Fixed Transition Probabilities Research Articles

Examining the behaviour of inflation to supply and demand shocks using an MS-VAR model

This paper examines how inflation reacts depending on whether a supply (cost) or demand (markup) shock occurs. Despite their importance, the behaviour of markups remains an open empirical question in the literature. We use data for the US over the 1948q1-2019q3 period, decompose the price index to markups and costs, and employ a small-scale DSGE model to extract identifying size conditions for the coefficient estimates. These are then used in a Markov-switching VAR (MS-VAR) with fixed transition probabilities using an updating step. The empirical exercise shows that three different regimes exist (expansionary, contractionary, supply shock), while the Generalized Impulse Response Functions document that markups appear to be countercyclical and marginal costs are procyclical across all regimes. As such, inflation's reaction to a shock can be less volatile than expected depending on the regime. In addition, larger shocks have a lower and less persistent effect on inflation, because they are more easily identifiable, allowing corrective action to be taken.

Improved FPA for aircraft conceptual design

Aircraft weight design is a key technology in aircraft conceptual design. The design of aircraft weight is directly related to many design parameters of the aircraft, and there are complex constraint relationships between these parameters. It is difficult to determine the design parameters in the process of aircraft conceptual design with an accurate mathematical analytical model, it is necessary to use an optimization algorithm. Firstly, to build the optimization object function, the aircraft weight function and some constrain functions are given out, thus, the aircraft weight design problem is transformed into a single objective multi-constraint optimization problem, aiming at the problem of parameter coupling, Aitken acceleration algorithm is proposed to solve the problem through iterative, thus, and a feasible solution of aircraft parameters are obtained; Secondly, considering the advantages of flower pollination algorithms in solving unconstrained problems, the flower pollination algorithm (FPA) combined with the Aitken acceleration algorithm is proposed for automatic global optimization, to avoid the FPA falling into the local optimal solution or missing optimal solution, propose improvements to the FPA algorithm from three aspects: adaptive conversion probability, adaptive step size adjustment, and adaptive reproduction probability generation. To use FPA, the multi-constraint optimization problem is transformed into a multi-parameter unconstrained optimization problem with a penalty term based on the penalty function method. Finally, a design example is given to verify the effectiveness of the algorithm, and the superiority of the improved algorithm is verified by comparing it with PSO and the fixed transition probability FPA.

Finite-Time Control for Discrete-Time Positive Systems Subject to Event-Triggered Scheme and Markov Jump Parameters

This brief investigates the finite-time control problem for discrete-time positive systems subject to event-triggered scheme and Markov jump parameters. Switching transition probability is used to extend the fixed transition probability, which is more suitable to describe the performance of Markov jump systems (MJSs). On the basis of the 1-norm, a developed event-triggered scheme is proposed to be associated with the sample signal and the actual signal. Furthermore, a linear Lyapunov function and an average dwell time approach are adopted to obtain sufficient conditions for guaranteeing stochastic finite-time stability of the underlying systems. Additionally, a matrix decomposition strategy is constructed to design a finite-time event-triggered control law such that the corresponding MJSs are positive and stochastically finite-time bounded. All conditions are proposed on the basis of linear programming. Finally, a local railway transportation model is provided to verify the practicability of the strategy.

Synchronization of semi-Markovian switching complex networks with partial unknown transition rates via event-triggered control

This article investigates the synchronization of semi-Markovian switching complex networks with partial unknown transition rates. Different from Markovian switching system, semi-Markov process is characterized by a fixed transition probability matrix and a probability density function of the sojoturn time, and the sojourn time obeys an expanded exponential distribution, which relaxes the restriction that the sojoturn time obeys memoryless exponential distribution. To realize synchronization, event-triggered control is applied which effectively reduced the control cost. It is demonstrated that the semi-Markovian switching complex networks can realize synchronization under appropriate control gain, event-triggered parameter and average sojourn time. Besides, to cope with the uncertainties that may occur during the switching process, the synchronization of the semi-Markovian switching complex networks with parameter uncertainties and external disturbances is also discussed. In the end, two simulation examples are provided to verify the feasibility of our results.

Price decomposition and asymmetry in various regimes of the economy

This paper decomposes price into its 2 major constituents, namely markup (Mkp) and marginal cost (MC) with which a Markov-switching VAR with fixed transition probabilities is estimated. Since the proposed pair of variables has not been extensively analysed, a theoretical model that derives markups and marginal costs as functions of parameters and shocks is developed to extract identifying restrictions for the VAR. In the empirical exercise, a non-linear representation of GIRFs is obtained that allows the analysis of 3 different regimes the economy enters and observe any potential sign or size asymmetries in the responses for each regime. We document that due to the opposite movement of Mkp and Mc in all regimes, inflation is less volatile in the recessionary state than in the expansionary state. In addition, we find that larger shocks have a lower (as % of the magnitude of the shock) and less persistent effect on inflation than shocks of a lower magnitude.

Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?

This study examines whether geopolitical risk (GPR) exhibits an ability to forecast crude oil volatility from a time-varying transitional dynamics perspective. Unlike previous studies that assume an oversimplification of the fixed transition probabilities for crude oil volatility, we develop an asymmetric time-varying transition probability Markov regime switching (AS-TVTP-MS) GARCH model. In-sample estimated results show that GPR yields strong evidence of regime switching behavior on crude oil volatility and that the negative shocks of GPR result in greater effects on switching probabilities than positive shocks. Out-of-sample results indicate that the AS-TVTP-MS GARCH model containing the GPR index outperforms other models, suggesting that the consideration of GPR information and time-varying regime switching together results in superior predictive performance. Moreover, the predictability of oil volatility is further verified to be economically significant in the framework of portfolio allocation. In addition, our results are robust to various settings.

Volatility forecasting revisited using Markov‐switching with time‐varying probability transition

AbstractThis study proposes a novel model, Markov‐switching Heterogeneous Autoregressive (MS‐HAR) model with jump‐driven time‐varying transition probabilities (TVTP), to forecast the future volatility in Chinese stock market. The in‐sample results show that MS‐HAR models are more powerful than HAR‐RV‐type models; furthermore, the high‐volatility regime is short‐lived. Moreover, the out‐of‐sample results indicate that the MS‐HAR with TVTP model can achieve a superior forecasting performance and increase the economic value than the competing models including the simple HAR model and the MS‐HAR with fixed transition probabilities (FTP) model. The results are robust to several robustness checks including alternative forecast window, alternative evaluation method, alternative predictive model, sub‐sample analysis and alternative representative index.

Ground Vehicle Tracking Using Context-Based Sojourn Time Dependent Markov Model and Pseudo-Measurement

Tracking maneuvering vehicles in complex dynamic environment is a challenging problem for advanced driver assistance system and autonomous driving systems. Most conventional vehicle tracking algorithms can not model the vehicle dynamic exactly because of the uncertain moving behavior. However, due to the on-road capability, vehicles have to subject to various constraints imposed by traffic rules and roads. Taking advantage of those context information can refine and improve the performance of tracking as it provides additional prior information for vehicles' dynamic behavior. To achieve this goal, this paper presents a novel context-enhanced tracking approach that exploits the context information to reduce the uncertainty of dynamic estimation. A new context-based sojourn-time dependent semi-Markov (STDM) model, called sojourn-time dependent semi-Markov variable structure interacting multiple model (STDM-VSIMM), is proposed to describe the vehicle's longitudinal acceleration process. In order to cope with the context information into STDM model, a context-based Bayesian network is presented to replace the fixed model transition probabilities with sojourn-time dependent transition probabilities. Compared with traditional interacting multiple model tracking with fixed transition probability, this adaptation switching strategy makes the vehicle motion sequence closer to the natural behavior and improve the tracking performance. Furthermore, a novel pseudo-measurement is constructed to formulate the road-map constraint in tracking process for reducing uncertain on mobility constraints. Simulation results shows that the proposed STDM-VSIMM can achieve better performance after considering the context.

Variance Minimization Hedging Analysis Based on a Time-Varying Markovian DCC-GARCH Model

Considering time-varying transition probability (TVTP), this article combines Markov regime switching with a dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model to construct a new hedging model and study a state-dependent minimum variance hedging ratio. A two-stage maximum likelihood method is constructed to estimate the model parameters. A filtering algorithm is used in an estimation process. Empirical results on commodity futures hedging show that compared with other benchmark models, the proposed one has the best fitting effect. In addition, in terms of hedging effectiveness, the proposed model is superior to other models in most cases, which means that introducing TVTP into a DCC-GARCH model can effectively improve the performance of hedging portfolio. Note to Practitioners —This article deals with a state-dependent minimum variance hedging problem. It combines a time-varying Markov regime switching with dynamic conditional correlation generalized autoregressive conditional heteroscedasticity named DCC-GARCH to construct a new hedging model and estimates a state-dependent hedging ratio. Empirical results from commodity futures hedging show that introducing TVTP into the DCC-GARCH model can effectively reduce portfolio risk and provide better hedging performance than other traditional models, including Markov regime switching DCC-GARCH with a fixed transition probability, DCC-GARCH, ordinary least squares, naive hedging strategies, and unhedged spots. Thus, this article is of guiding significance for hedgers to fully learn the hedging rules of futures market and avoid the spots price risk.

