Switching Dynamic Model Research Articles

Switching costs in transient technology use: Panel evidence from Kenyan maize production

AbstractThe idea of switching costs and how they affect agricultural technology adoption has received little attention in the literature. We employ a dynamic switching model to show that switching costs generally discourage the adoption of new technologies by farmers, but the extent of the effect depends on the level of switching costs, as well as farmer perceptions and foresight. We provide an empirical strategy to identify switching costs and estimate their effects on adoption under different farmers’ perceptions about adoption uncertainty. An application to hybrid seed adoption in Kenya confirms the existence of switching costs. The correlated‐random‐effects probit results suggest there is heterogeneity in dominant farmer types across agro‐ecological zones in Kenya. Area‐specific policies are suggested to promote technology adoption in different environments.

Exploring causal interactions between macroprudential policy and financial cycles in South Africa

This study investigated the causal interactions between macroprudential policy, measured by the Macroprudential Policy Index (MPI), and financial cycles represented by the Aggregate Financial Cycle (AFC) and the Credit and Asset Prices Financial Cycle (CAFC) in South Africa from 1970q1 to 2023q2. Additionally, the study explored the effects of macroprudential policy during different phases of financial cycles. Using the time-varying Granger-causality model, the study found that the MPI Granger-caused financial cycles during the 2003/07 credit boom in South Africa, while the AFC and CAFC Granger-caused macroprudential policy during the Covid-19-induced collapse of financial markets. The results suggest that macroprudential policy is employed more proactively during financial booms and more reactively during financial busts in South Africa. The Markov switching dynamic regression model used to assess the MPI's effects revealed that macroprudential policy's effectiveness is stronger during financial busts and weaker during financial booms in South Africa. This is because financial institutions in South Africa tend to resist stricter regulations during boom phases due to heightened optimism about future prospects. Conversely, they are more receptive to stimulatory interventions during bust phases. Based on these findings, it is recommended that the South African Reserve Bank and the Prudential Authority use macroprudential policy more assertively during financial booms to enhance its effectiveness. This could involve setting more stringent parameters upon activating macroprudential policy tools or complementing macroprudential policy with monetary policy during financial booms.

Approximating Botswana's financial cycle: Expanding the macroprudential toolkit

AbstractThis study estimates the financial cycle for Botswana using the Christiano–Fitzgerald band‐pass filter, unobserved components approach and a Markov switching dynamic factor model. Using real credit and equity prices, together with several macroeconomic variables from 2001Q1 to 2021Q4, we find that the domestic financial cycle generally captures movements along the business cycle, with peaks reflecting the cumulative build‐up of risks during the boom that ultimately burst, coinciding with periods of financial distress. These findings shed light on the implications of financial fluctuations on domestic financial stability, hence, inform effective calibration of macroprudential tools, while also providing traction for implementation of the recently approved macroprudential policy framework for Botswana.

Macroprudential and Monetary Policy Interactions and Coordination in South Africa: Evidence from Business and Financial Cycle Synchronisation

The article reports on the interactions and possibility of coordination between macroprudential and monetary policies in South Africa, based on business and financial cycles synchronisation. To this end, relying on financial and economic time series indicators spanning the period 2000M01–2018M12, a two-step Markov switching dynamic factor model was adopted for the measurement of composite indices, while both the dynamic conditional correlations and asymmetric generalised dynamic conditional correlations models were adopted for synchronisation analysis, together with the Metcalfe scale of coordination. The empirical evidence obtained is such that, under conditions of financial and real economic stress in South Africa, when there is crisis management rather than crisis prevention, macroprudential policy and monetary policy decisions are complementary. Therefore, there will be limited/no need for coordination between the two policy decisions. However, under normal times when there is crisis prevention rather than management, macroprudential policy and monetary policy decisions are noncomplementary; hence, the greatest degree of coordination is warranted, even though it might not be easy. Therefore, we conclude that it is possible to the coordinate the conduct of macroprudential, and monetary policies based on the synchronicity of business and financial cycles.

