Book Chapter10.1007/978-0-387-21763-5_4
Unit Root Tests
Eric Zivot,Jiahui Wang +1 more
- 01 Jan 2003
- pp 105-127
TL;DR: Many economic and financial time series exhibit trending behavior or non-stationarity in the mean leading examples are asset prices, exchange rates and the levels of macroeconomic aggregates like real GDP.
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Abstract: Many economic and financial time series exhibit trending behavior or non-stationarity in the mean Leading examples are asset prices, exchange rates and the levels of macroeconomic aggregates like real GDP An important econometric task is determining the most appropriate form of the trend in the data For example, in ARMA modeling the data must be transformed to stationary form prior to analysis If the data are trending, then some form of trend removal is required
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References
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.
26.7K
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.
Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?
TL;DR: In this paper, a test of the null hypothesis that an observable series is stationary around a deterministic trend is proposed, where the series is expressed as the sum of deterministic trends, random walks, and stationary error.
12.9K