Multivariate Tests for Time Series Models, Issue 100
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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accuracy alternative hypothesis analysis application approach associated assumed assumption calculated causality causality tests cause Chapter coefficients cointegration Compare condition Constant correlation critical values CROMWELL cross-correlation decompositions defined dependent determining developed Dickey discussed distribution Econometrics Economics employed Engle equations error error correction estimated evaluating examining example exist forecast Fuller function Geweke given Granger Granger-cause HANNAN important impulse responses independent instantaneous integrated interval joint Journal lags linear feedback matrix mean measure method MIS(t moving average multivariate normality Note null hypothesis observations obtain PA(t performed period predictions presented referred Regional relationship representation Research residuals respectively restricted Review sample Science series models shock significance level Sims specification standard stationarity Step suggest Table Test Procedure Step test statistic transformations true unit root univariate University variables variance vector Vector Autoregressive West Virginia white noise zero
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