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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.

In consultant–client relationships, relationship longevity can create significant cost advantages and operational efficiencies for both client and consultant. At the same time, each party may also be motivated to look for new perspectives and opportunities by switching to new relationships. However, the benefits of replacing one consulting relationship with another are mitigated by switching costs: the costs associated with the act of changing the relationship itself.

This chapter explores the concept of switching costs by examining various types of costs, the ways these costs have been conceptualized in the literature, and how these costs may impact the nature and continuity of consultant–client relationships. The chapter will end with a series of hypotheses and suggestions for a research agenda to further develop our understanding of this important phenomenon.

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.

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.

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

Purpose – The purpose of this chapter is to better understand the connection between religious affiliation and educational attainment and how this connection has changed over time.

Methodology/Approach – We utilize the cumulative 1972–2008 General Social Surveys to examine the relationships between childhood religious affiliation, college degree attainment, and religious switching across three birth cohorts.

Findings – We find in early cohorts that traditions such as Conservative Protestantism and Catholicism are negatively associated with college degree attainment. However, switching out of those traditions is positively associated with obtaining a college degree. In later cohorts, these effects disappear.

Social implications – The finding that the relationships between religious affiliation and educational attainment are dramatically changing over time means that scholars, educators, and religious groups might need to revise their current thinking concerning the topic of religion and education.

Originality/Value of chapter – This research helps us better understand the complexities involved when thinking about the role of religion in education and vice versa. By explicitly considering the different causal and temporal factors involved, this analysis provides a more nuanced understanding of the connection between religious affiliation and educational attainment.

The valuation of innovation investments still poses several unresolved questions. Although some authors have analyzed these problems within a framework based on real options theory, their work has not explicitly tested the value of specific real options. The model of firm market value presented in this paper formally includes a technology switching option, which allows a firm to exchange an existing technology with a new technology. We test the model on a panel of publicly traded British firms operating in different manufacturing industries. The results provide support to the claim that the stock market recognizes and evaluates a technology switching option.

This study analyses the determinants of the value of a portfolio of real options and explores implications for strategic management. It focuses the analysis on four elements: the number of real options in the portfolio, constraints on the number of options that can be exercised, the volatility of underlying assets, and the correlation between underlying assets. These elements are articulated around a trade-off between growth options and switching options and are applied to different strategic situations of technological, market, and macroeconomic uncertainty.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.