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Testing continuity of a density via g-order statistics in the regression discontinuity design
Bugni, Federico A.; Canay, Ivan A. - 2018
test for continuity of a density at a point based on the so-called g-order statistics, and study its properties under a …
Persistent link: https://www.econbiz.de/10011809514
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Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction
Cheng, Tingting; Gao, Jiti; Linton, Oliver - 2018
historical mean model in terms of in-sample fitting, and also for some cases in terms of the out-of-sample forecasting. …
Persistent link: https://www.econbiz.de/10011775136
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Additive nonparametric models with time variable and both stationary and nonstationary regressions
Dong, Chaohua; Linton, Oliver - 2017
This paper considers nonparametric additive models that have a deterministic time trend and both stationary and integrated variables as components. The diverse nature of the regressors caters for applications in a variety of settings. In addition, we extend the analysis to allow the stationary...
Persistent link: https://www.econbiz.de/10011775349
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Non-parametric transformation regression with non-stationary data
Linton, Oliver; Wang, Qiying - 2013
We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.
Persistent link: https://www.econbiz.de/10009734305
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Copula-based nonlinear quantile autoregression
Chen, Xiaohong (contributor); Koenker, Roger (contributor);  … - 2008
Parametric copulas are shown to be attractive devices for specifying quantile autoregressive models for nonlinear time-series. Estimation of local, quantile-specific copula-based time series models offers some salient advantages over classical global parametric approaches. Consistency and...
Persistent link: https://www.econbiz.de/10003765985
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Nonparametric regression for locally stationary time series
Vogt, Michael - 2012
In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the...
Persistent link: https://www.econbiz.de/10009614397
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Finite- and large-sample inference for ranks using multinomial data with an application to ranking political parties
Bazylik, Sergei; Mogstad, Magne; Romano, Joseph P.; … - 2021
surveys or polls about political attitudes. Since these rankings are computed using estimates of the share of support rather … our methodology to rank political parties in Australia using data from the 2019 Australian Election Survey. We find that … territories with few survey respondents and/or with parties that are chosen by only a small share of the survey respondents. In …
Persistent link: https://www.econbiz.de/10012667308
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LASSO-driven inference in time and space
Chernozhukov, Victor; Härdle, Wolfgang; Huang, Chen; … - 2019
We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of regressions with many regressors using LASSO...
Persistent link: https://www.econbiz.de/10012003693
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Kernel block bootstrap
Parente, Paulo M. D. C.; Smith, Richard J. - 2018 - This draft: July 2018
This article introduces and investigates the properties of a new bootstrap method for time-series data, the kernel block bootstrap. The bootstrap method, although akin to, offers an improvement over the tapered block bootstrap of Paparoditis and Politis (2001), admitting kernels with unbounded...
Persistent link: https://www.econbiz.de/10011878210
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LASSO-driven inference in time and space
Chernozhukov, Victor; Härdle, Wolfgang; Huang, Chen; … - 2018
We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to...
Persistent link: https://www.econbiz.de/10011865621