Compensation of friction and stick-slip uncertainties in trajectory tracking control of servo DC machines considering actuation constraints

Nowadays, electric motors are common in many precision instrument industries that require high-precision control. If a suitable model of a servo system is available, accurate results can be obtained using a model-based control algorithm. The DC motor is one of the most widely used systems in precision industries, for which a high-precision controller can be designed using its mathematical model with feedback on position and velocity. In such systems and other rotating systems, it is necessary to consider friction in the control system design. Friction enters the system in the form of resistant force or torque in both static and kinetic states, which causes uncertainty in the dynamic modeling of the system, the stick-slip phenomenon, and creates a deadzone in the actuation system. In this article, considering a switching dynamic model for different states of a system subject to friction which has a saturation limit on the control input and an actuation system dependent on the angular velocity feedback, first, the stable reaching laws in two modes of position control and velocity control for different dynamic model switch modes are defined. Then, the control input that forces the tracking error to follow the corresponding reaching law for each control mode is calculated. In the next step, according to the limitations of the actuation system, such as saturation and deadzone, using the filtering mechanism and optimal search for each control mode, the desired values are filtered and it is guaranteed that the control input will be chosen within the allowed range. Finally, using the optimal switching mode selection mechanism, the optimal control mode is selected and the voltage is calculated as the next step input.

Capacitance Variations and Gate Voltage Hysteresis Effects on the Turn-ON Switching Transients Modeling of High-Voltage SiC MOSFETs

This paper presents a discrete and real-time capable dynamic behavioural model of the turn-on switching transition of high-voltage and high-current Silicon Carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) half-bridge power modules. The dynamic switching model utilizes the Shichman and Hodges equations using voltage-dependent non-linear device capacitances and module electrical parameters to obtain an accurate dynamic model of the device switching transients. The key device states gate-source voltage, drain current and drain-source voltage are modelled. The paper investigates the impact of correct device capacitance modelling with low off-state gate-source voltage values, impacting the device capacitances and causes gate-voltage hysteresis effects. It has been shown that the presented discrete-time dynamic switching model accurately describes the turn-on transient and the results highlight the importance of correct capacitance and threshold voltage characterization data. The modelling results are compared to experimental measurements conducted in a 3.3 kV/750 A SiC MOSFET power module. The model exhibits an average accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 4\%$</tex-math></inline-formula> for turn-on energy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 1.3\%$</tex-math></inline-formula> for the turn-on time compared to measurements. These models are valuable for rapid and cost effective design and validation of advanced gate-driver circuits and for determining key design and operating parameters such as deadtime, switching frequency and switching losses.

Detecting business cycles for Hungarian leading and coincident indicators with a Markov switching dynamic model to improve sustainability in economic growth

This paper applies the hidden Markov switching dynamic regression (MSDR) model to estimate transition probabilities of the Hungarian GDP between recessionary and expansionary periods. The transition probabilities are then compared to the OECD Hungarian binary business cycle indicator to assess the predictive power of the model. The paper proposes a linear model with a mean and a homoscedastic component. The level of symmetricity between the GDP and business cycles is explained by the panel data variables (Unemployment rate, IPI index, Inflation, BUX year-on-year change, and 10-3 Year sovereign bond yield spreads). It is assumed in this paper that by extending the model to encompass an exogenous variable listed in the panel data, essentially making the model bivariate, the maximum likelihood function would capture the business cycle more accurately. The results show that by plugging the unemployment rate as the exogenous variable in the regression, our model’s accuracy is 70%.

