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Academic literature on the topic 'Cointegration models'

Author: Grafiati

Published: 28 May 2022

Last updated: 18 July 2025

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Journal articles on the topic "Cointegration models"

1

Aue, Alexander, Lajos Horváth, Clifford Hurvich, and Philippe Soulier. "LIMIT LAWS IN TRANSACTION-LEVEL ASSET PRICE MODELS." Econometric Theory 30, no. 3 (2013): 536–79. http://dx.doi.org/10.1017/s0266466613000406.

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We consider pure-jump transaction-level models for asset prices in continuous time, driven by point processes. In a bivariate model that admits cointegration, we allow for time deformations to account for such effects as intraday seasonal patterns in volatility and nontrading periods that may be different for the two assets. We also allow for asymmetries (leverage effects). We obtain the asymptotic distribution of the log-price process. For the weak fractional cointegration case, we obtain the asymptotic distribution of the ordinary least squares estimator of the cointegrating parameter based on data sampled from an equally spaced discretization of calendar time, and we justify a feasible method of hypothesis testing for the cointegrating parameter based on the correspondingt-statistic. In the strong fractional cointegration case, we obtain the limiting distribution of a continuously averaged tapered estimator as well as other estimators of the cointegrating parameter, and we find that the rate of convergence can be affected by properties of intertrade durations. In particular, the persistence of durations (hence of volatility) can affect the degree of cointegration. We also obtain the rate of convergence of several estimators of the cointegrating parameter in the standard cointegration case. Finally, we consider the properties of the ordinary least squares estimator of the regression parameter in a spurious regression, i.e., in the absence of cointegration.

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Hwan Seo, Myung. "ESTIMATION OF NONLINEAR ERROR CORRECTION MODELS." Econometric Theory 27, no. 2 (2010): 201–34. http://dx.doi.org/10.1017/s026646661000023x.

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Asymptotic theory for the estimation of nonlinear vector error correction models that exhibit regime-specific short-run dynamics is developed. In particular, regimes are determined by the error correction term, and the transition between regimes is allowed to be discontinuous, as in, e.g., threshold cointegration. Several nonregular problems are resolved. First of all, consistency—square rootnconsistency for the cointegrating vectorβ—is established for the least squares estimation of this general class of models. Second, the convergence rates are obtained for the least squares of threshold cointegration, which aren3/2andnforβandγ, respectively, whereγdenotes the threshold parameter. This fast rate forβin itself is of practical relevance because, unlike in smooth transition models, the estimation error inβdoes not affect the estimation of short-run parameters. We also derive asymptotic distributions for the smoothed least squares estimation of threshold cointegration.

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Olaniran, Saidat Fehintola, Oyebayo Ridwan Olaniran, Jeza Allohibi, and Abdulmajeed Atiah Alharbi. "A Novel Approach for Testing Fractional Cointegration in Panel Data Models with Fixed Effects." Fractal and Fractional 8, no. 9 (2024): 527. http://dx.doi.org/10.3390/fractalfract8090527.

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Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behavior of our proposed test is examined through extensive Monte Carlo experiments under various data-generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels, even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labor wage of 36 countries’ agricultural markets.

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Black, Angela J., David G. McMillan, and Fiona J. McMillan. "Cointegration between stock prices, dividends, output and consumption." Review of Accounting and Finance 14, no. 1 (2015): 81–103. http://dx.doi.org/10.1108/raf-09-2013-0103.

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Purpose – This paper aims to empirically test for multiple cointegrating vectors in a holistic manner. Theoretical developments imply bivariate cointegration among stock prices, dividends, output and consumption where independent models identify key theoretical cointegration vectors. Design/methodology/approach – This paper considers both Johansen and Horvath–Watson testing approaches for cointegration. This paper also examines the forecasting power of these cointegrating relationships against alternate forecast variables. Findings – The results suggest evidence of a long-run cointegrating relationship between stock prices, dividends, output and consumption, although not necessarily linked by a single common stochastic trend; each series responds to disequilibrium with greater evidence of a reaction from dividends and consumption – of note, output responds to changes in stock market equilibrium; and there is forecast power from the joint stock market–macro cointegrating vector for stocks returns and consumption growth over the historical average. Of particular note, other forecast models that include consumption perform well and suggest a key role for this variable in stock return and consumption growth forecasts. Originality/value – This is the first paper to combine the cointegrating relationships between stocks, dividends, output and consumption. Thus, the empirical validity of stated theoretical hypotheses can be analysed. The forecast results also demonstrate the usefulness of this. They also show that forecast models that include consumption perform well and suggest a key role for this variable in stock return and consumption growth forecasts.