Reliability modeling for a discrete time multi-state system with random and dependent transition probabilities

In a random environment, state transition probabilities of a multi-state system can change as the environment changes. Thus, a dynamic reliability model with random and dependent transition probabilities is developed for non-repairable discrete-time multi-state system in this article. The dependence among the random state transition probabilities of the system is modeled by a copula function. By probability argument and random process theory, we obtain explicit expressions of some reliability characteristics and joint survival function of random time spent by the system in all working states (partially and completely working states). A special case is considered when the state transition probabilities are dependent random variables with power distribution, and the dependence structure is modeled by Farlie–Gumbel–Morgenstern copula. Numerical examples are also presented to demonstrate the developed model and perform a comparison for the models with random and fixed transition probabilities.

Finite Time H∞ Boundedness of Discrete-time Markovian Jump Neural Networks with Time-varying Delays

This paper is concerned with the problem of finite-time H∞ boundedness of discrete-time Markovian jumping neural netwoks with time-varying delays. A new sufficient condition is presented which guarantees the stability of the closed-loop system and the same time maximizes the boundedness on the non-linearity. An extension of fixed transition probability Markovian model is combined to time-varying transition probabilities has offered. By constructing a novel Lyapunov-Krasovskii functional, the system under consideration is subject to interval timevarying delay and norm-bounded disturbances. Linear matrix inequality approach is used to solve the finite-time stability problem. Numerical example is given to illustrate the effectiveness of the proposed result.

Asymmetric Random Walk in a One-Dimensional Multizone Environment

We consider a random walk model in a one-dimensional environment, formed by several zones of finite width with the fixed transition probabilities. It is also assumed that the transitions to the left and right neighboring points have unequal probabilities. In continuous limit, we derive analytically the probability distribution function, which is mainly determined by a walker diffusion and drift and accounts perturbatively for interface effects between zones. It is used for computing the probability to find a walker in a given space-time point and the time dependence of the mean squared displacement of a walker, which reveals the transient anomalous diffusion. To justify our approach, the probability function is compared with the results of numerical simulations for a three-zone environment.

Revisiting the Relationship between Military Expenditure and Economic Growth in Pakistan

This study aims to examine relationship of military expenditure and economic growth in different phases of military regimes in the context of Pakistan. This study uses two-state Markov switching models with Constant Transition Probability (CTP) and Time Varying Transition Probabilities (TVTP) for the time period: 1973-2014. This investigation analyses two sorts of relations between military expenditures and economic development through fixed transition probability Markov exchanging models. To begin with, there is negative connection between GDP growth and military expenditures during a high variance state (i.e. having low economic growth). Second, there is positive relation between both variables, during low variance state (i.e. having higher economic growth) which is also supported by idea of Keynesian income multiplier. Another, empirical test of time varying transition probability model was used to capture the switch through indicator variable. Results of the study suggest that chances of switching are increased from low to high economic growth. The chances of switching increase from lower to higher economic growth period (or high variance period) if non-military expenditure increases. The study concludes that military expenditure and economic growth are state dependent. If conditions of economy are stable then increase of expenditure results in positive outcomes, otherwise, it affects negatively. Empirical findings suggest that military spending should be planned in accordance to the economic performance of the country.

An Alternative to Fixed Transition Probabilities for the Projection of Interprovincial Migration in Canada

Internal migration plays a critical role in subnational population projections. The multiregional model is often seen as a gold standard, for its capacity to project several interconnected regions simultaneously and coherently. However, undesirable effects may occur when assumptions of constant transition probabilities are used. This paper investigates these limits, explores a few solutions provided in the literature and describes the alternative methodology used by Statistics Canada in its most recent edition of population projections for the Canadian provinces and territories. Among other things, the new method is shown to improve the consistency between internal migration assumptions and results and to facilitate the projection of the uncertainty associated with this component.

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations.

