TY - JOUR
T1 - Chapter 47 Vector autoregressions and cointegration
AU - Watson, Mark W.
N1 - Funding Information:
2.1. Introductory comments 2.2. An example 2.3. A useful lemma 2.4. Continuing with the example 2.5. A general framework 2.6. Applications 2.7. Implications for econometric practice 3. Cointegrated systems 3.l. Introductory comments 3.21 Representations for the I(1) cointegrated model 3.3. Testing for cointegration in I(1) systems 3.4. Estimating cointegrating vectors 3.5. The role of constants and trends 4. Structural vector autoregressions 4.1. Introductory comments 4.2. The structural moving average model, impulse response functions and variance decompositions 4.3. The structural VAR representation 4.4. Identification of the structural VAR 4.5. Estimating structural VAR models References *The paper has benefited from comments by Edwin Denson, Rob Engle, Neil Ericsson, Michael Horvath, Soren Johansen, Peter Phillips, Greg Reinsel, James Stock and students at Northwestern University and Studienzentrum Gerzensee. Support was provided by the National Science Foundation through grants SES-89-10601 and SES-91-22463.
PY - 1994
Y1 - 1994
N2 - This paper surveys three topics: vector autoregressive (VAR) models with integrated regressors, cointegration, and structural VAR modeling. The paper begins by developing methods to study potential "unit root" problems in multivariate models, and then presents a simple set of rules designed to help applied researchers conduct inference in VARs. A large number of examples are studied, including tests for Granger causality, tests for VAR lag length, spurious regressions and OLS estimators of cointegrating vectors. The survey of cointegration begins with four alternative representations of cointegrated systems: the vector error correction model (VECM), and the moving average, common trends and triangular representations. A variety of tests for cointegration and efficient estimators for cointegrating vectors are developed and compared. Finally, structural VAR modeling is surveyed, with an emphasis on interpretation, econometric identification and construction of efficient estimators. Each section of this survey is largely self-contained. Inference in VARs with integrated regressors is covered in Section 2, cointegration is surveyed in Section 3, and structural VAR modeling is the subject of Section 4.
AB - This paper surveys three topics: vector autoregressive (VAR) models with integrated regressors, cointegration, and structural VAR modeling. The paper begins by developing methods to study potential "unit root" problems in multivariate models, and then presents a simple set of rules designed to help applied researchers conduct inference in VARs. A large number of examples are studied, including tests for Granger causality, tests for VAR lag length, spurious regressions and OLS estimators of cointegrating vectors. The survey of cointegration begins with four alternative representations of cointegrated systems: the vector error correction model (VECM), and the moving average, common trends and triangular representations. A variety of tests for cointegration and efficient estimators for cointegrating vectors are developed and compared. Finally, structural VAR modeling is surveyed, with an emphasis on interpretation, econometric identification and construction of efficient estimators. Each section of this survey is largely self-contained. Inference in VARs with integrated regressors is covered in Section 2, cointegration is surveyed in Section 3, and structural VAR modeling is the subject of Section 4.
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U2 - 10.1016/S1573-4412(05)80016-9
DO - 10.1016/S1573-4412(05)80016-9
M3 - Review article
AN - SCOPUS:70350105389
SN - 1573-4412
VL - 4
SP - 2843
EP - 2915
JO - Handbook of Econometrics
JF - Handbook of Econometrics
ER -