prevent unnecessarily concluding that groups are incomparable, and second, it alerts the user when comparisons of interest may still be invalidated even when the invariance model appears to fit the data. R code and data for the examples discussed in this article are provided in the electronic appendix (http://hdl.handle.net/1902.1/21816).

Simulation of Thermal Cooling Curves in the Process to Ordering Alloys by the Monte Carlo Method

2021 ◽  
Vol 410 ◽  
pp. 227-234
Author(s):  
Albert R. Khalikov ◽  
Sergey V. Dmitriev
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Stoichiometric Composition ◽  
Square Lattice ◽  
Cooling Curves ◽  
Ordering Kinetics ◽  
The Monte Carlo Method ◽  
Proposed Model ◽  
Coordination Spheres ◽  
Thermal Cooling

An algorithm is proposed for constructing curves of thermal cooling and ordering kinetics with a monotonic decrease in temperature for alloys to stoichiometric composition. Modeling is carried out by the Monte Carlo method in the model of a rigid crystal lattice and pair interatomic interactions. The application of the algorithm is illustrated by the example to a square lattice, taking into account interatomic interactions in the first two coordination spheres for alloys with the composition AB, A3B, and A3B5. The proposed model makes it possible to calculate individual sections of the phase diagrams to the state for binary alloys.

Optimal investment with time-varying transition probabilities for regime switching

2021 ◽  
Vol 29 (2) ◽  
pp. 102-115
Author(s):  
Hyo-Chan Lee ◽  
Seyoung Park ◽  
Jong Mun Yoon
Keyword(s):  
Regime Switching ◽  
Transition Probabilities ◽  
Optimal Investment ◽  
Cash Flows ◽  
Optimal Timing ◽  
Time Varying ◽  
Content Type ◽  
Stochastic Nature ◽  
Optimal Thresholds

Abstract This study aims to generalize the following result of McDonald and Siegel (1986) on optimal investment: it is optimal for an investor to invest when project cash flows exceed a certain threshold. This study presents other results that refine or extend this one by integrating timing flexibility and changes in cash flows with time-varying transition probabilities for regime switching. This study emphasizes that optimal thresholds are either overvalued or undervalued in the absence of time-varying transition probabilities. Accordingly, the stochastic nature of transition probabilities has important implications to the search for optimal timing of investment.

scholarly journals The Impact of Financial Development on Innovation Activities in Emerging and Developing Countries

2019 ◽  
Vol 10 (1) ◽  
pp. 112 ◽  
Author(s):  
KamiliaKamilia LoukilLoukil
Keyword(s):  
Developing Countries ◽  
Financial Development ◽  
Financial Services ◽  
Positive Impact ◽  
Threshold Model ◽  
Threshold Value ◽  
Innovation Activities ◽  
Linear Effects ◽  
Emerging And Developing Countries ◽  
The Impact

We investigate in this paper the effect of financial development on innovation in emerging and developing countries. The estimation of panel threshold model for a sample 54 countries during the period 1980-2009 shows the presence of non linear effects in the relationship between financial development and innovation. We find a threshold value of economic development below which the financial development level has no significant impact on innovation and above which financial development has a significant positive impact on innovation. In sum, our findings suggest that the presence of a healthy economic environment is crucial for financial institutions to offer high-quality financial services, promoting more innovation.

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