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Testing missing at random using instrumental variables

Breunig, Christoph - 2015

This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure based on integrated squared distance is proposed. The statistic’s...

Persistent link: https://www.econbiz.de/10010503886

Pseudo-Maximum Likelihood Estimation in Two Classes of Semiparametric Diffusion Models

Kristensen, Dennis - 2009

A novel estimation method for two classes of semiparametric scalar diffusion models is proposed: In the first class, the diffusion term is parameterised and the drift is left unspecified, while in the second class only the drift term is specified. Under the assumption of stationarity, the...

Persistent link: https://www.econbiz.de/10013156186

Shortcomings of a Parametric VaR Approach and Nonparametric Improvements Based on a Non-Stationary Return Series Model

Gürtler, Marc - 2009

volatility dynamics are modelled both exogenously and deterministic, captured by a nonparametric regression-type approach …. Consistency and asymptotic normality of a symmetric and of a one-sided kernel estimator of volatility are outlined with remarks on …

Persistent link: https://www.econbiz.de/10013159079

Semiparametrically Point-Optimal Hybrid Rank Tests for Unit Roots

Zhou, Bo - 2019

We propose a new class of unit root tests that exploits invariance properties in the Locally Asymptotically Brownian Functional limit experiment associated to the unit root model. The invariance structures naturally suggest tests that are based on the ranks of the increments of the observations,...

Persistent link: https://www.econbiz.de/10012903532

Testing missing at random using instrumental variables

Breunig, Christoph - 2017

Persistent link: https://www.econbiz.de/10011894725

Semiparametrically Optimal Cointegration Test

Zhou, Bo - 2023

This paper aims to address the issue of semiparametric efficiency for cointegration rank testing in finite-order vector autoregressive models, where the innovation distribution is considered an infinite-dimensional nuisance parameter. Our asymptotic analysis relies on Le Cam's theory of limit...

Persistent link: https://www.econbiz.de/10014347665

Nonparametric Estimation of a Periodic Sequence in the Presence of a Smooth Trend

Vogt, Michael; Linton, Oliver B. - 2012

In this paper, we study a nonparametric regression model including a periodic component, a smooth trend function, and a stochastic error term. We propose a procedure to estimate the unknown period and the function values of the periodic component as well as the nonparametric trend function. The...

Persistent link: https://www.econbiz.de/10014165806

Detecting serial dependence in tail events

Diks, Cees G. H. - 2002

A test for serial independence is proposed which is related to the BDS test but focuses on tail event probabilities rather than probabilities near the center of the distribution. The motivation behind this approach is to obtain a test more suitable for detecting structure in the tails, such as...

Persistent link: https://www.econbiz.de/10011327543

Nonparametric change-point analysis of volatility

Bibinger, Markus; Jirak, Moritz; Vetter, Mathias - 2015

This work develops change-point methods for statistics of high-frequency data. The main interest is the volatility of … discriminate different smoothness classes of the underlying stochastic volatility process. In a high-frequency framework we prove … extremely mild smoothness assumptions on the stochastic volatility we thereby derive a consistent test for volatility jumps. A …

Persistent link: https://www.econbiz.de/10010477582

Long Run Canonical Correlations : Estimation and Inference

Dovonon, Prosper - 2009

This paper proposes methods for both the consistent estimation of so-called long run canonical correlations (LRCCs) and also testing the null hypothesis that a subset of LRCCs are zero. Two test statistics are proposed and their limiting distribution is derived under the null hypothesis. It is...

Persistent link: https://www.econbiz.de/10013155084