Multivariate Tests for Time Series Models, Issue 100
SAGE, 1994 - Social sciences - 98 pages
Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests.
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accept the alternative ADF test alternative hypothesis AMACRO bivariate BVAR Calculate the test causality tests cause x(t chi-square distribution coefficients cointegrating regression correlation CRDW critical values cross-correlation dependent variable differencing distribution Engle and Granger error correction model estimated exist F statistics F test forecast accuracy forecast error Geweke Granger and Newbold Granger causality Granger-cause MIS(r Granger-cause PA(r impulse responses independent instantaneous causality joint stationarity Kurtosis Labys lag length linear dependence matrix MicroTSP MIS(f MIS(r model order model specification moving average multivariate time series nonlinear nonstationary normality null hypothesis number of lags number of observations PA(f performed Portmanteau test predictions residuals restricted and unrestricted series models Specify the hypotheses stationary test equations Test Procedure Step test statistic test the null testing for cointegration unit root univariate University of Montpellier unrestricted equation variance decomposition VARMA Vector Autoregressive vector time series vector X(t West Virginia University white noise zero
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