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Explaining Cointegration Analysis Part Ii Ideas Repec

Leo Migdal
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explaining cointegration analysis part ii ideas repec

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Academia.edu no longer supports Internet Explorer. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. 2000, RePEc: Research Papers in Economics We describe the concept of cointegration, its implications in modelling and forecasting, and discuss inference procedures appropriate in integrated-cointegrated vector autoregressive processes (VARs). Particular attention is paid to the properties of VARs, to the modelling of deterministic terms, and to the determination of the number of cointegration vectors. The analysis is illustrated by empirical examples.

considers the key role of deterministic terms (like constants and trends) in cointegration analyses. At that stage, the formalization of the model and analysis of its properties are complete, so we turn to issues of estimation (section 7) and inference (section 8), illustrated empirically in section 9. Section 10 considers the identification of the cointegration parameters, and hypothesis tests thereon, and section 11 discusses issues that arise in the analysis of partial systems (conditional on a subset of the variables) and... Finally, we discuss forecasting in cointegrated systems (section 12) and the associated topic of parameter constancy (section 13), also relevant to any policy applications of cointegrated systems. Section 14 concludes. The paper uses matrix algebra extensively to explain the main ideas, so we adopt the following notation: bold-face capital letters for matrices, boldface lower case for vectors, normal case for variables and coefficients, and...

We generally assume all variables are in logs, which transformation produces more homogeneous series for inherently-positive variables (see e.g., Hendry, 1995a, ch. 2), but we will not distinguish explicitly between logs and the original units. 2 Cointegration analysis is inherently multivariate, as a single time series cannot be cointegrated. Consequently, consider a set of integrated variables, such as gasoline prices at different locations as in , where each individual gasoline price (denoted p i,t ) is I(1), but follows a common long-run path,... Cointegration between the gasoline prices could arise, for example, if the price differentials between any two locations were stationary. However, cointegration as such does not say anything about the direction of causality.

For example, one of the locations could be a price leader and the others price followers; or, alternatively, none of the locations might be more important than the others. In the first case, the price of the leading location would be driving the prices of the other locations (be 'exogenous' to the other prices) and cointegration could be analyzed from the equations for... In the second case, all prices would be 'equilibrium adjusting' and, hence, all equations would contain information about the cointegration relationships. In the bivariate analysis in , cointegration was found in a single-equation model of p 1,t given p 2,t , thereby assuming that p 2,t was a price leader. If this assumption was incorrect, then the estimates of the cointegration relation would be inefficient, and could be seriously biased. To find out which variables adjust, and which do not adjust, to the long-run cointegration relations, an analysis of the full system of equations is required, as illustrated in Section 11.

Here, we will focus on a vector autoregression (VAR) as a description of the system to be investigated. In a VAR, each variable is 'explained' by its own lagged values, and the lagged values of all other variables in the system. To see which questions can be asked within a cointegrated VAR, we postulate a trivariate VAR model for the two gasoline prices p 1,t and p 2,t , together with the price of crude... We restrict the analysis to one lagged change for simplicity, and allow for 2 cointegration relations. Oxford Bulletin of Economics and Statistics, 1996 Research output: Working paper › Research

Research output: Working paper › Research PB - Department of Economics, University of Copenhagen doi: 10.5547/issn0195-6574-ej-vol22-no1-4 doi: 10.5547/issn0195-6574-ej-vol22-no1-4 We describe the concept of cointegration, its implications in modelling and forecasting, and discuss inference procedures appropriate in integrated-cointegrated vector autoregressive processes (VARs). Particular attention is paid to the properties of VARs, to the modelling of deterministic terms, and to the determination of the number of cointegration vectors.

The analysis is illustrated by empirical examples. VAR; Deterministic Components; Rank Determination; Gasoline Prices, jel: jel:C51, jel: jel:C32, jel: jel:E31 VAR; Deterministic Components; Rank Determination; Gasoline Prices, jel: jel:C51, jel: jel:C32, jel: jel:E31 Working Papers Journal Articles Books and Chapters Software Components EconPapers FAQ Archive maintainers FAQ Cookies at EconPapers The RePEc blog The RePEc plagiarism page

No 00-20, Discussion Papers from University of Copenhagen. Department of Economics Abstract: We describe the concept of cointegration, its implications in modelling and forecasting, and discuss inference procedures appropriate in integrated-cointegrated vector autoregressive processes (VARs). Particular attention is paid to the properties of VARs, to the modelling of deterministic terms, and to the determination of the number of cointegration vectors. The analysis is illustrated by empirical examples.

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All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:enejou:v:22:y:2001:i:1:p:75-120. See general information about how to correct material in RePEc. If you have authored this item and are not yet registered with RePEc, we encourage you to do it her...

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Academia.edu No Longer Supports Internet Explorer. To Browse Academia.edu And

Academia.edu no longer supports Internet Explorer. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. 2000, RePEc: Research Papers in Economics We describe the concept of cointegration, its implications in modelling and forecasting, and discuss inference procedures appropriate in integrated-cointegrated vector autoregressive p...

Considers The Key Role Of Deterministic Terms (like Constants And

considers the key role of deterministic terms (like constants and trends) in cointegration analyses. At that stage, the formalization of the model and analysis of its properties are complete, so we turn to issues of estimation (section 7) and inference (section 8), illustrated empirically in section 9. Section 10 considers the identification of the cointegration parameters, and hypothesis tests th...

We Generally Assume All Variables Are In Logs, Which Transformation

We generally assume all variables are in logs, which transformation produces more homogeneous series for inherently-positive variables (see e.g., Hendry, 1995a, ch. 2), but we will not distinguish explicitly between logs and the original units. 2 Cointegration analysis is inherently multivariate, as a single time series cannot be cointegrated. Consequently, consider a set of integrated variables, ...