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Xiao, Zhijie. "Functional-coefficient cointegration models." Journal of Econometrics 152, no. 2 (2009): 81–92. http://dx.doi.org/10.1016/j.jeconom.2009.01.008.

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Jumah, Adusei, and Robert M. Kunst. "Prediction of Consumption and Income in National Accounts: Simulation-Based Forecast Model Selection." Engineering Proceedings 5, no. 1 (2021): 55. http://dx.doi.org/10.3390/engproc2021005055.

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Simulation-based forecast model selection considers two candidate forecast model classes, simulates from both models fitted to data, applies both forecast models to simulated structures, and evaluates the relative benefit of each candidate prediction tool. This approach, for example, determines a sample size beyond which a candidate predicts best. In an application, aggregate household consumption and disposable income provide an example for error correction. With panel data for European countries, we explore whether and to what degree the cointegration properties benefit forecasting. It evolves that statistical evidence on cointegration is not equivalent to better forecasting properties by the implied cointegrating structure.

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Elliott, Graham, and Elena Pesavento. "TESTING THE NULL OF NO COINTEGRATION WHEN COVARIATES ARE KNOWN TO HAVE A UNIT ROOT." Econometric Theory 25, no. 6 (2009): 1829–50. http://dx.doi.org/10.1017/s026646660999034x.

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A number of tests have been suggested for the test of the null of no cointegration. Under this null, correlations are spurious in the sense of Granger and Newbold (1974) and Phillips (1986). We examine a set of models local to the null of no cointegration and derive tests with optimality properties in order to examine the efficiency of available tests. We find that, for a sufficiently tight weighting over potential cointegrating vectors, commonly employed full system tests have power that can, in some situations, be quite far from the power bounds for the models examined.

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Biondini, Riccardo, Yan-Xia Lin, and Michael Mccrae. "A case study of the residual-based cointegration procedure." Journal of Applied Mathematics and Decision Sciences 7, no. 1 (2003): 29–48. http://dx.doi.org/10.1155/s1173912603000038.

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The study of long-run equilibrium processes is a significant component of economic and finance theory. The Johansen technique for identifying the existence of such long-run stationary equilibrium conditions among financial time series allows the identification of all potential linearly independent cointegrating vectors within a given system of eligible financial time series. The practical application of the technique may be restricted, however, by the pre-condition that the underlying data generating process fits a finite-order vector autoregression (VAR) model with white noise. This paper studies an alternative method for determining cointegrating relationships without such a pre-condition. The method is simple to implement through commonly available statistical packages. This ‘residual-based cointegration’ (RBC) technique uses the relationship between cointegration and univariate Box-Jenkins ARIMA models to identify cointegrating vectors through the rank of the covariance matrix of the residual processes which result from the fitting of univariate ARIMA models. The RBC approach for identifying multivariate cointegrating vectors is explained and then demonstrated through simulated examples. The RBC and Johansen techniques are then both implemented using several real-life financial time series.

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Skrobotov, A. A. "Structural breaks in cointegration models." Applied Econometrics 63 (2021): 117–41. http://dx.doi.org/10.22394/1993-7601-2021-63-117-141.

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Deistler, Manfred, and Martin Wagner. "Cointegration in singular ARMA models." Economics Letters 155 (June 2017): 39–42. http://dx.doi.org/10.1016/j.econlet.2017.03.001.

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Dissertations / Theses on the topic "Cointegration models"

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Pashourtidou, Nicoletta. "Cointegration in misspecified models." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252324.

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Al-Balaa, Norah Rashid. "On the estimation of cointegration models." Thesis, Aberystwyth University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271006.

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Silvestrini, Andrea. "Essays on aggregation and cointegration of econometric models." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210304.

