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ABSTRACTDecision support for managers and policy makers, such as required in planning and evaluation efforts, often requires ad hoc behavioral modeling to account for context‐specific phenomena and to handle data limitations. This paper introduces a systematic approach useful for meeting these requirements in which time‐varying parameter estimation plays an important role. A case study evaluating the impacts of public policy actions on residential natural gas conservation illustrates the approach.

This paper highlights the exchange rate pass-through dynamics in Tunisia using a time-varying parameters model. The results show that contrary to what has been generally observed in emerging and transitional economies, pass-through has been increasing steadily during the last decade. This acceleration is due to the implemented exchange rate policy, the absence of credible nominal anchor for monetary policy and the efforts made to reduce subsidies on administered products.

We explore whether modelling parameter time variation improves the point, interval and density forecasts of nine major exchange rates vis-a-vis the US dollar over the period 1976-2015. We find that modelling parameter time variation is needed for an accurate calibration of forecast confidence intervals, and is better suited at long horizons and in high-volatility periods. The biggest forecast improvements are obtained by modelling time variation in the volatilities of the innovations, rather than in the slope parameters. Moreover, we do not find evidence that parameter time variation helps to unravel exchange rate predictability by macroeconomic fundamentals. Finally, an economic evaluation of the different forecast models reveals that controlling for parameter time variation leads to higher portfolios returns, and to higher utility values for investors.

© 2016, Springer-Verlag Berlin Heidelberg. This paper investigates the effects of institutional changes within the UK housing market in recent decades using structural break tests and time-varying parameter models. This approach is motivated by models of institutional change drawn from the political science literature which focus on the existence of both fast-moving and slow-moving institutional changes and the interactions between them as drivers of the dynamics of asset prices. As a methodological contribution, we use several time-varying parameter models for the first time in investigations of institutional change. Our findings support the existence of both structural breaks and continuous variance in parameters. This contributes to our understanding of the housing market in two respects. Firstly, the dates of structural breaks appear to better match unexpected market shocks rather than remarkable political events, and this supports prior institutional theory. Secondly, assessment of the effect of slow-moving institutional changes shows that people's biased expectations rather than the economic fundamentals have increasingly played an important role in driving housing prices in the short run although fundamentals continue to drive house prices to converge to their long-run equilibrium.