Results 111 to 120 of about 30,567 (245)
Econometrics at the Extreme: From Quantile Regression to QFAVAR1
Journal of Economic Surveys, EarlyView.ABSTRACT This paper surveys quantile modelling from its theoretical origins to current advances. We organize the literature and present core econometric formulations and estimation methods for: (i) cross‐sectional quantile regression; (ii) quantile time series models and their time series properties; (iii) quantile vector autoregressions for ...
Stéphane Goutte +4 more
wiley +1 more source
Penalizing function based bandwidth choice in nonparametric quantile regression [PDF]
In nonparametric mean regression various methods for bandwidth choice exist. These methods can roughly be divided into plug-in methods and methods based on penalizing functions.
Klaus Abberger
core
Monitoring panels of sparse functional data
Journal of Time Series Analysis, EarlyView.Panels of random functions are common in applications of functional data analysis. They often occur when sequences of functions are observed at a number of different locations. We propose a methodology to monitor for structural breaks in such panels and to identify the changing components with statistical certainty.
Tim Kutta +2 more
wiley +1 more source
Applied Computing and Geosciences, 2019 High precision and reliable wind speed forecasting is a challenge for meteorologists. We used multiple nonparametric tree-based machine learning techniques, for predicting the maximum wind speed at 10 m using selected convective weather variables ...
Bhuiyan Md Abul Ehsan +3 more
doaj +1 more source
Cointegrating Polynomial Regressions With Power Law Trends
Journal of Time Series Analysis, EarlyView.ABSTRACT The common practice in cointegrating polynomial regressions (CPRs) often confines nonlinearities in the variable of interest to stochastic trends, thereby overlooking the possibility that they may be caused by deterministic components. As an extension, we propose univariate and multivariate CPRs that incorporate power law deterministic trends.
Yicong Lin, Hanno Reuvers
wiley +1 more source
Nonparametric Inferences on Conditional Quantile Processes [PDF]
This paper is concerned with tests of restrictions on the sample path of conditional quantile processes. These tests are tantamount to assessments of lack of fit for models of conditional quantile functions or more generally as tests of how certain ...
Chuan Goh
core
Gradual Changes in Functional Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We consider the problem of detecting gradual changes in the sequence of mean functions from a not necessarily stationary functional time series. Our approach is based on the maximum deviation (calculated over a given time interval) between a benchmark function and the mean functions at different time points.
Patrick Bastian, Holger Dette
wiley +1 more source
Journal of Time Series Analysis, EarlyView.ABSTRACT In this paper, we propose a new test for the detection of a change in a non‐linear (auto‐)regressive time series as well as a corresponding estimator for the unknown time point of the change. To this end, we consider an at‐most‐one‐change model and approximate the unknown (auto‐)regression function by a neural network with one hidden layer. It
Claudia Kirch, Stefanie Schwaar
wiley +1 more source
Nonparametric Detection of a Time‐Varying Mean
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose a nonparametric portmanteau test for detecting changes in the unconditional mean of a univariate time series which may display either long or short memory. Our approach is designed to have power against, among other things, cases where the mean component of the series displays abrupt level shifts, deterministic trending behaviour ...
Fabrizio Iacone, A. M. Robert Taylor
wiley +1 more source
Quantile regression with varying coefficients
, 2007 Quantile regression provides a framework for modeling statistical quantities of interest other than the conditional mean. The regression methodology is well developed for linear models, but less so for nonparametric models.
Kim, Mi-Ok
core +1 more source