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This paper aims to propose a new unified and non-ideal switch model for analysis of switching circuits.

The model has a single unified structure that includes all possible states (on, off) of the switches. The analysis with the proposed switch model requires only one topology and uses the single system equation regardless of states of switches. Moreover, to improve accuracy, the model contains the on-state resistance and capacitive effect of switches. The system equations and the states of switches are updated by control variables, used in the model.

There are no restrictions on circuit topology and switch connections. Switches can be internally and externally controlled. The non-ideal nature of the model allows the switch to be modeled more realistically and eliminates the drawbacks of the ideal switch concept. After modeling with the proposed switch model, a linear circuit is obtained. Two examples related to switching circuits are included into the study. The results confirm the accuracy of the model.

This paper contributes a different switch model for analysis of switching converters to the literature. The main advantage of the model is that it has a unified and non-ideal property. With the proposed switch model, the transient events, like voltage spikes and high-frequency noises, caused by inductor and capacitor elements at switching instants can be observed properly.

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact method for implementing DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an inflation forecasting application. We find strong evidence of model switching. We also compare different ways of implementing DMA/DMS and find forgetting factor approaches and approaches based on the switching Gaussian state space model to lead to similar results.

The purpose of this paper is to develop and empirically test the antecedent, mediating and moderating role of switching costs on the relationship between satisfaction and loyalty.

Competing models are proposed based on previous studies investigating the influence of switching costs on satisfaction and loyalty. A survey was conducted with 7,461 customers of a large Brazilian bank. The four competing models were tested using structural equation modeling technique.

The analysis revealed that: switching cost is a significant antecedent of both attitudinal and behavioral loyalty; the mediating effect of switching cost is stronger in the relationship between satisfaction and attitudinal loyalty; and the moderating effect of switching cost is stronger in the relationship between satisfaction and behavioral loyalty.

This study emphasizes the relevance of the switching cost construct in the banking industry. Customers with different switching costs levels will manifest distinct relationship between satisfaction and behavioral loyalty. Thus, investment on marketing strategies and campaigns should be oriented to better convert switching perceptions into effective loyalty considering its mediating or moderating effects.

Even though there are several different approaches (i.e. direct, mediator and moderator) concerning the effects of switching costs on the satisfaction‐loyalty relationship, there is a lack of integration between these approaches. The paper tests and compares the different roles of switching costs. Another contribution is the inclusion of both attitudinal and behavioral aspects of loyalty, given that the current literature is incipient concerning the role of switching cost when considering the distinct loyalty components.

This chapter estimates a regime switching Taylor Rule for the European Central Bank (ECB) in order to investigate some potential nonlinearities in the forward-looking policy reaction function within a real-time framework. In order to compare observed and predicted policy behavior, the chapter estimates Actual and Perceived regime switching Taylor Rules for the ECB. The former is based on the refi rate set by the Governing Council while the latter relies on the professional point forecasts of the refi rate performed by a large investment bank before the upcoming policy rate decision. The empirical evidence shows that the Central Bank’s main policy rate has switched between two regimes: in the first one the Taylor Principle is satisfied and the ECB stabilizes the economic outlook, while in the second regime the Central Bank cuts rates more aggressively and puts a higher emphasis on stabilizing real output growth expectations. Second, the results point out that the professional forecasters have broadly well predicted the actual policy regimes. The estimation results are also robust to using consensus forecasts of inflation and real output growth. The empirical evidence from the augmented Taylor Rules shows that the Central Bank has most likely not responded to the growth rates of M3 and the nominal effective exchange rate and the estimated regimes are robust to including these additional variables in the regressions. Finally, after the bankruptcy of Lehman Brothers the policy rate has switched to a crisis regime as the ECB has focused on preventing a further decline in economic activity and on securing the stability of the financial system.

This paper aims to propose a novel model to forecast regime switches in a time series to assist decision making.

The authors apply the clustering technique to group the data into five states. Then, a model is proposed to formulate the relationships from in‐sample observations, including regime switch relationships. Afterwards, the model uses the relationships to forecast the regime switches in out‐sample observations.

The study uses daily Taiwan Stock Exchange Capitalization Weighted Stock Index as the forecasting target. Regime switches in in‐sample observations are identified. And a regime switch is successfully forecasted by the proposed model.

The proposed model identifies a regime switch which matches the real event. It implies that the proposed model can be applied to other time series, such as Dow Jones or NASDAQ.

Previous studies contribute to the forecasting of regime switches. The forecasting results are validated with the real event. One of the forecasted regime switches matches the event of Lehman Brothers' declaring of bankruptcy.

