Testing for Explosive Behaviour in Relative Inflation Measures: Implications for Monetary Policy

In this paper we test for large deviations in headline measures of the price level relative to core measures using the recently proposed test of Phillips et al. (2011a). We find evidence of explosive behaviour in the headline price index of personal consumption expenditures (PCE) relative to the core PCE (less food and energy prices) on three occasions from 1982-2010. Two of these episodes correspond to energy supply shocks (OPEC price collapse of 1986 and Hurricane Katrina). The third one is during March 2008 through September 2008 which seems to be driven by both food and energy prices as these indices exhibit explosive behaviour. We also find evidence suggesting that inflation expectations behave differently under normal and explosive periods. In particular, unemployment and interest rates also help predict inflation expectations during explosive episodes relative to normal times. Furthermore, explosive episodes in the relative measure between headline and core inflation is found to be more important than the relative volatile periods implied by a Markov-switching model when studying inflation expectations. The findings of this paper suggest that explosive behaviour of headline versus core PCE should be taken into account when conducting monetary policy as it is a key determinant in consumers’ inflation expectations.Explosive behaviour, core inflation, relative measure, inflation expectations

Real Time Monitoring of Asset Markets: Bubbles and Crises

While each financial crisis has its own characteristics, there is now widespread recognition that crises arising from sources such as financial speculation and excessive credit creation do inflict harm on the real economy. Detecting speculative market conditions and ballooning credit risk in real time is therefore of prime importance in the complex exercises of market surveillance, risk management, and policy action. This chapter provides an R implementation of the popular real-time monitoring strategy proposed by Phillips, Shi and Yu in the International Economic Review (2015), along with a new bootstrap procedure designed to mitigate the potential impact of heteroskedasticity and to effect family-wise size control in recursive testing algorithms. This methodology has been shown effective for bubble and crisis detection and is now widely used by academic researchers, central bank economists, and fiscal regulators. We illustrate the effectiveness of this procedure with applications to the S&P financial market and the European sovereign debt sector using the psymonitor R package developed in conjunction with this chapter

Housing Fever in Australia 2020-2023: Insights from an Econometric Thermometer

Australian housing markets experienced widespread and, in some cases, extraordi-nary growth in prices between 2020 and 2023. Using recently developed methodology that accounts for fundamental economic drivers, we assess the existence and degree of speculative behaviour as well as the timing of exuberance and downturns in these markets. Our findings indicate that speculative behaviour was indeed present in six of the eight capital cities at some time over the period studied. The sequence of events in this nation-wide housing bubble began in the Brisbane market and concluded in Melbourne, Canberra, and Hobart following the interest rate hike implemented by the Reserve Bank of Australia in May 2022. As of March 2023, the housing markets in Syd-ney, Canberra, and Hobart had broadly regained stable conditions, while Melbourne is more gradually returning to its normal state. In addition, over-corrections against fundamentals are evident in the housing markets of Brisbane, Adelaide, Darwin, and Perth. For regular updates on the housing markets, readers may visit the authors’ website at www.housing-fever.com

Sequential Cauchy Combination Test for Multiple Testing Problems with Financial Applications

We introduce a simple tool to control for false discoveries and identify individual signals in scenarios involving many tests, dependent test statistics, and potentially sparse signals. The tool applies the Cauchy combination test recursively on a sequence of expanding subsets of $p$-values and is referred to as the sequential Cauchy combination test. While the original Cauchy combination test aims to make a global statement about a set of null hypotheses by summing transformed $p$-values, our sequential version determines which $p$-values trigger the rejection of the global null. The sequential test achieves strong familywise error rate control, exhibits less conservatism compared to existing controlling procedures when dealing with dependent test statistics, and provides a power boost. As illustrations, we revisit two well-known large-scale multiple testing problems in finance for which the test statistics have either serial dependence or cross-sectional dependence, namely monitoring drift bursts in asset prices and searching for assets with a nonzero alpha. In both applications, the sequential Cauchy combination test proves to be a preferable alternative. It overcomes many of the drawbacks inherent to inequality-based controlling procedures, extreme value approaches, resampling and screening methods, and it improves the power in simulations, leading to distinct empirical outcomes.Comment: 35 pages, 6 figure

