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Abstract. This paper investigates the causal relationship between asset prices and per capita output across 50 US states and the District of Columbia over 1975 to 2012. A bootstrap panel Granger causality approach is applied on a trivariate VAR comprising of real house prices, real stock prices and real per capita personal income (proxying output), which allows us to account not only for heterogeneity and cross-sectional dependence, but also for interdependency between the two asset markets. Empirical results reveal the existence of a unidirectional causality running from both asset prices to output. This confirms the leading indicator property of asset prices for the real economy, while also substantiating the wealth and/or collateral transmission mechanism. Moreover, the absence of reverse causation from the personal income per capita to both housing and stock prices tend to suggest that noneconomic fundamentals may have played an important role in the formation of bubbles in these markets.

In this study, we propose a new unit root test procedure that allows for both gradual structural break and asymmetric nonlinear adjustment towards the equilibrium level. Small-sample properties of the new test are examined through Monte-Carlo simulations. The simulation results suggest that the new test has satisfactory size and power properties. We then apply this new test along with other unit root tests to examine stationarity properties of real exchange rate series of the sample countries. Our test rejects the null of unit root in more cases when compared to alternative tests. Overall, we find that the PPP proposition holds in majority of the European countries examined in this paper.

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We compare the performance of unit root tests which include flexible Fourier trends in their testing processes. The algorithms considered are those of Broyden, Fletcher, Goldfarb and Shanno (BFGS), Berndt, Hall, Hall and Hausman (BHHH), Simplex, Genetic and grid search (GS). The simulation results indicate that derivative-free methods, such as Genetic and Simplex, have advantages over hill-climbing methods, such as BFGS and BHHH in providing accurate fractional frequencies for fractional frequency flexible Fourier form (FFFFF) unit root test. When the parameters are estimated under the alternative hypothesis of the FFFFF type of unit root test, the grid search and derivativefree methods provide unbiased and efficient estimations. We also provide the asymptotic distribution of the FFFFF unit root test. We extend the FFFFF unit root test to a panel version in order to increase the power of the test. Finally, the empirical analyses of healthcare convergence show that derivative-free methods, hill climbing and extensive grid searches can be used interchangeably. However, for big data and accurate estimation of the frequency parameters, the Simplex methodology using the bootstrap process is preferred.

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