Real-Time FPGA Simulation of High-Voltage Silicon Carbide MOSFETs

This article presents a real-time field-programmable gate array (FPGA)-based dynamic model of high-voltage and high-current silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> ) half-bridge power modules. The dynamic switching model utilizes the Shichman and Hodges equations using voltage-dependent nonlinear device capacitances and module electrical parameters to obtain an accurate dynamic model of the device switching transients. The key device states gate-source voltage, drain current, and drain-source voltage are modeled and discretized using forward Euler discrete integration method. Analysis of synthesizing the discrete-time model into real-time FPGA-based system with real-time data output from the on-board digital-to-analog converter is presented in detail. The model is utilized in modeling a 320-kW, medium-voltage dc/dc dual-active bridge converter and verified using dynamic experimental results from a 3.3-kV SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> half-bridge power module. It has been shown that the presented discrete-time dynamic switching model accurately describes the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> switching transients of the SiC power module at various voltage and current levels. Such models are useful for rapid and cost effective design and prototyping of SiC-based power electronic systems by defining key design and operating parameters, such as deadtime, switching frequency, and switching losses.

Early warning systems in Indonesian Islamic banks: A comparison of Islamic commercial and rural banks

The study examines the stability of Indonesian Islamic Commercial Bank (ICB) and Islamic Rural Bank (IRB) by employing a Markov Switching Dynamic model of two regimes, stable (tranquil) and unstable (crisis). This study utilizes monthly data between December 2007 and April 2022. Findings show that both ICB and IRB have greater probabilities of remaining in the tranquil regime compared to the period of crisis. Even though ICB Z-scores have a wider range of volatility than IRB Z-scores, the ICB has been shown to recover from crises faster than the IRB. The differences in characteristics between ICB and IRB, as well as swings in the Z-score, do not provide resilience benefits. Thus, both ICB and IRB are equally vulnerable to the crisis. This study’s findings could be used to generate policy for the relevant stakeholders of Islamic banking industries.

Modeling Perception in Autonomous Vehicles via 3D Convolutional Representations on LiDAR

This paper proposes an algorithm to model and process streams of LiDAR data under an autonomous vehicle framework. LiDAR is assumed to be an exteroceptive sensor that allows the vehicle to have dynamic 3D scene perception of its surroundings. We employ an encoder-decoder architecture based on 3D-Convolutional layers called 3D Convolution Encoder-Decoder (3D-CED), together with a transfer learning strategy to extract a set of features from point clouds, which are relevant in the context of autonomous driving. The resulting features allow to make inferences of the future point cloud data and detect multiple abstraction level anomalies in controlled scenarios by utilizing a probabilistic switching dynamic model called High Dimensional Markov Jump Particle Filter (HD-MJPF). Moreover, a comparison is provided between piecewise linear, piecewise nonlinear, and nonlinear predictive models for anomaly detection at multiple abstraction levels. Our approach is evaluated with data collected from the LiDAR sensors of the autonomous vehicle while performing certain tasks in a controlled environment.

Understanding transient technology use among smallholder farmers in Africa: A dynamic programming approach

AbstractThis article studies the causes and effects of transient use of agricultural technology. Most existing studies have assumed that adoption of modern technology is irreversible, and widely ignored the switching between technologies that is often observed in practice. To more fully understand the pattern of agricultural technology adoption, this article takes switching behavior into account and develops a dynamic switching model to analyze transient technology use. The conceptual model is then calibrated and simulated using dynamic programming, and numerical results show that relative profitability, switching costs, and farmer's foresight are important influences on the pattern of transient technology use. Switching costs play a key role in preventing farmers from adopting new technologies, even when they appear otherwise profitable, and can cause farmers to switch back to traditional technologies, even when the move appears otherwise irrational. Also, smallholder farmers, differentiated based on their perception of switching costs and future economic conditions, exhibit heterogeneous decision rules and adoption patterns. Therefore, policies targeted at increasing technology adoption will be more effective if they differentiate specific types of farmers.