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Risk–Return Relationship in BRIC Equity Markets: Evidence from Markov Regime Switching Model with Time-varying Transition Probabilities

A rich literature supports the existence of both positive and negative relationship between the risk and return in the developed equity markets. However, the present study attempts to capture the risk–return relationship in the most promising and opportunities-instilled emerging market club, the “BRIC” equity markets, by employing a Markov regime switching model with time-varying transition probabilities, further taking St. Louis Fed Financial Stress Index (the US financial market stress) as an economic variable. The weekly benchmark index values are used in the analysis, spanning from the year 2004 to 2013. The results report the existence of time-varying transition probabilities with respect to the Brazilian and Indian markets only and fixed transition probabilities for the other countries undertaken. The Markov results support the existence of two regimes, wherein regime-1 reports a positive risk–return relationship, and regime-2 reports a negative relationship between the risk and return. Ironically, the Chinese equity market is found to be the riskiest but a perfect hedge instrument amongst others, considering its risk–return interactions in both the regimes. Furthermore, a lower level of financial stress in the US financial market is associated with a higher probability of remaining in the “Bullish” regime-1 in the Indian market as well as Brazilian market. Moreover, there is a positive co-movement between the US financial stress and the expected time-varying duration of remaining in the “Bearish” regime. This shows that due to the growing interdependence among the worldwide economies, a financial stress in one economy does have an impact on the other markets and risk–return relationship in their equity markets. An understanding of the risk–return dynamics coupled with the impact of exogenous variables is an imperative task that a portfolio manager must undertake so as to justify and manage the investments made in the equity markets.

The Effects of Oil Price Shocks on Transitional Dynamics of Turkish Business Cycle

In this study we investigated usefulness of oil price shocks in predicting switches between the growth phases of output in Turkey using monthly data for the period 1986-2014 by extending Markov Switching framework to include time-varying transition probabilities. We investigate the issue of whether the addition of various real oil price shocks to a univariate Markov Switching model for output can characterize the dynamics of business cycles better than the fixed transition probability version of Markov Switching model. The main results are summarized as follows. We find that although information about the lags of output growth and the information contained in transition probabilities combine to help identify which state of the economy has occurred in the TVTP model, oil price shocks are not the leading indicator of Turkish business cycle.

Asymmetries and Markov-switching structural VAR

The development of nonlinear representations and of generalized IRFs favored the study of the variables behavior in response to an economically identified shock as regards (i) the state of the system when the shock occurs, (ii) the size of the shock and (iii) the sign of the shock. Generalized IRFs are widely used in threshold representations to illustrate and even test the presence of asymmetries (Potter, 1995; van Dijk et al., 2007). However, GIRF have known no comparable development in Markov-switching VAR. I show that whether IRF and GIRF recently developed in this framework impose sign and size symmetries or display poor properties. In this paper, I propose a new GIRF for general Markov-switching structural VAR with fixed transition probabilities. In a simulation framework inspired from Rubio-Ramirez et al. (2005), I relax the assumption of perfect knowledge of the regime and introduce the updating step proposed by Camacho and Perez-Quiros (2013). As a consequence, sign/size asymmetries can now be examined in MSIAH-VAR and the GIRF now incorporates the dependence on future shocks without extra complexity. Visited regimes can now differ endogenously between the shocked and baseline trajectories due to the initial and future shocks but the only exogenous difference in the simulated trajectories relies on the initial structural shock. I use this approach to implement a test for sign and size asymmetries on US aggregate gross job flows.

Finite-time boundedness of a class of discrete-time Markovian jump systems with piecewise-constant transition probabilities subject to average dwell time switching

This paper investigates the problem of finite-time boundedness of a class of discrete-time Markovian jump systems with piecewise-constant transition probabilities subject to average dwell time switching. Another set of useful regime-switching models has been given for both fixed transition probability Markov switching models and time-varying transition probabilities. Based on the knowledge of average dwell time and multiple Lyapunov function, a novel sufficient condition for finite-time boundedness of H∞ filtering is derived and the system trajectory stays within a prescribed bound. Finally, an example is provided to illustrate the usefulness and effectiveness of the proposed method.

The Effect of Energy Consumption Growth on Value-added Growth of Economic Sectors: Markov Models

In this paper an attempt is made to study the impact of energy consumption growth on forming regimes of low and high value-added growth of economic sectors of Iran economy by using seasonal data from 1970 to 2009. So, in some models, Markov switching model with the supposition of fixed transition probabilities by value-added growth of economic sectors variable were expanded. According to modeling results, increase in energy consumption growth, leads to increase in value-added growth of economic sectors in Iran economy. In addition in modeling value-added growth of agriculture group and value-added growth of manufacturing and mining group, the positive effect with motion of low value-added growth regime to high value-added growth regime is increased. Also in value-added growth of transportation storage and communications group, the positive effect for both regimes are great.