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Cointegration Analysis in the Presence of Structural Breaks in the Deterministic Trend.

TLDR
In this paper, a cointegration model with piecewise linear trend and known break points is proposed to test co-integration rank, restrictions on the cointegrating vector as well as the slopes of the broken linear trend.
Abstract
When analysing macroeconomic data it is often of relevance to allow for structural breaks in the statistical analysis. In particular, cointegration analysis in the presence of structural breaks could be of interest. We propose a cointegration model with piecewise linear trend and known break points. Within this model it is possible to test cointegration rank, restrictions on the cointegrating vector as well as restrictions on the slopes of the broken linear trend.

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Dealing with Structural Breaks

TL;DR: In this paper, a review of methods related to estimation and inference about break dates for single equations with or without restrictions, with extensions to multi-equations systems where allowance is also made for changes in the variability of the shocks, tests for structural changes including tests for a single or multiple changes and tests valid with unit root or trending regressors, and tests for changes of the trend function of a series that can be integrated or trend-stationary.
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Crude oil and stock markets: Stability, instability, and bubbles ☆

TL;DR: The authors analyzed the long-run relationship between the world price of crude oil and international stock markets over 1971:1-2008:3 using a cointegrated vector error correction model with additional regressors.
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Explaining Cointegration Analysis: Part II

TL;DR: In this paper, the authors discuss the importance of stationarity for empirical modeling and inference, explain the effects of incorrectly assuming stationarity, and formulate a class of nonstationary processes (autoregressions with unit roots) that seem empirically relevant for analyzing economic time series.
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A small sample correction for the test of cointegrating rank in the vector autoregressive model

TL;DR: In this article, the authors proposed a correction factor for the likelihood ratio test for cointegration in the vector autoregressive model to improve the finite sample properties of the test.
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Who Benefits from Store Brand Entry

TL;DR: In this article, the authors demonstrate permanent performance effects of store brand entry, typically benefiting the retailer, the consumers, and premium-brand manufacturers, while harming second-tier brand manufacturers.
References
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Journal ArticleDOI

Statistical analysis of cointegration vectors

TL;DR: In this paper, the authors consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors, and derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions.
Journal ArticleDOI

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

Pierre Perron
- 01 Nov 1989 - 
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.
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Testing for a Unit Root in a Time Series With a Changing Mean

TL;DR: In this article, the authors consider the change as being exogenous and as occurring at a known date and show that standard unit-root tests are biased toward nonrejection of the hypothesis of a unit root when the full sample is used.
Journal ArticleDOI

Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions

TL;DR: In this paper, the authors studied linear restrictions on regression coefficients and obtained confidence regions under various conditions, including the assumption that the rank of the dependent variates is a given number.
Journal ArticleDOI

Limiting Distributions of Least Squares Estimates of Unstable Autoregressive Processes

Ngai Hang Chan, +1 more
- 01 Mar 1988 - 
TL;DR: The limiting distribution of the least squares estimate of a stochastic integral under a 2 + ε moment assumption on the characteristic polynomial is derived in this article. But this is restricted to the case where the polynomials have all roots on or outside the unit circle.
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