The Dynamics of Contagion and Behavior of the Euro Area Sovereign Bond Markets

We investigate the contagion dynamics in the main Economic and Monetary Union sovereign bond markets during the subprime and euro crises. To this end, we adopt the APARCH-ADCC model and the Markov Switching Dynamic Regression model. The impact of both crises on each country in our sample is investigated using two crisis indicators: synchronization and intensity. The empirical results show that contagious episodes and their intensities vary over time and across market pairs for both crises, thus illustrating the complexity of this phenomenon. They also reveal that the market couples composed of the Economic and Monetary Union peripheral countries were immune to the contagion of the subprime crisis and that its third phase was the most violent in terms of the intensity of the transmission of shocks. Moreover, the results show that all the countries in our sample were affected by contagion from the euro crisis, at least during one of its sub-phases. Furthermore, before the first rescue plan for the Hellenic economy, our methodology detected a contagion effect stemming from Italy, Spain, and Portugal, while intuitively one could have imagined this phenomenon coming solely from the Greek sovereign market. Finally, the analysis of the synchronization and intensity variables provides evidence of a « multi-speed Economic and Monetary Union », refuting the notion of uniform integration of the euro area sovereign bond markets. We believe that it is necessary to complete the institutional architecture of the Economic and Monetary Union to make it more resilient to shocks.

Towards a cleaner energy system: Estimating the odds of transitioning to an energy-efficient state

At the heart of the global transition to cleaner energy systems is also an inherent mandate of ensuring a transition to an energy-efficient economy. This study applies a two-regime Markov switching dynamic model to examine the possibility of transitioning to an energy-efficient state, using Ghana as the subject of analysis. Since changes in energy intensity do not necessarily suggest improvements in energy efficiency, the study controls for the effect of income, trade, and energy price. The results show that Ghana has a higher chance of transitioning to an energy-efficient state but a relatively lower possibility for such efficiency to persist. What's more, it would take 1.2 y for the said efficiency to persist. The study recommends a broad policy overhaul targeting institutional restructuring, energy financing, and regulatory efficiency as means to facilitate the prospects of transitioning and improving energy efficiency persistence.

A switching dynamic model based on phased COVID-19 data in Chongqing and its evaluation.

ObjectivesAlthough COVID-19 has been controlled in China, the risk of invasion of imported cases remains. We aimed to characterize the impact of the number of imported cases and the implementation of first-level emergency response (FLER) policy.MethodsA SCQIHR switching model was constructed and verified by the complete phased data of COVID-19 in Chongqing in 2020. Then it was used to investigate the impact of the number of imported cases and the timing of FLER. Lastly, it was evaluated by three actual scenarios in Chongqing in 2021.ResultsThe proposed model can fit the multidimensional time series well. After the implementation of FLER, the mean effective reproduction number, contact rate and misdetection rate were decreased significantly, but the quarantine rate for close contacts and isolation rate for non-hospitalized infectious cases were increased significantly. The peaks of quarantined close contacts and hospitalized infectious cases increased linearly with the increase of the number of imported cases and the lag of FLER time, which was verified by three actual scenarios in Chongqing in 2021.ConclusionsThese findings can provide guidance for local public health policy-making and allocation of medical resources, reduce the impact of COVID-19 on the local population.

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model.

Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state's mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states' evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand's daily happiness data for May 2019 -November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.

Locomotion Control of Snake-Like Robot with Rotational Elastic Actuators Utilizing Observer

The purpose of this paper is designing a head control system capable of adapting to passive side-slipping. The environments in which snake-like robots are expected to be utilized generally have ground surface conditions with nonuniform frictional coefficients. In such conditions, the passive wheels of the snake-like robot have a chance of side-slipping. To locomote the snake-like robot dexterously, a control system which adapts to such side-slipping is desired. There are two key points to realizing such a system: First, a dynamic model capable of representing the passive side-slipping must be formulated. A solution for the first key point is to develop a switching dynamic model for the snake-like robot, which switches depending on the occurrence of the side-slipping, by utilizing a projection method. The second key point is to adapt the control system’s behavior to side-slipping. An idea for such a solution is to include the side-slipping velocity in the weighting matrices. An algorithm to estimate the occurrence of side-slipping and the particular side-slipping link is constructed, to formulate the dynamic model depending on the actual side-slipping situation. The effectiveness of the designed Luenberger observer and the head control system for side-slipping adaptation is verified through numerical simulation.

