Testing for nonlinear cointegration under heteroskedasticity Anzeigen

This article discusses cointegration tests for nonlinear cointegration in the presence of variance breaks in the errors. We build on approaches of Cavaliere and Taylor (2006, Journal of Time Series Analysis) for heteroskedastic cointegration tests and of Choi and Saikkonen (2010, Econometric Theory) for nonlinear cointegration tests. We propose a bootstrap test and prove its consistency. A Monte Carlo study shows the approach to have appealing finite sample properties and to work better...

A portmanteau-type test for detecting serial correlation in locally stationary functional time series Anzeigen

The Portmanteau test provides the vanilla method for detecting serial correlations in classical univariate time series analysis. The method is extended to the case of observations from a locally stationary functional time series. Asymptotic critical values are obtained by a suitable block multiplier bootstrap procedure. The test is shown to asymptotically hold its level and to be consistent against general alternatives.

A note on optimal designs for estimating the slope of a polynomial regression Anzeigen

In this note we consider the optimal design problem for estimating the slope of a polynomial regression with no intercept at a given point, say z. In contrast to previous work, which considers symmetric design spaces we investigate the model on the interval [0; a] and characterize those values of z, where an explicit solution of the optimal design is possible.

Correcting intraday periodicity bias in realized volatility measures Anzeigen

Diurnal fluctuations in volatility are a well-documented stylized fact of intraday price data. We investigate how this intraday periodicity (IP) affects both finite sample as well as asymptotic properties of several popular realized estimators of daily integrated volatility which are based on functionals of M intraday returns. We demonstrate that most of the estimators considered in our study exhibit a finite-sample bias due to IP, which can however get negligible if the number of intraday r...

Data-based priors for vector error correction models Anzeigen

We propose two data-based priors for vector error correction models. Both priors lead to highly automatic approaches which require only minimal user input. An empirical investigation reveals that Bayesian vector error correction (BVEC) models equipped with our proposed priors turn out to scale well to higher dimensions and to forecast well. In addition, we find that exploiting information in the level variables has the potential for improving long-term forecasts. Thus, working with VARs...

Difference-in-differences estimation under non-parallel trends Anzeigen

Classic difference-in-differences estimation relies on the validity of the "parallel trends assumption" (PTA), which ensures that the evolution of the variable of interest in the control group can be used to determine its counterfactual development in the treatment group in the absence of treatment. The plausibility of the PTA is usually assessed by a test of the null hypothesis that the difference between the means of both groups is constant over time before the treatment. However, this...

Correcting intraday periodicity bias in realized volatility measures Anzeigen

Diurnal fluctuations in volatility are a well-documented stylized fact of intraday price data. We investigate how this intraday periodicity (IP) affects both finite sample as well as asymptotic properties of several popular realized estimators of daily integrated volatility which are based on functionals of M intraday returns. We demonstrate that most of the estimators considered in our study exhibit a finite-sample bias due to IP, which can however get negligible if the number of intrad...

New model-based bioequivalence statistical approaches for pharmacokinetic studies with sparse sampling Anzeigen

In traditional pharmacokinetic (PK) bioequivalence analysis, two one-sided tests (TOST) are conducted on the area under the concentration-time curve and the maximal concentration derived using a non-compartmental approach. When rich sampling is unfeasible, a model-based (MB) approach, using nonlinear mixed effect models (NLMEM) is possible. However, MB-TOST using asymptotic standard errors (SE) presents increased type I error when asymptotic conditions do not hold. Methods : In this work, we ...

A global-local prior for time-varying parameter VARs and monetary policy Anzeigen

Time-varying parameter VARs have become the workhorse models in empirical macroeconomics. These models are usually equipped with tightly parametrized prior distributions which favor a small and gradual change in parameters. Do such prior distributions suppress some degree of time variation in the VAR coefficients? We address this question by proposing a exible global-local prior. It turns out that the conventional prior may suppress economically relevant patterns of time variation. Usi...

Detecting relevant differences in the covariance operators of functional time series - a sup-norm approach Anzeigen

In this paper we propose statistical inference tools for the covariance operators of functional time series in the two sample and change point problem. In contrast to most of the literature the focus of our approach is not testing the null hypothesis of exact equality of the covariance operators. Instead we propose to formulate the null hypotheses in the form that "the distance between the operators is small", where we measure deviations by the sup-norm. We provide powerful bootstrap tes...

