After a prolonged period characterized by rapid real appreciation in house prices, there is now broad recognition of the severe correction in housing markets that followed as one of the causes of the 2008-09 global recession. We investigate the time series characteristics of three relevant price indicators of the housing market-real house prices, price-to-income, and price-to-rent ratios-for the U.S. and 21 other countries during the period 1975Q1-2013Q2 (see Mack and Martínez-García (2011)) for evidence of explosive behavior as a plausible explanation for the boom and bust. The empirical detection of explosive behavior in house prices provides a precise timeline as well as empirical content to the narrative connecting the evolution of housing markets to the global recession; our rich cross-country dataset o¤ers a novel international perspective. For testing and detection, we adopt a pair of novel techniques based on a right-tail variation of the standard Augmented Dickey-Fuller (ADF ) test-the supremum ADF (SADF ) ) and the generalized SADF (GSADF ) (Phillips et al. (2012) and Phillips et al. (2013))-where the alternative hypothesis is of a mildly explosive process (even periodically collapsing with the GSADF test) behavior within sample. Statistically signi…cant periods of exuberance are found in most countries, with our empirical estimates suggesting an unprecendented synchronization across countries preceeding the global recession. The boom in housing begins during the late 90s in the U.S. spreading to most countries by the early 2000s, until it bursts for most during 2007 08 as the impact on economic activity was being felt. In this regard, our …ndings corroborate the narrative of the 2008-09 global recession. In this paper, we also discuss more generally the use of these procedures to monitor international housing markets and as a warning signal.JEL Classi…cation: C22, G12, R30, R31

The probabilistic structure of periodically collapsing bubbles creates a gap between future spot and forward (futures) asset prices in small samples. By exploiting this fact, we use a recently developed recursive unit root test and rolling Fama regressions for detecting bubbles. Both methods do not rely on a particular model of asset price determination, are robust to explosive fundamentals, and allow date stamping. An application to U.S. dollar exchange rates provides evidence of bubbles during the interwar German hyperinflation, but not during the recent floating-rate period. A further application to S&P 500 supports the existence of bubbles in the U.S. equity market. * Manuscript . 2 In line with most of the empirical literature, we focus on rational extrinsic bubbles. Froot and Obstfeld (1992) and Driffill and Sola (1998) examine intrinsic bubbles, i.e., bubbles that depend on market fundamentals. There are also studies that relax the rational expectations assumption and incorporate behavioral aspects into the analysis, such as overconfidence and differences in priors and beliefs (Abreu and Brunnermeier

The wide fluctuations of oil prices from 2003 to 2008 have attracted the interest of academics and policymakers. A popular view is that these fluctuations were caused by speculative bubbles due to the increased financialization of oil futures markets. This hypothesis, however, is difficult to examine since the fundamental price of oil is unobservable and, therefore, econometric evidence in favor of bubbles may actually be due to misspecified market fundamentals. In this paper, we extend two recently proposed methodologies for bubble detection that alleviate this problem by using market expectations of future prices. Both methodologies provide no evidence of speculative bubbles.

The specification of Smooth Transition Regression models consists of a sequence of tests, which are typically based on the assumption of i.i.d. errors. In this paper we examine the impact of conditional heteroskedasticity and investigate the performance of several heteroskedasticity robust versions. Simulation evidence indicates that conventional tests can frequently result in finding spurious nonlinearity. Conversely, when the true process is nonlinear in mean the tests appear to have low size adjusted power and can lead to the selection of misspecified models. The above deficiencies also hold for tests based on Heteroskedasticity Consistent Covariance Matrix Estimators but not for the Fixed Design Wild Bootstrap. We highlight the importance of robust inference through empirical applications.

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