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This dissertation can be broadly divided into two independent parts. The first three chapters analyse issues related to temporal and contemporaneous aggregation of econometric models. The fourth chapter contains an application of Bayesian techniques to investigate whether the post transition fiscal policy of Poland is sustainable in the long run and consistent with an intertemporal budget constraint.<p><p><p>Chapter 1 surveys the econometric methodology of temporal aggregation for a wide range of univariate and multivariate time series models. <p><p><p>A unified overview of temporal aggregation techniques for this broad class of processes is presented in the first part of the chapter and the main results are summarized. In each case, assuming to know the underlying process at the disaggregate frequency, the aim is to find the appropriate model for the aggregated data. Additional topics concerning temporal aggregation of ARIMA-GARCH models (see Drost and Nijman, 1993) are discussed and several examples presented. Systematic sampling schemes are also reviewed.<p><p><p>Multivariate models, which show interesting features under temporal aggregation (Breitung and Swanson, 2002, Marcellino, 1999, Hafner, 2008), are examined in the second part of the chapter. In particular, the focus is on temporal aggregation of VARMA models and on the related concept of spurious instantaneous causality, which is not a time series property invariant to temporal aggregation. On the other hand, as pointed out by Marcellino (1999), other important time series features as cointegration and presence of unit roots are invariant to temporal aggregation and are not induced by it.<p><p><p>Some empirical applications based on macroeconomic and financial data illustrate all the techniques surveyed and the main results.<p><p>Chapter 2 is an attempt to monitor fiscal variables in the Euro area, building an early warning signal indicator for assessing the development of public finances in the short-run and exploiting the existence of monthly budgetary statistics from France, taken as "example country". <p><p><p>The application is conducted focusing on the cash State deficit, looking at components from the revenue and expenditure sides. For each component, monthly ARIMA models are estimated and then temporally aggregated to the annual frequency, as the policy makers are interested in yearly predictions. <p><p><p>The short-run forecasting exercises carried out for years 2002, 2003 and 2004 highlight the fact that the one-step-ahead predictions based on the temporally aggregated models generally outperform those delivered by standard monthly ARIMA modeling, as well as the official forecasts made available by the French government, for each of the eleven components and thus for the whole State deficit. More importantly, by the middle of the year, very accurate predictions for the current year are made available. <p><p>The proposed method could be extremely useful, providing policy makers with a valuable indicator when assessing the development of public finances in the short-run (one year horizon or even less). <p><p><p>Chapter 3 deals with the issue of forecasting contemporaneous time series aggregates. The performance of "aggregate" and "disaggregate" predictors in forecasting contemporaneously aggregated vector ARMA (VARMA) processes is compared. An aggregate predictor is built by forecasting directly the aggregate process, as it results from contemporaneous aggregation of the data generating vector process. A disaggregate predictor is a predictor obtained from aggregation of univariate forecasts for the individual components of the data generating vector process. <p><p>The econometric framework is broadly based on Lütkepohl (1987). The necessary and sufficient condition for the equality of mean squared errors associated with the two competing methods in the bivariate VMA(1) case is provided. It is argued that the condition of equality of predictors as stated in Lütkepohl (1987), although necessary and sufficient for the equality of the predictors, is sufficient (but not necessary) for the equality of mean squared errors. <p><p><p>Furthermore, it is shown that the same forecasting accuracy for the two predictors can be achieved using specific assumptions on the parameters of the VMA(1) structure. <p><p><p>Finally, an empirical application that involves the problem of forecasting the Italian monetary aggregate M1 on the basis of annual time series ranging from 1948 until 1998, prior to the creation of the European Economic and Monetary Union (EMU), is presented to show the relevance of the topic. In the empirical application, the framework is further generalized to deal with heteroskedastic and cross-correlated innovations. <p><p><p>Chapter 4 deals with a cointegration analysis applied to the empirical investigation of fiscal sustainability. The focus is on a particular country: Poland. The choice of Poland is not random. First, the motivation stems from the fact that fiscal sustainability is a central topic for most of the economies of Eastern Europe. Second, this is one of the first countries to start the transition process to a market economy (since 1989), providing a relatively favorable institutional setting within which to study fiscal sustainability (see Green, Holmes and Kowalski, 2001). The emphasis is on the feasibility of a permanent deficit in the long-run, meaning whether a government can continue to operate under its current fiscal policy indefinitely.<p><p>The empirical analysis to examine debt stabilization is made up by two steps. <p><p>First, a Bayesian methodology is applied to conduct inference about the cointegrating relationship between budget revenues and (inclusive of interest) expenditures and to select the cointegrating rank. This task is complicated by the conceptual difficulty linked to the choice of the prior distributions for the parameters relevant to the economic problem under study (Villani, 2005).<p><p>Second, Bayesian inference is applied to the estimation of the normalized cointegrating vector between budget revenues and expenditures. With a single cointegrating equation, some known results concerning the posterior density of the cointegrating vector may be used (see Bauwens, Lubrano and Richard, 1999). <p><p>The priors used in the paper leads to straightforward posterior calculations which can be easily performed.<p>Moreover, the posterior analysis leads to a careful assessment of the magnitude of the cointegrating vector. Finally, it is shown to what extent the likelihood of the data is important in revising the available prior information, relying on numerical integration techniques based on deterministic methods.<p><br>Doctorat en Sciences économiques et de gestion<br>info:eu-repo/semantics/nonPublished

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Li, Hongyi. "Small sample inference in unit roots and cointegration models." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1263403552.

