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Shrinkage estimation and forecasting in dynamic regression models under structural instability

Mehrabani, Ali; Parsaeian, Shahnaz; Ullah, Aman - 2024

Persistent link: https://www.econbiz.de/10015076825

A Simple Unit Root Test For Near-I(2) Time Series

Morin, Lealand - 2020

We consider the problem of testing for unit root in time series where the error term of the series is near unit root. As the error term approaches unit root, existing tests no longer retain reasonable small sample properties. We introduce a test statistic that is well-behaved in small sample...

Persistent link: https://www.econbiz.de/10012847666

Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order : Critical Comments

Luitel, Hari; Mahar, Gerry - 2022

In this research note, we accomplish two objectives. First, we reexamine the reliability of unit root findings in the study by Said and Dickey (1984) and show that their results are internally consistent. Second, we provide new results from the reanalysis of the original data that were not...

Persistent link: https://www.econbiz.de/10013309626

Jackknife bias reduction in the presence of a unit root

Chambers, Marcus J.; Kyriacou, Maria - 2010

does not eliminate fully the first-order bias as intended. The joint moment generating function of the numerator and … bias reductions that can be obtained with these computationally simple jackknife estimators and they also highlight the … importance of correct lag-length selection in ADF regressions. -- Jackknife ; bias reduction ; unit root …

Persistent link: https://www.econbiz.de/10003927895

A fixed-b perspective on the Phillips-Perron unit root tests

Vogelsang, Timothy J.; Wagner, Martin - 2011

We extend fixed-b asymptotic theory to the nonparametric Phillips-Perron (PP) unit root tests. We show that the fixed-b limits depend on nuisance parameters in a complicated way. These non-pivotal limits provide an alternative theoretical explanation for the well known finite sample problems of...

Persistent link: https://www.econbiz.de/10009686209

Wavelet Power : Wavelet Energy Ratio Unit Root Tests

Trokic, Mirza - 2014

This paper uses wavelet theory to propose a frequency domain nonparametric and tuning parameter free family of unit root tests indexed by the fractional parameter d. The proposed test exploits the wavelet power spectrum of the observed series and its fractional partial sum to construct a test of...

Persistent link: https://www.econbiz.de/10013065650

Regulated Fractional Variance Ratio Unit Root Tests

Trokic, Mirza - 2014

This article addresses unit root testing on regulated series through the variance ratio (VR) statistic of Nielsen (2009). The asymptotic distribution of the regulated VR statistic is developed with and without OLS detrending. Results of Cavaliere and Xu (2011) are extended by also developing the...

Persistent link: https://www.econbiz.de/10013066223

Prediction and simulation using simple models characterized by nonstationarity and seasonality

Swanson, Norman R.; Urbach, Richard - 2013

In this paper, we provide new evidence on the empirical usefulness of various simple seasonal models, and underscore the importance of carefully designing criteria by which one judges alternative models. In particular, we underscore the importance of both choice of forecast or simulation horizon...

Persistent link: https://www.econbiz.de/10009777938

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

Estimation of possibly non-stationary first-order auto-regressive processes

Martins, Ana Paula - 2016

This paper inspects a grid search algorithm to estimate the AR(1) process, based on the joint estimation of the canonical AR(1) equation along with its reverse form. The method relies on the GLS principle, accounting for the covariance error structure of the special estimable system....

Persistent link: https://www.econbiz.de/10011784570

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