Employing a bivariate regime switching model, this paper attempts to examine the regime-dependent effects of inflation uncertainty and output growth uncertainty on inflation and output growth. Using monthly data of the United Kingdom and the United States, we provide evidence that both nominal and real uncertainty exert regime-dependent impacts on inflation. Furthermore, in case of both the countries, inflation uncertainty has adverse impact on output growth mainly during the period of economic contraction. Also, for these two countries, it can be argued that higher real uncertainty significantly reduces output growth only in their respective low output growth regimes.

This paper studies the impact of inflation on inflation uncertainty in a modelling framework where both the conditional mean and conditional variance of inflation are regime specific, and the GARCH model for inflation uncertainty is extended by including a lagged inflation term in each regime. Applying this model to the G7 countries with monthly data from 1970 till 2013, it is found that the impact of inflation on inflation uncertainty differs over the regimes in most of the G7 countries. The findings also provide strong empirical support to the well-known Friedman-Ball hypothesis of positive impact of inflation on inflation uncertainty, but only for the high-inflation regime.

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The boom-bust cycle in U.S. house prices has been a fundamental determinant of the recent financial crisis leading up to the Great Recession. The risky financial innovations in the housing market prior to the recent crisis fueled the speculative housing boom. In this backdrop, the main objectives of this empirical study are to i) detect the possibility of multiple structural breaks in the US house price data during 1995-2010, exhibiting very sharp upturns and downturns; ii) endogenously determine the break points and iii) conduct house price forecasting exercises to see how models with structural breaks fare with competing time series modelslinear and nonlinear. Using a very general methodology (Bai-Perron, 1998, 2003, we found four break points in the trend in the S&P/Case-Shiller 10 city aggregate house-price index series. Next, we compared the forecasting performance of the model with structural breaks to four competing modelsnamely, Random Acceleration (RA), Autoregressive Moving Average (ARMA), Self-Exciting Threshold Autoregressive (SETAR), and Smooth Transition Autoregressive (STAR). Our findings suggest that house price series not only has undergone structural changes but also regime shifts. Hence, forecasting models that assume constant coefficients such as ARMA may not accurately capture house price dynamics.

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