It is rare for the forecasts of one economic forecasting model to always be more accurate than the forecasts from an alternative model. This suggests the possibility of implementing a switching strategy that chooses, at each point in time, the forecasting model that is expected to be most accurate conditional on a set of instruments that are used to track the relative accuracy of the underlying forecasts. The authors analyze the factors determining the expected gains from such a switching rule over a strategy of always using one of the underlying forecasts. The authors derive bounds on the expected gains from switching for both the nested and non-nested cases and also analyze the case with a highly persistent (near-unit root) predictor variable.

The purpose of this study is to determine the factors that underpin consumer switching intention in the smartphone industry. Most of the literature on brand switching uses conventional models that lack the ability to explain this behavior for high-technology products. Such products have unique characteristics that make the switching process more challenging from the consumer perspective.

The proposed model is built based on the related theories that consider the distinctive aspects of high-technology products. Furthermore, two variables “relative advantage of product features” and “company innovativeness” are introduced for the first time to evaluate consumer attitude to switch a high-technology product. The smartphone industry was selected to test the proposed model, where an online survey was sent to Apple and Samsung users.

The results confirm the expectation that perceived product usefulness, perceived ease of use and relative advantage of product features are the major factors driving the intention of users to switch, whereas subjective norms have limited impact. The financial cost of switching is the main barrier to consumers’ decision to switch to a new technology. The pleasure consumers feel toward their brand and the other brand is positively associated with their attitude toward switching.

This research contributes to the literature on brand switching by introducing a comprehensive model that explains consumer switching behavior of high-technology products. Research findings would allow managers to draft better marketing strategies to improve consumer brand awareness.

The majority of literature on brand switching uses simple models to explain consumer behavior. This study is the first attempt to build a comprehensive model that considers the characteristics of high-technology products and how they shape consumer behavior during the decision-making process.

The purpose of this paper is to examine the effects of parameter uncertainty in the returns process with regime shifts on optimal portfolio choice over the long run for a static buy-and-hold investor who is investing in industry portfolios.

This paper uses a Markov switching model to model returns on industry portfolios and propose a Gibbs sampling approach to take into account parameter uncertainty. This paper compares the results with a linear benchmark model and estimates without taking into account parameter uncertainty. This paper also checks the predictive power of additional predictive variables.

Incorporating parameter uncertainty and taking into account the possible regime shifts in the returns process, this paper finds that such investors can allocate less in the long run to stocks. This holds true for both the NASDAQ portfolio and the individual high tech and manufacturing industry portfolios. Along with regime switching returns, this paper examines dividend yields and Treasury bill rates as potential predictor variables, and conclude that their predictive effect is minimal: the allocation to stocks in the long run is still generally smaller.

Studying the effect of regime switching behavior in returns on the optimal portfolio choice with parameter uncertainty is our main contribution.

The purpose of this paper is to derive the small-signal/canonical model derivation of the high-side active clamp forward converter (ACFC) with diode rectification for ideal and with resistive parasitics. It also covers the analysis of ACFC small-signal model with resistive parasitics using computer-aided modeling software Personal Computer Simulation Program with Integrated Circuit Emphasis (PSPICE) 16.6. The effects of variation of system parameters on the ACFC’s state transfer functions and operations have been highlighted in this paper.

The large-signal model and small-signal model of the ACFC with diode rectification has been derived using AC small-signal modeling approach.

The operating point of the converter changes with the consideration of resistive parasitics compared with the ideal case. The response obtained from the hardware matches with the time domain response of the averaged model and switch model developed in PSPICE.

This paper limits the study of ACFC small-signal behavior by using computer-aided design software PSPICE. The dead time of the converter is not considered because it is negligible when compared with the on and off time. The leakage inductance which plays a role in zero voltage switching of the ACFC switches is neglected in the analysis as it is very small compared to the magnetizing inductance. The switching losses are not considered in the modeling.

The mathematical computation of deriving the system transfer functions from canonical model is complex and time consuming.

The modeling with resistive parasitics improves the effectiveness of the equivalent model. Also, the analysis with computer-aided modeling software PSPICE gives reliable results in less time.

The purpose of this paper is to develop a simulation tool which permits reducing the cost of long time‐range simulation of complex converters and running at high frequency.

A different method is used to represent a simplified converter but the adopted technique uses the average representation of the cell converter.

The paper shows that the use of averaged representation of the pulse width modulation switch in multilevel converters is staying applied. The main advantage of the proposed averaged model is its simplified representation when only electrical behaviour is considered.

The analytical algorithm of the averaged model can be introduced in different simulator as it has a description language, enabling study of the Compatibilité Electromagnétique and electrothermal phenomena.

This paper presents an averaged model of the multilevel converter which can be implemented in any simulator as it has a description language.