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Abhayaratne, Anoma S. P. "Growth and international trade in developing countries : an empirical analysis." Thesis, University of Essex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242227.

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Lebo, Matthew Jonathan. "Fractional Integration and Political Modeling." Thesis, University of North Texas, 1999. https://digital.library.unt.edu/ark:/67531/metadc2229/.

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This dissertation investigates the consequences of fractional dynamics for political modeling. Using Monte Carlo analyses, Chapters II and III investigate the threats to statistical inference posed by including fractionally integrated variables in bivariate and multivariate regressions. Fractional differencing is the most appropriate tool to guard against spurious regressions and other threats to inference. Using fractional differencing, multivariate models of British politics are developed in Chapter IV to compare competing theories regarding which subjective measure of economic evaluations best predicts support levels for the governing party; egocentric measures outperform sociotropic measures. The concept of fractional cointegration is discussed and the value of fractionally integrated error correction mechanisms are both discussed and demonstrated in models of Conservative party support. In Chapter V models of presidential approval in the United States are reconfigured in light of the possibilities of fractionally integrated variables. In both the British and American case accounting for the fractional character of all variables allows the development of more accurate multivariate models.

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Li, Dao. "Common Features in Vector Nonlinear Time Series Models." Doctoral thesis, Högskolan Dalarna, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:du-13253.

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This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.

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Andersson, Michael K. "On testing and forecasting in fractionally integrated time series models." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1998. http://www.hhs.se/efi/summary/467.htm.

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Brännström, Tomas. "Bias approximation and reduction in vector autoregressive models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1995. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-878.

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In the last few decades, vector autoregressive (VAR) models have gained tremendous popularity as an all-purpose tool in econometrics and other disciplines. Some of their most prominent uses are for forecasting, causality tests, tests of economic theories, hypothesis-seeking, data characterisation, innovation accounting, policy analysis, and cointegration analysis. Their popularity appears to be attributable to their flexibility relative to other models rather than to their virtues per se. In addition, analysts often use VAR models as benchmark models. VAR modeling has not gone uncriticised, though. A list of relevant arguments against VAR modelling can be found in Section 2.3 of this thesis. There is one additional problem which is rarely mentioned though, namely the often heavily biased estimates in VAR models. Although methods to reduce this bias have been available for quite some time, it has probably not been done before, at least not in any systematic way. The present thesis attempts to systematically examine the performance of bias-reduced VAR estimates, using two existing and one newly derived approximation to the bias. The thesis is orginanised as follows. After a short introductory chapter, a brief history of VAR modelling can be found in Chapter 2 together with a review of different representations and a compilation of criticisms against VAR models. Chapter 3 reports the results of very extensive Monte Carlo experiments serving dual purposes: Firstly, the simulations will reveal whether or not bias really poses a serious problem, because if it turns out that biases appear only by exception or are mainly insignificant, there would be little need to reduce the bias. Secondly, the same data as in Chapter 3 will be used in Chapter 4 to evaluate the bias approximations, allowing for direct comparison between bias-reduced and original estimates. Though Monte Carlo methods have been (rightfully) criticised for being too specific to allow for any generalisation, there seems to be no good alternative to analyse small-sample properties of complicated estimators such as these. Chapter 4 is in a sense the core of the thesis, containing evaluations of three bias approximations. The performance of the bias approximations is evaluated chiefly using single regression equations and 3D surfaces. The only truly new research result in this thesis can also be found in Chapter 4; a second-order approximation to the bias of the parameter matrix in a VAR(p) model. Its performance is compared with the performance of two existing first-order approximations, and all three are used to construct bias-reduced estimators, which are then evaluated. Chapter 5 holds an application of US money supply and inflation in order to find out whether the results in Chapter 4 can have any real impacts. Unfortunately though, bias reduction appears not to make any difference in this particular case. Chapter 6 concludes.<br>Diss. Stockholm : Handelshögsk.

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Custódio, Sandra Cristina Casquinha Gancho da Silva. "Curva de Phillips para Portugal : uma abordagem de cointegração." Master's thesis, Instituto Superior de Economia e Gestão, 1998. http://hdl.handle.net/10400.5/18802.

