We develop a portmanteau test of extremal serial dependence. The test statistic is asymptotically chi-squared under a null of "extremal white noise", as long as extremes are Near-Epoch-Dependent, covering linear and nonlinear distributed lags, stochastic volatility, and GARCHprocesses with possib ly unit or explosive roots. We apply tail speci...c tests to equity market and exchange rate return...

In this paper we generalize the KPSS-type test to allow for two structural breaks. Seven models have been de…ned depending on the way that the structural breaks a¤ect the time series behaviour. The paper derives the limit distribution of the test both under the null and the alternative hypotheses and conducts a set of simulation experiments to analyse the performance in …nite samples. Keywords:...

We consider a number of unit root tests for micro panels where the number of individuals is typically large, but the number of time periods is often very small. As we discuss, the presence of a unit root is closely related to the identification of parameters of interest in this context. Calculations of asymptotic local power and Monte Carlo evidence indicate that two simple t-tests based on ord...

A new approach to robust testing in cointegrated systems is proposed using nonparametric HAC estimators without truncation. While such HAC estimates are inconsistent, they still produce asymptotically pivotal tests and, as in conventional regression settings, can improve testing and inference. JEL Classi…cation: C12; C14; C22 Keywords: Cointegration, HAC estimation, robust inference, steep orig...

This paper discusses inference about the pre and post break value of a scalar parameter in GMM time series models with a single break at an unknown point in time. We show that treating the break date estimated by least squares as the true break date leads to substantially oversized tests and confidence intervals unless the break is large. We develop an alternative test that controls size unifor...

This paper aims at improved accuracy in testing for long-run predictability in noisy series, such as stock market returns. Long-horizon regressions have previously been the dominant approach in this area. We suggest an alternative method that yields more accurate results. We find evidence of predictability in S&P 500 returns even when the confidence intervals are constructed using model-free me...

We develop a selective entry model for first-price auctions that nests several models earlier proposed in the literature, and features a pro-competitive selection effect. The selection effect is shown to be nonparametrically identifiable, and a nonparametric test for its presence is proposed. In the empirical application to highway procurement, we find that the selection effect overwhelms the a...

This article proposes a functional dynamic factor model for the evaluation of the impact of scalar– and curve–valued factors on the shapes of intraday price curves. The asymptotic theory leads to practically useful confidence intervals for the factor coefficients. The main findings pertain to the impact of the shapes of intraday oil futures on the shapes of intraday prices of blue chip stocks. ...

We show that the quantile regression estimator is consistent and asymptotically normal when the error terms are correlated within clusters but independent across clusters. A consistent estimator of the covariance matrix of the asymptotic distribution is provided and we propose a specification test capable of detecting the presence of intra-cluster correlation. A small simulation study illustrat...

This paper discusses the existence of spurious long memory in common nonlinear time series models, namely Markov switching and threshold models. We describe the asymptotic behavior of the process in terms of autocovariance and autocorrelation function and support the theoretical evidences by providing Monte Carlo simulation. The existence of long memory in these nonlinear processes is induced b...