Dekarbonisierung bis zum Jahr 2050? Klimapolitische Maßnahmen und Energieprognosen für Deutschland, Österreich und die Schweiz Anzeigen

Angesichts der wachsenden klimapolitischen Herausforderungen streben viele Länder Europas bis zum Jahr 2050 eine Dekarbonisierung an, das heißt den Ausstieg aus der Nutzung fossiler Energieträger. Vor diesem Hintergrund präsentiert dieser Beitrag Prognosen des Energiebedarfs und der Energiemixe für Deutschland, Österreich und die Schweiz für das Jahr 2030 sowie einen Ausblick auf das Jahr 2050. Der Vergleich der bisherigen Energiepolitiken dieser Länder offenbart gravierende Unterschiede...

Sequential change point detection in high dimensional time series Anzeigen

Change point detection in high dimensional data has found considerable interest in recent years. Most of the literature designs methodology for a retrospective analysis, where the whole sample is already available when the statistical inference begins. This paper takes a different point of view and develops monitoring schemes for the online scenario, where high dimensional data arrives steadily and the goal is to detect changes as fast as possible controlling at the same time the probab...

A distribution free test for changes in the trend function of locally stationary processes Anzeigen

In the common time series model Xi,n = μ(i/n)+"i,n with non-stationary errors we consider the problem of detecting a significant deviation of the mean function g(μ) from a benchmark g(μ) (such as the initial value μ(0) or the average trend R 1 0 μ(t)dt). The problem is motivated by a more realistic modelling of change point analysis, where one is interested in identifying relevant deviations in a smoothly varying sequence of means (μ(i/n))i=1,...,n and cannot assume that the sequence is piece...

K-sign depth: From asymptotics to efficient implementation Anzeigen

The K-sign depth (K-depth) of a model parameter θ in a data set is the relative number of K-tuples among its residual vector that have alternating signs. The K-depth test based on K-depth, recently proposed by Leckey et al. (2019), is equivalent to the classical residual-based sign test for K = 2, but is much more powerful for K ≥ 3. This test has two major drawbacks. First, the computation of the K-depth is fairly time consuming, and second, the test requires knowledge about the quantiles of...

Powerful generalized sign tests based on sign depth Anzeigen

The classical sign test usually provides very bad power for certain alternatives. We present a generalization which is similarly easy to comprehend but much more powerful. It is based on K-sign depth, shortly denoted by K-depth. These so-called K-depth tests are motivated by simplicial regression depth, but are not restricted to regression problems. They can be applied as soon as the true model leads to independent residuals with median equal to zero. Moreover, general hypotheses on the ...

Market premia for renewables in Germany: The effect on electricity prices Anzeigen

Due to the growing share of ”green” electricity generated by renewable energy technologies, the frequency of negative price spikes has substantially increased in Germany. To reduce such events, in 2012, a market premium scheme (MPS) was introduced as an alternative to feed-in tariffs for the promotion of green electricity. Drawing on hourly day-ahead spot prices for the time period spanning 2009 to 2016 and employing a nonparametric modeling strategy called Bayesian Additive Regression ...

Efficient tests for bio-equivalence in functional data Anzeigen

We study the problem of testing the equivalence of functional parameters (such as the mean or variance function) in the two sample functional data problem. In contrast to previous work, which reduces the functional problem to a multiple testing problem for the equivalence of scalar data by comparing the functions at each point, our approach is based on an estimate of a distance measuring the maximum deviation between the two functional parameters. Equivalence is claimed if the estimate f...

Providing Information by Resource- Constrained Data Analysis Anzeigen

The Collaborative Research Center SFB 876 (Providing Information by Resource-Constrained Data Analysis) brings together the research fields of data analysis (Data Mining, Knowledge Discovery in Data Bases, Machine Learning, Statistics) and embedded systems and enhances their methods such that information from distributed, dynamic masses of data becomes available anytime and anywhere. The research center approaches these problems with new algorithms respecting the resource constraints in the d...

Pivotal tests for relevant differences in the second order dynamics of functional time series Anzeigen

Motivated by the need to statistically quantify differences between modern (complex) datasets which commonly result as high-resolution measurements of stochastic processes varying over a continuum, we propose novel testing procedures to detect relevant differences between the second order dynamics of two functional time series. In order to take the between-function dynamics into account that characterize this type of functional data, a frequency domain approach is taken. Test statistics ...