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Mestrado em Matemática Aplicada à Economia e à Gestão<br>A presente dissertação surge no âmbito da disciplina de Econometria I do curso de Mestrado em Matemática Aplicada à Economia e à Gestão, como proposta de um estudo de aplicação, e se possível desenvolvimento, de algumas técnicas econométricas leccionadas ao longo do curso. A este objectivo de carácter mais pedagógico juntou-se um genuíno interesse científico pelo tema: a cointegração é uma matéria "de ponta" no âmbito da econometria, tendo em si mesma um rigoroso suporte teórico em termos estatísticos, sendo naturalmente aplicável a diversas relações económicas. Neste contexto, é desenvolvido o tema das raízes unitárias e cointegração, propondo modelizações alternativas que passam pela consideração de modelos com mecanismo correctores do erro e análise em termos univariados e multivariados de algumas séries respeitantes à economia Portuguesa. Após a introdução, em termos da vertente económica, do tema referente ao eventual "trade-off' entre o produto-inflação em Portugal, inicia-se o estudo empírico das potenciais relações de cointegração. Pretendia-se inicialmente especificar uma Curva de Phillips para a economia Portuguesa, que fora já proposta por Carlos Robalo Marques, "Cointegration and the Output-Inflation trade-off': empirical evidence for Portugal" (1994, Banco de Portugal), como uma oportunidade de alargar conhecimentos e de aplicar técnicas econométricas com um desenvolvimento recente, no âmbito da modelização num contexto de integrabilidade. Em simultâneo, a perspectiva da teoria económica é de grande interesse, explicando a adesão ao tema em questão. Desta forma, era nosso objectivo, para além de dar continuidade temporal à análise de Marques (1994), fazer uma extensão da mesma pela introdução de novas variáveis, concretamente o grau de abertura da economia ao exterior, num contexto de cointegração.<br>This thesis is a proposal of a practical use of the econometric techniques applied to the economic science. It also surveys the cointegration theory in long-term equilibrium relationships and short-term model specifications. Specifically the study of unit roots and cointegration is developed by proposing altemative models, such as error-correction models, and by analysing series related to the Portuguese economy while looking at their univariate and multivariate properties. The first part of this paper is an introduction to the present economy in Portugal and the trade-off between product and inflation. Following is the empirical study of potential relationships of cointegration in the economy. As an opportunity to revise integrability, modelling methods, and to apply recently developed econometric techniques, a Phillips Curve pertinent to the Portuguese economy is specified, similar to the one proposed by Carlos Robalo Marques in "Cointegration and the Output-Inflation trade-off: empirical evidence for Portugal" (1994, Bank of Portugal). It is our goal then, to update and expand the Marques's analysis, always in the context of cointegration, by introducing new variables such as the degree of openness of the Portuguese economy. However, as will be shown by the parsimony principal, the inclusion of this new regressor does not change significantly the earlier results making it unnecessary and even counter-productive. As a result, the fluctuation of the real GDP will then be modelled with simple methods and using already existent variables. Due to the difficulties in obtaining sufficiently long and credible economic series, mainly those related to oil prices, the original goal of the thesis was affected. The data used carne ffom annually updated publications by the Bank of Portugal, National Institute of Statistics and the OECD. However, discrepancies in some of the series were found and these had to be re-evaluated. Due to eventual structural changes in the Portuguese economy, an analysis was done to the tests used; namely the ECM test which led to interesting results. The interpretation of this model should however be done with great care namely where the existence of cointegration between variables is concemed, since it may not be the most adequate due to its eventual lack of global stability as seen in the history of Portuguese economy,<br>info:eu-repo/semantics/publishedVersion

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Books on the topic "Cointegration models"

1

Gregory, Allan W. Testing for cointegration in linear quadratic models. Institute for Economic Research, Queen's University, 1991.

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Hashem, Pesaran M. Generalised impulse responseanalysis in linear multivariate models. University of Cambridge, Dept. of Applied Economics, 1997.

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Boswijk, H. Peter. Cointegration, identification, and exogeneity: Inference in structural error correction models. Thesis Publishers, 1992.

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P, Hargreaves Colin, ed. Nonstationary time series analysis and cointegration. Oxford University Press, 1994.

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Broersma, Lourens. A cointegration model for search equilibrium wage formation. International Monetary Fund, Policy Development and Review Dept., 2004.

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Robinson, P. M. Semiparametric frequency domain analysis of fractional cointegration. Suntory Centre, 1998.

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H, Baltagi Badi, ed. Nonstationary panels, panel cointegration, and dynamic panels. JAI, 2000.

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Haug, Alfred A. Tests for cointegration: a Monte Carlo comparison. York University, Department of Economics, 1993.

