Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jayaram, Amritha; John, Nimitha Title A name given to the resource Bivariate Cointegrated Model with Gamma Innovations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Mathematics and Computer Science for Real-World Applications;pp.145-162 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394275359.ch9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394275359.ch9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013698301?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013698301?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jayaram A., Department of Statistics and Data Science, Christ (Deemed to be University), Karnataka, Bangalore, India; John N., Department of Statistics and Data Science, Christ (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource The nature of time-bound data is its non-stationarity, that is, the constant presence of factors such as trend, seasonality, or both. Adopting mechanisms such as the method of differencing or ordinary least squares results in a loss of information or overestimation or underestimation of the parameters, respectively. A cointegration study reflects the notion of a long-run equilibrium, which is a concept of sensitivity in macroecometrics. Thus, cointegration can be defined as the onset of a longterm equilibrium between two or more time series that evolve under the influence of time, with the potential advantage of establishing a dynamic relationship using standard methods. Thus, this study explores the theoretical approach of estimating an error correction model for a cointegrated bivariate VAR (2) model with gamma innovation. To obtain the parameter estimates of the proposed model, we employ the conditional maximum likelihood estimation, implemented through the NewtonRaphson algorithm, because of the gamma distributions non-closed form nature. A theoretical study is strengthened by artificial simulations that support mathematical derivations. 2025 Scrivener Publishing LLC. Subject The topic of the resource Bivariate co-integration; Error correction model; Gamma innovation; Time series; Vector autoregressive model Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139427535-9; 978-139427532-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online