Special Issue "Recent Developments in Cointegration"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (28 February 2017)

Special Issue Editor

Guest Editor
Prof. Katarina Juselius

Department of Economics, University of Copenhagen, Copenhagen K, Denmark
Website | E-Mail
Interests: empirical econometrics; the cointegrated VAR; empirical methodology; international macro

Special Issue Information

Dear Colleagues,

This Special Issue contains recent contributions to empirical and theoretical cointegration models. Methodologically oriented papers that combine the two are particularly welcome. I envision papers that, for example, apply cointegration in a novel way, suggest a new way of testing hypotheses in a Cointegrated VAR model, derive new tests motivated by empirical applications, use cointegration analysis to solve interesting problems in macroeconomics and finance, such as the puzzling long, persistent swings around long-run equilibrium values. Papers dealing with near integration (near I(1) and near I(2)) are much welcome. Additionally, papers applying the I(2) cointegration and the fractional cointegration model to relevant economic problems are of great interest and so are theoretical advancements to these models. Cointegration models that address the problem of time-varying coefficients, changes in the equilibrium mean and changes in the mean growth rates are all within the scope of this Special Issue.

More generally, the purpose of the Special Issue is to advance cointegration techniques needed to properly address recent problems in macroeconomics and finance, in particular, problems that became painfully visible during the great recession. For example, using cointegration as a means to address self-reinforcing feed-back loops could potentially add valuable information about some of the mechanisms that generated the recent crisis.

Prof. Katarina Juselius
Guest Editor

Manuscript Submission Information

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Keywords

  • Cointegrated VAR models for I(1), I(2), fractionally integrated and explosive root data
  • Inference in near integrated processes
  • Hypothesis testing
  • Structure analysis
  • Time-varying coefficients
  • Modelling changes in equilibrium means and mean growth rates

Published Papers (10 papers)

