Journal ArticleDOI
Testing for unit roots in autoregressive-moving average models of unknown order
Said E. Said,David A. Dickey +1 more
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TLDR
In this paper, the authors developed a test for unit roots which is based on an approximation of an autoregressive-moving average model by an auto-gression, which has a limit distribution whose percentiles have been tabulated.Abstract:
SUMMARY Recently, methods for detecting unit roots in autoregressive and autoregressivemoving average time series have been proposed. The presence of a unit root indicates that the time series is not stationary but that differencing will reduce it to stationarity. The tests proposed to date require specification of the number of autoregressive and moving average coefficients in the model. In this paper we develop a test for unit roots which is based on an approximation of an autoregressive-moving average model by an autoregression. The test statistic is standard output from most regression programs and has a limit distribution whose percentiles have been tabulated. An example is provided.read more
Citations
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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
Testing for unit roots in heterogeneous panels
TL;DR: In this article, a unit root test for dynamic heterogeneous panels based on the mean of individual unit root statistics is proposed, which converges in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension)→∞.
Journal ArticleDOI
Unit root tests in panel data: asymptotic and finite-sample properties
TL;DR: In this article, the authors consider pooling cross-section time series data for testing the unit root hypothesis, and they show that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unit-root test for each individual time series.
Journal ArticleDOI
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.
Journal ArticleDOI
The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis
TL;DR: In this paper, the authors consider the null hypothesis that a time series has a unit root with possibly nonzero drift against the alternative that the process is "trend-stationary" and show how standard tests of the unit root hypothesis against trend stationary alternatives cannot reject the unit-root hypothesis if the true data generating mechanism is that of stationary fluctuations around a trend function which contains a one-time break.
References
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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.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Book
Introduction to Statistical Time Series
TL;DR: In this paper, Fourier analysis is used to estimate the mean and autocorrelations of the Fourier spectral properties of a Fourier wavelet and the estimated spectrum of the wavelet.
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Distribution of the Estimators for Autoregressive Time Series with a Unit Root
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