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Income inequality, economic growth, and structural changes in Egypt: new insights from quantile cointegration approach
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This article is published in Economic change and restructuring.The article was published on 2022-07-16. It has received 1 citations till now. The article focuses on the topics: Quantile & Cointegration.read more
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Journal ArticleDOI
Distribution of the Estimators for Autoregressive Time Series with a Unit Root
David A. Dickey,Wayne A. Fuller +1 more
TL;DR: In this article, the limit distributions of the estimator of p and of the regression t test are derived under the assumption that p = ± 1, where p is a fixed constant and t is a sequence of independent normal random variables.
Journal ArticleDOI
Testing for a Unit Root in Time Series Regression
TL;DR: In this article, the authors proposed new tests for detecting the presence of a unit root in quite general time series models, which accommodate models with a fitted drift and a time trend so that they may be used to discriminate between unit root nonstationarity and stationarity about a deterministic trend.
Journal ArticleDOI
Bounds testing approaches to the analysis of level relationships
TL;DR: In this paper, the authors developed a new approach to the problem of testing the existence of a level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary.
Journal ArticleDOI
Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis
Eric Zivot,Donald W.K. Andrews +1 more
TL;DR: In this paper, a variation of Perron's test is considered in which the breakpoint is estimated rather than fixed, and the asymptotic distribution of the estimated breakpoint test statistic is determined.
ReportDOI
Efficient Tests for an Autoregressive Unit Root
TL;DR: In this paper, a modified version of the Dickey-Fuller t test is proposed to improve the power when an unknown mean or trend is present, and a Monte Carlo experiment indicates that the modified test works well in small samples.