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Research

Open AccessArticle Formula I(1) and I(2): Race Tracks for Likelihood Maximization Algorithms of I(1) and I(2) Cointegrated VAR Models
Econometrics 2017, 5(4), 49; doi:10.3390/econometrics5040049 (registering DOI)
Received: 1 July 2017 / Revised: 4 October 2017 / Accepted: 15 October 2017 / Published: 20 November 2017
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Abstract
This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1) and I(2) models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as
[...] Read more.
This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1) and I(2) models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as the ability to find the overall maximum. The next step is to compare their efficiency and reliability across experiments. The aim of the paper is to commence a collective learning project by the profession on the actual properties of algorithms for cointegrated vector autoregressive model estimation, in order to improve their quality and, as a consequence, also the reliability of empirical research. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Short-Term Expectation Formation Versus Long-Term Equilibrium Conditions: The Danish Housing Market
Econometrics 2017, 5(3), 40; doi:10.3390/econometrics5030040
Received: 28 February 2017 / Revised: 1 August 2017 / Accepted: 25 August 2017 / Published: 4 September 2017
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Abstract
The primary contribution of this paper is to establish that the long-swings behavior observed in the market price of Danish housing since the 1970s can be understood by studying the interplay between short-term expectation formation and long-run equilibrium conditions. We introduce an asset
[...] Read more.
The primary contribution of this paper is to establish that the long-swings behavior observed in the market price of Danish housing since the 1970s can be understood by studying the interplay between short-term expectation formation and long-run equilibrium conditions. We introduce an asset market model for housing based on uncertainty rather than risk, which under mild assumptions allows for other forms of forecasting behavior than rational expectations. We test the theory via an I(2) cointegrated VAR model and find that the long-run equilibrium for the housing price corresponds closely to the predictions from the theoretical framework. Additionally, we corroborate previous findings that housing markets are well characterized by short-term momentum forecasting behavior. Our conclusions have wider relevance, since housing prices play a role in the wider Danish economy, and other developed economies, through wealth effects. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Cointegration between Trends and Their Estimators in State Space Models and Cointegrated Vector Autoregressive Models
Econometrics 2017, 5(3), 36; doi:10.3390/econometrics5030036
Received: 1 March 2017 / Revised: 15 August 2017 / Accepted: 17 August 2017 / Published: 22 August 2017
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Abstract
A state space model with an unobserved multivariate random walk and a linear observation equation is studied. The purpose is to find out when the extracted trend cointegrates with its estimator, in the sense that a linear combination is asymptotically stationary. It is
[...] Read more.
A state space model with an unobserved multivariate random walk and a linear observation equation is studied. The purpose is to find out when the extracted trend cointegrates with its estimator, in the sense that a linear combination is asymptotically stationary. It is found that this result holds for the linear combination of the trend that appears in the observation equation. If identifying restrictions are imposed on either the trend or its coefficients in the linear observation equation, it is shown that there is cointegration between the identified trend and its estimator, if and only if the estimators of the coefficients in the observation equations are consistent at a faster rate than the square root of sample size. The same results are found if the observations from the state space model are analysed using a cointegrated vector autoregressive model. The findings are illustrated by a small simulation study. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Using a Theory-Consistent CVAR Scenario to Test an Exchange Rate Model Based on Imperfect Knowledge
Econometrics 2017, 5(3), 30; doi:10.3390/econometrics5030030
Received: 1 March 2017 / Revised: 21 June 2017 / Accepted: 22 June 2017 / Published: 7 July 2017
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Abstract
A theory-consistent CVAR scenario describes a set of testable regularieties one should expect to see in the data if the basic assumptions of the theoretical model are empirically valid. Using this method, the paper demonstrates that all basic assumptions about the shock structure
[...] Read more.
A theory-consistent CVAR scenario describes a set of testable regularieties one should expect to see in the data if the basic assumptions of the theoretical model are empirically valid. Using this method, the paper demonstrates that all basic assumptions about the shock structure and steady-state behavior of an an imperfect knowledge based model for exchange rate determination can be formulated as testable hypotheses on common stochastic trends and cointegration. This model obtaines remarkable support for almost every testable hypothesis and is able to adequately account for the long persistent swings in the real exchange rate. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Modeling Real Exchange Rate Persistence in Chile
Econometrics 2017, 5(3), 29; doi:10.3390/econometrics5030029
Received: 28 February 2017 / Revised: 14 June 2017 / Accepted: 30 June 2017 / Published: 7 July 2017
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Abstract
The long and persistent swings in the real exchange rate have for a long time puzzled economists. Recent models built on imperfect knowledge economics seem to provide a theoretical explanation for this persistence. Empirical results, based on a cointegrated vector autoregressive (CVAR) model,
[...] Read more.