Diagnosing Housing Fever with an Econometric Thermometer

Housing fever is a popular term to describe an overheated housing market or housing price bubble. Like other financial asset bubbles, housing fever can inflict harm on the real economy, as indeed the US housing bubble did in the period following 2006 leading up to the general financial crisis and great recession. One contribution that econometricians can make to minimize the harm created by a housing bubble is to provide a quantitative `thermometer’ for diagnosing ongoing housing fever. Early diagnosis can enable prompt and effective policy action that reduces long term damage to the real economy. This paper provides a selective review of the relevant literature on econometric methods for identifying housing bubbles together with some new methods of research and an empirical application. We first present a technical definition of a housing bubble that facilitates empirical work and discuss significant difficulties encountered in practical work and the solutions that have been proposed in the past literature. A major challenge in all econometric identification procedures is to assess prices in relation to fundamentals, which requires measurement of fundamentals. One solution to address this challenge is to estimate the fundamental component from an underlying structural relationship involving measurable variables. A second aim of the paper is to improve the estimation accuracy of fundamentals by means of an easy-to-implement reduced-form approach. Since many of the relevant variables that determine fundamentals are nonstationary and interdependent we use the IVX (Phillips and Magdalinos, 2009) method to estimate the reduced-form model to reduce the finite sample bias which arises from highly persistent regressors and endogeneity. The recursive evolving test of Phillips, Shi and Yu (2015) is applied to the estimated non-fundamental component for the identification of speculative bubbles. The new bubble test developed here is referred to as PSY-IVX. An empirical application to the eight Australian capital city housing markets over the period 1999 to 2017 shows that bubble testing results are sensitive to different ways of controlling for fundamentals and highlights the importance of accurate estimation of these housing market fundamentals

Econometric Analysis of Asset Price Bubbles

In the presence of bubbles, asset prices consist of a fundamental and a bubble component, with the bubble component following an explosive dynamic. The general idea for bubble identification is to apply explosive root tests to a proxy of the unobservable bubble. Three notable proxies are the real asset prices, log price-payoff ratios, and estimated non-fundamental components. The rationale for all three proxy choices rests on the definition of bubbles, which has been presented in various forms in the literature. This chapter provides a theoretical framework that incorporates several definitions of bubbles (and fundamentals) and offers guidance for selecting proxies. For explosive root tests, we introduce the recursive evolving test of Phillips et al. (2015b,c) along with its asymptotic properties. This procedure can serve as a real-time monitoring device and has been shown to outperform several other tests. Like all other recursive testing procedures, the PSY algorithm faces the issue of multiplicity in testing that contaminates conventional significance values. To address this issue, we proposea multiple-testing algorithm to determine appropriate test critical values and show its satisfactory performance in finite samples by simulations. To illustrate, we conduct a pseudo real-time bubble monitoring exercise in the S&P 500 stock market from January 1990 to June 2020. The empirical results reveal the importance of using a good proxy for bubbles and addressing the multiplicity issue

Moving Window Unit Root Test: Locating Real Estate Price Bubbles in Seoul Apartment Market

Bubbles are characterized by rapid expansion followed by a contraction. Evans (1991) shows that stationarity tests suggested by Hamilton and Whiteman (1985) and Diba and Grossman (1988) are incapable of detecting periodically collapsing bubbles. Phillips, Wu, and Yu (2006) advanced the forward recursive unit root test which improves the power significantly in the presence of periodically collapsing bubbles. In this paper, we consider rolling window unit root test with a pre-selected optimum window. A combining use of conventional unit root test and forward recursive unit root test is suggested from the results of power comparison. Furthermore, we apply those three methods to test the existence of bubbles in Seoul apartment market and to locate the bubble period if they were present

Common Bubble Detection in Large Dimensional Financial Systems

Price bubbles in multiple assets are sometimes nearly coincident in occurrence. Such near-coincidence is strongly suggestive of co-movement in the associated asset prices and likely driven by certain factors that are latent in the financial or economic system with common effects across several markets. Can we detect the presence of such common factors at the early stages of their emergence? To answer this question, we build a factor model that includes I(1), mildly explosive, and stationary factors to capture normal, exuberant, and collapsing phases in such phenomena. The I(1) factor models the primary driving force of market fundamentals. The explosive and stationary factors model latent forces that underlie the formation and destruction of asset price bubbles, which typically exist only for subperiods of the sample. The paper provides an algorithm for testing the presence of and date-stamping the origination and termination of price bubbles determined by latent factors in a large-dimensional system embodying many markets. Asymptotics of the bubble test statistic are given under the null of no common bubbles and the alternative of a common bubble across these markets. We prove consistency of a factor bubble detection process for the origination and termination dates of the common bubble. Simulations show good finite sample performance of the testing algorithm in terms of its successful detection rates. Our methods are applied to real estate markets covering 89 major cities in China over the period January 2003 to March 2013. Results suggest the presence of three common bubble episodes in what are known as China’s Tier 1 and Tier 2 cities over the sample period. There appears to be little evidence of a common bubble in Tier 3 cities

Causal change detection in possibly integrated systems: Revisiting the money-income relationship

tru