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Journal ArticleDOI

The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis

Pierre Perron
- 01 Nov 1989 - 
- Vol. 57, Iss: 6, pp 1361-1401
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TLDR
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.
Abstract
We 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». The interest is that we allow under both the null and alternative hypotheses for the presence for a one-time change in the level or in the slope of the trend function. We 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

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Journal ArticleDOI

Structural Analysis of Cointegrating VARs

TL;DR: In this paper, a survey of recent developments in the analysis of cointegrating vector autoregressions (VARs) to examine their links to the older structural modelling traditions using Autoregressive Distributed Lag (ARDL), and Simultaneous Equations Models (SEMs).
Journal ArticleDOI

Improving GARCH volatility forecasts with regime-switching GARCH

TL;DR: The authors generalizes the GARCH model by distinguishing two regimes with different volatility levels; GARCH effects are allowed within each regime, and the resulting Markov regime-switching Garch model improves on existing variants, for instance by making multi-period-ahead volatility forecasting a convenient recursive procedure.
Journal ArticleDOI

Less than 2 °C warming by 2100 unlikely

TL;DR: A country-specific version of Kaya’s identity is used to develop a statistically-based probabilistic forecast of CO2 emissions and temperature change to 2100, and a joint Bayesian hierarchical model for GDP per capita and carbon intensity is developed.
Journal ArticleDOI

CO2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China

TL;DR: Based on the autoregressive distributed lag (ARDL) bounds testing approach and vector error correction model (VECM) Granger causality approach, the authors explores the relationships among per capita carbon dioxide (CO2) emissions, gross domestic product (GDP), renewable, non-renewable energy production and foreign trade for China covering the period 1980-2014.
Journal ArticleDOI

Testing for and Dating Common Breaks in Multivariate Time Series

TL;DR: The authors developed methods for constructing asymptotically valid confidence intervals for the date of a single break in multivariate time series, including I(0), I(1), and deterministically trending regressors.
References
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Journal ArticleDOI

Co-integration and Error Correction: Representation, Estimation and Testing

TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
Journal ArticleDOI

Distribution of the Estimators for Autoregressive Time Series with a Unit Root

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.
Book

Convergence of Probability Measures

TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
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