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Lob, Matthias. Kointegration und Granger-Kausalität: Empirische Analysen der tariflichen Einkommen in der Bundesrepublik Deutschland. Schulz-Kirchner, 1994.

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McAleer, Michael. Cointegration and direct tests of the rational expectations hypothesis. Department of Applied Economics, University of Cambridge, 1993.

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Book chapters on the topic "Cointegration models"

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Gujarati, Damodar. "Cointegration and Error Correction Models." In Econometrics. Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-37502-5_14.

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Asteriou, Dimitrios, and Stephen G. Hall. "Cointegration and Error-Correction Models." In Applied Econometrics. Macmillan Education UK, 2016. http://dx.doi.org/10.1057/978-1-137-41547-9_17.

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Dufrénot, Gilles, and Valérie Mignon. "Nonlinear Equilibration, Cointegration and NEC Models." In Recent Developments in Nonlinear Cointegration with Applications to Macroeconomics and Finance. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3615-1_4.

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Johansen, Søren. "Likelihood-based inference for cointegration of some nonstationary time series." In Time Series Models. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-2879-5_2.

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Fantazzini, Dean. "Fractionally Integrated Models for Volatility: A Review." In Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230295216_5.

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Krolzig, Hans-Martin. "Cointegration Analysis of VAR Models with Markovian Shifts in Regime." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-51684-9_14.

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Dufrénot, Gilles, and Valérie Mignon. "Asymmetric and Threshold Nonlinear Error-Correction Models." In Recent Developments in Nonlinear Cointegration with Applications to Macroeconomics and Finance. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3615-1_5.

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Dufrénot, Gilles, and Valérie Mignon. "Are the Unit-Root Tests Adequate for Nonlinear Models?" In Recent Developments in Nonlinear Cointegration with Applications to Macroeconomics and Finance. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3615-1_2.

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Berkowitz, Jeremy. "Valuing Equity when Discounted Cash Flows are Markov." In Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230295216_1.

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Guidolin, Massimo, and Federica Ria. "Markov Switching Mean-Variance Frontier Dynamics: Theory and International Evidence." In Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230295216_2.

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Conference papers on the topic "Cointegration models"

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Meng, Xianglei, Fubing Xia, Xue Xu, and Yuanjian Fu. "A Novel Nonstationary Process Monitoring Model Based on Cointegration Analysis." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065675.

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Michele, Mattiacci, Andrea Meoni, Antonella D'Alessandro, Branko Glisic, and Filippo Ubertini. "Strain-based damage detection using nonlinear cointegration theory: application to a masonry building model using smart bricks." In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2025, edited by Maria Pina Limongelli, Ching Tai Ng, and Didem Ozevin. SPIE, 2025. https://doi.org/10.1117/12.3050323.

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Chun Ping, Chang, and Lee Chien-Chiang. "Multivariate Panel Cointegration Models and Money Demand Function." In 9th Joint Conference on Information Sciences. Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.154.

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de Marcos, Rodrigo A., Javier Reneses, and Antonio Bello. "Long-term Spanish electricity market price forecasting with cointegration and VEC models." In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2016. http://dx.doi.org/10.1109/pmaps.2016.7764158.

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WORDEN, KEITH, and ELIZABETH J. CROSS. "ON ENGLE-GRANGER COINTEGRATION USING TREED GAUSSIAN PROCESSES." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/37058.

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Over the last decade or so, cointegration has emerged as arguably the state-of-the-art in terms of removing Environmental and Operational Variations (EOVs) from structural health monitoring (SHM) data. When data channels share common trends which can be removed by linear projection, the Johansen procedure, a maximum-likelihood approach developed within the field of econometrics, is provably optimal. Unfortunately, SHM problems can present where the trends occupy a nonlinear submanifold of the feature space, and in this case, linear cointegration/projection fails. It is still possible to make progress in this case by moving to the older Engle-Granger approach to cointegration, where one linearly regresses one of the feature space variables on the others; nonlinear cointegration is then ‘simply’ the application of an appropriate nonlinear regressor. Over the years, a number of nonlinear regression algorithms have been applied, motivated by machine learning or evolutionary computation; each with pros and cons. The aim of the current paper is to demonstrate an approach based on Treed Gaussian Processes (TGP); the advantage being that the algorithm allows switching between cointegration models in different parts of the feature space. Examination of the switching points can provide insight into the physical processes driving the nonlinearity. The approach is demonstrated here on the well-known Z24 Bridge data set, where the ambient temperature drives EOVs which cannot be removed by linear methods.