Exploiting the switching dynamics of HfO2-based ReRAM devices for reliable analog memristive behavior

The utilization of bipolar-type memristive devices for the realization of synaptic connectivity in neural networks strongly depends on the ability of the devices for analog conductance modulation under application of electrical stimuli in the form of identical voltage pulses. Typically, filamentary valence change mechanism (VCM)-type devices show an abrupt SET and a gradual RESET switching behavior. Thus, it is challenging to achieve an analog conductance modulation during SET and RESET. Here, we show that analog as well as binary conductance modulation can be achieved in a Pt/HfO2/TiOx/Ti VCM cell by varying the operation conditions. By analyzing the switching dynamics over many orders of magnitude and comparing to a fully dynamic switching model, the origin of the two different switching modes is revealed. SET and RESET transition show a two-step switching process: a fast conductance change succeeds a slow conductance change. While the time for the fast conductance change, the transition time, turns out to be state-independent for a specific voltage, the time for the slow conductance change, the delay time, is highly state-dependent. Analog switching can be achieved if the pulse time is a fraction of the transition time. If the pulse time is larger than the transition time, the switching becomes probabilistic and binary. Considering the effect of the device state on the delay time in addition, a procedure is proposed to find the ideal operation conditions for analog switching.

Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns

A non-Gaussian multivariate regime switching dynamic correlation model for financial asset returns is proposed. It incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns. All model parameters are estimated consistently using a new two-stage expectation–maximization algorithm that also allows for incorporation of shrinkage estimation via quasi-Bayesian priors. It is shown that use of Markov switching correlation dynamics not only leads to highly accurate risk forecasts, but also potentially reduces the regulatory capital requirements during periods of distress. In terms of portfolio performance, the new regime switching model delivers consistently higher Sharpe ratios and smaller losses than the equally weighted portfolio and all competing models. Finally, the regime forecasts are employed in a new dynamic risk control strategy that avoids most losses during the financial crisis and vastly improves risk-adjusted returns.

A Flexible Regime Switching Model for Asset Returns

A non-Gaussian multivariate regime switching dynamic correlation model for fi nancial asset returns is proposed. It incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns. All model parameters are estimated consistently using a new two-stage expectation-maximization algorithm that also allows for incorporation of shrinkage estimation via quasi-Bayesian priors. It is shown that use of Markov switching correlation dynamics not only leads to highly accurate risk forecasts, but also potentially reduces the regulatory capital requirements during periods of distress. In terms of portfolio performance, the new regime switching model delivers consistently higher Sharpe ratios and smaller losses than the equally weighted portfolio and all competing models. Finally, the regime forecasts are employed in a dynamic risk control strategy that avoids most losses during the fi nancial crisis and vastly improves risk-adjusted returns.

Large Shocks and the Business Cycle: The Effect of Outlier Adjustments

This study examines the impact of outlier-adjusted data on business cycle inferences using coincident indicators of the composite index (CI) in Japan. To estimate the CI and business cycles, this study proposes a Markov switching dynamic factor model incorporating Student’s t-distribution in both the idiosyncratic noise and the factor equation. Furthermore, the model includes a stochastic volatility process to identify whether a large shock is associated with a business cycle. From the empirical analysis, both the factor and the idiosyncratic component have fat-tail error distributions, and the estimated CI and recession probabilities are close to those published by the Economic and Social Research Institute. Compared with the estimated CI using the adjusted data set, the outlier adjustment reduces the depth of the recession. Moreover, the results of the shock decomposition show that the financial crisis in mid-2008 was caused by increase of clustering shocks and large unexpected shocks. In contrast, the Great East Japan Earthquake in 2011 was derived from idiosyncratic noise and did not cause a recession. When analyzing whether to use a sample that includes outliers associated with the business cycle, it is not desirable to use the outlier-adjusted data set.