The long and persistent swings in the real exchange rate have for a long time puzzled economists. Recent models built on imperfect knowledge economics seem to provide a theoretical explanation for this persistence. Empirical results, based on a cointegrated vector autoregressive (CVAR) model, provide evidence of error-increasing behavior in prices and interest rates, which is consistent with the persistence observed in the data. The movements in the real exchange rate are compensated by movements in the interest rate spread, which restores the equilibrium in the product market when the real exchange rate moves away from its long-run benchmark value. Fluctuations in the copper price also explain the deviations of the real exchange rate from its long-run equilibrium value. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Likelihood Ratio Tests of Restrictions on Common Trends Loading Matrices in I(2) VAR Systems
Econometrics 2017, 5(3), 28; doi:10.3390/econometrics5030028
Received: 28 February 2017 / Revised: 30 May 2017 / Accepted: 21 June 2017 / Published: 29 June 2017
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Abstract
Likelihood ratio tests of over-identifying restrictions on the common trends loading matrices in I(2) VAR systems are discussed. It is shown how hypotheses on the common trends loading matrices can be translated into hypotheses on the cointegration parameters. Algorithms for (constrained) maximum likelihood
[...] Read more.
Likelihood ratio tests of over-identifying restrictions on the common trends loading matrices in I(2) VAR systems are discussed. It is shown how hypotheses on the common trends loading matrices can be translated into hypotheses on the cointegration parameters. Algorithms for (constrained) maximum likelihood estimation are presented, and asymptotic properties sketched. The techniques are illustrated using the analysis of the PPP and UIP between Switzerland and the US. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
Open AccessArticle Sustainable Financial Obligations and Crisis Cycles
Econometrics 2017, 5(2), 27; doi:10.3390/econometrics5020027
Received: 28 February 2017 / Revised: 23 May 2017 / Accepted: 13 June 2017 / Published: 22 June 2017
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Abstract
The ability to distinguish between sustainable and excessive debt developments is crucial for securing economic stability. By studying US private sector credit loss dynamics, we show that this distinction can be made based on a measure of the incipient aggregate liquidity constraint, the
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The ability to distinguish between sustainable and excessive debt developments is crucial for securing economic stability. By studying US private sector credit loss dynamics, we show that this distinction can be made based on a measure of the incipient aggregate liquidity constraint, the financial obligations ratio. Specifically, as this variable rises, the interaction between credit losses and the business cycle increases, albeit with different intensity depending on whether the problems originate in the household or the business sector. This occurs 1–2 years before each recession in the sample. Our results have implications for macroprudential policy and countercyclical capital-buffers. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles
Econometrics 2017, 5(2), 25; doi:10.3390/econometrics5020025
Received: 20 April 2017 / Revised: 25 May 2017 / Accepted: 7 June 2017 / Published: 14 June 2017
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Abstract
It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the
[...] Read more.
It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper formulates a CVAR model allowing for multiple near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then two critical value adjustments suggested by McCloskey (2017) for the test on the cointegrating relations are implemented for the model with a single near unit root, and it is found by simulation that they eliminate the serious size distortions, with a reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Maximum Likelihood Estimation of the I(2) Model under Linear Restrictions
Econometrics 2017, 5(2), 19; doi:10.3390/econometrics5020019
Received: 27 February 2017 / Revised: 2 May 2017 / Accepted: 8 May 2017 / Published: 15 May 2017
Cited by 2 | PDF Full-text (367 KB) | HTML Full-text | XML Full-text
Abstract
Estimation of the I(2) cointegrated vector autoregressive (CVAR) model is considered. Without further restrictions, estimation of the I(1) model is by reduced-rank regression (Anderson (1951)). Maximum likelihood estimation of I(2) models, on the other hand, always requires iteration. This paper presents a new
[...] Read more.
Estimation of the I(2) cointegrated vector autoregressive (CVAR) model is considered. Without further restrictions, estimation of the I(1) model is by reduced-rank regression (Anderson (1951)). Maximum likelihood estimation of I(2) models, on the other hand, always requires iteration. This paper presents a new triangular representation of the I(2) model. This is the basis for a new estimation procedure of the unrestricted I(2) model, as well as the I(2) model with linear restrictions imposed. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Panel Cointegration Testing in the Presence of Linear Time Trends
Econometrics 2016, 4(4), 45; doi:10.3390/econometrics4040045
Received: 15 May 2016 / Revised: 28 September 2016 / Accepted: 20 October 2016 / Published: 1 November 2016
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Abstract
We consider a class of panel tests of the null hypothesis of no cointegration and cointegration. All tests under investigation rely on single-equations estimated by least squares, and they may be residual-based or not. We focus on test statistics computed from regressions with
[...] Read more.
We consider a class of panel tests of the null hypothesis of no cointegration and cointegration. All tests under investigation rely on single-equations estimated by least squares, and they may be residual-based or not. We focus on test statistics computed from regressions with intercept only (i.e., without detrending) and with at least one of the regressors (integrated of order 1) being dominated by a linear time trend. In such a setting, often encountered in practice, the limiting distributions and critical values provided for and applied with the situation “with intercept only” are not correct. It is demonstrated that their usage results in size distortions growing with the panel size N. Moreover, we show which are the appropriate distributions, and how correct critical values can be obtained from the literature. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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