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Rubilar-Maturana, Claudina, Cesar Venegas-Pineda, Ana Maria Vallina-Hernandez, and Hanns De La Fuente-Mella. "Herd Behaviour in the Pension Fund Administrators in Chile: A Correlation and Cointegration Analysis." In 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO). IEEE, 2016. http://dx.doi.org/10.1109/smrlo.2016.92.

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Elmas, Bekir, and Ömer Esen. "Determining a Dynamic Relationship Between Stock Prices and Exchange Rates: An Empirical Study on Eurasia." In International Conference on Eurasian Economies. Eurasian Economists Association, 2010. http://dx.doi.org/10.36880/c01.00168.

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The stock price has a close relationship with some macroeconomic variables. As examples of the main macroeconomic variables can be shown that exchange rates, inflation, interest rate, growth rates. This paper empirically examined the relationship between the local stock market indexes and exchange rate (USD) in six Eurasian countries namely Turkey, Germany, France, Netherlands, Russia, France and India. The paper set out by testing existence of a long-term relationship between considered two variables using the Engle-Granger (1987), Johansen (1988, 1995) and Johansen-Juselius (1990) cointegration methods. Results of Engle- Granger cointegration test showed that there is no cointegration linkage between two variables under consideration. Furthermore, The Johansen cointegration test found that there is a long-term relationship between two variables (variables in the two countries). Under the VAR (Vector Autoregressive) and VEC (Vector Error Correction) models appllied the Granger causality test, revealed an unidirectional casual relationship between two variables in each of the six countries. In addition as regards the relationship While there is a unidirectional causal relationship running from exchange rate to stock market for four countries. However this relation is casual running from stock market to exchange rate for other two countries. According to the direction of the relationship these results that relationship between stock prices and exchange rate in four countries supports for the “Traditional Approach”. Furthermore, this relation also supports for the “Portfolio Approach” for other two countries.

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Ganiev, Junus, Tezcan Abasız, and Damira Baigonushova. "The Validity of the Endogenous Money Hypothesis in the Eurasian Economic Union Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2022. http://dx.doi.org/10.36880/c14.02609.

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The endogeneity of the money supply hypothesis is one of the most discussed and researched topics in the theoretical and empirical literature. According to this hypothesis, the money supply is determined internally within the economy. The aim of this study is to test this hypothesis in the member countries of the Eurasian Economic Union. In order to achieve the goal, the quarterly data of the five countries for the years 2010-2021 was analyzed by panel data analysis. First of all, bank loans were taken as the dependent variable and it was determined that there is a cointegration relationship between these variables. While all three independent variables affected the total loan amount in the short run, the coefficient of money supply was statistically insignificant in the long run. According to the results, one dollar increase in deposit amount increases the loan volume to $0.06 on average in the long run. A one-dollar increase in GDP increases it to 0.3 dollars. In addition, the panel ARDL cointegration test was applied by accepting the deposit amount, M2, M2X money supply and GDP variables as the dependent variables, but no cointegration relationship was found between the variables in all models. In the light of these results, it can be said that the money endogeneity hypothesis is not valid in the Eurasian Economic Union countries in general.

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Özbek, Gökhan Berk. "The Effect of Volatility Index on Turkish and European Stock Indices." In International Conference on Eurasian Economies. Eurasian Economists Association, 2023. http://dx.doi.org/10.36880/c15.02767.

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In times of high uncertainty, it is usual for investors to be cautious about the capital markets and turn to investment instruments that are considered a security blanket. This situation may adversely affect the capital markets and the real sector. In this context, it is aimed to investigate the effect of the Volatility Index (VIX), also known as Fear Index, on some European stock markets. In the study, five different models were created in which the independent variable is VIX and the dependent variables are FTSE 100, DAX, CAC 40, BIST 30 and BIST Participation 30. Including the BIST Participation 30 Index in the study; it is also desired to determine whether there is a difference in the context of the conventional index-Islamic Index. In the study, a weekly data set including 76 observations was used in the period of 12.11.2021-28.04.2023. Data were obtained from Refinitiv Eikon. The long-term relationship of the variables was examined with the Engle-Granger Cointegration Test. Cointegration relationships were determined between VIX and FTSE 100; it was specified that there is no cointegrated relationships between VIX and BIST 30, BIST Participation 30, DAX and CAC 40. Fully Modified Ordinary Least Squares (FMOLS) method was used to estimate the coefficients of the relationship between the variables whose cointegration relationship was determined. Consistent with the literature, it was determined that VIX had negative effect on the FTSE 100. In this context, investors can use the VIX indicator to provide foresight in their investments in the FTSE 100.

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Yılmaz, Fatih, Onur Şeker, and Eren Pektaş. "Testing The Validity of The Phillips Curve for Turkey With Vector Autoregressive and Markov Switching Models on The Basis of Inflation and Unemployment." In International Conference on Eurasian Economies. Eurasian Economists Association, 2019. http://dx.doi.org/10.36880/c11.02349.

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In this study, we tested the validity of the Phillips Curve for Turkey. We used Markov Switching Model for examine the relationship between two variables in different regime periods, Engle Granger Causality Test for detect the causality between two variables, Johansen Cointegration Test for observe the long term equilibrium relationship and The Impulse Response Analysis and Variance Decomposition Analysis for investigate the explanatory effect of two variables on each other. As a result of the analysis, it was determined that Inflation and Unemployment act together in the short and long term. Between 2010M01 and 2017M10, it was determined that the Phillips Curve is ineffective for Turkey.

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Reports on the topic "Cointegration models"

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Campbell, John, and Robert Shiller. Cointegration and Tests of Present Value Models. National Bureau of Economic Research, 1986. http://dx.doi.org/10.3386/w1885.

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Cheung, Yin-Wong, and Menzie Chinn. Integration, Cointegration and the Forecast Consistency of Structural Exchange Rate Models. National Bureau of Economic Research, 1997. http://dx.doi.org/10.3386/w5943.

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Khadan, Jeetendra. Tax Buoyancy in the Caribbean: Evidence from Heterogenous Panel Cointegration Models. Inter-American Development Bank, 2020. http://dx.doi.org/10.18235/0002138.

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Brown, Scott, N. Edward Coulson, and Robert Engle. Non-Cointegration and Econometric Evaluation of Models of Regional Shift and Share. National Bureau of Economic Research, 1990. http://dx.doi.org/10.3386/w3291.

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Kraujalienė, Lidija, Atif Yaseen, and Inga Bilinskienė. Effects of Natural Resources and Renewable Energy Consumption on Carbon Dioxide Emmisions in the Country Economics. Vilnius Business College, 2024. https://doi.org/10.57005/ab.2024.3.5.

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Climate change is a highly debated issue among policymakers and stakeholders because it catalyzes numerous other problems. Similarly, natural resources are a blessing for any country’s economic development, but sometimes, this blessing can become a source of many problems. The research rigorously employs the Autoregressive Distributed Lag (ARDL) model, a widely accepted econometric tool for analyzing long-run relationships, and aims to investigate the impact of natural resources, renewable energy consumption, and agricultural activities on carbon emissions, considering economic growth in Russia for 30 years period. The ARDL model has evaluated that natural resources and agrarian activities significantly positively affect carbon emissions due to economic growth, while renewable energy hurts carbon emissions in Russia. This research uses a quantitative approach and relies on secondary data. Furthermore, robustness checks using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR) confirmed the primary outcomes of the ARDL model. Diagnostic tests (CUSUM and CUSUMSQ) have shown the model's stability, while the multicollinearity test (VIF) has highlighted the absence of multicollinearity. The research findings have confirmed that Russia’s resources and agriculture harm the environment, while renewable energy offers a beacon of hope, promoting sustainability and economic growth. This research, with its recommendations for reducing carbon emissions in developed countries, offers a path towards a more sustainable future, inspiring optimism and hope.

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Mehbub Anwar, Ahm, Nourah Al-Hosain, and Yagyavalk Bhatt. Analyzing the Interplay of Urbanization, Economic Development, and Seaborne Trade A Case of Saudi Arabia. King Abdullah Petroleum Studies and Research Center, 2024. https://doi.org/10.30573/ks--2024-dp62.

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Urbanization is widely recognized as a critical factor influencing economic growth and global trade, yet there is ongoing debate about whether it drives these outcomes or is a consequence of them. To address this, it is essential to determine whether urbanization spurs economic development and trade, or if these processes influence urbanization, or if the relationship is one of mutual causality. This study investigates the interplay between urbanization, economic development, and trade in both the short and the long term. Using data from Saudi Arabia spanning from 1991 to 2022, the research employs cointegration and Granger causality tests to first determine the order of integration of the variables, and it then applies the Autoregressive Distributed Lag (ARDL) model and the Error Correction Model (ECM) to examine causal relationships over different time horizons. The results reveal a bidirectional causality between urbanization and economic development in both the short and the long terms. In contrast, while there is bidirectional causality between trade and economic development in the short term, the long-term analysis indicates a unidirectional causality from trade to economic development. This suggests that trade influences economic development, which in turn affects urbanization, with no direct causality found between trade and urbanization.

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