Multiple Time Series Models, Issue 148
Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models.
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Introduction to Multiple Time Series Models
Basic Vector Autoregression Models
Examples of VAR Analyses
3 other sections not shown
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AECTR analyze approach ARIMA models Cholesky decomposition cointegrating relationships cointegration computed contemporaneous relationships covariance matrix decomposition econometrics economic ECTR endogenous variables error bands error correction models error covariance estimate evaluate exogeneity tests exogenous forecast error variance functions Granger causality Granger causality tests hypothesis tests identification assumptions impacts Impulse Response Analysis impulse responses inferences innovation accounting interpretation lag length lagged values likelihood ratio test long-run Lutkepohl Macropartisanship and Public methods Mood and Macropartisanship moving average multiple time series nonstationary null hypothesis P-value parameters past values predict present programs public mood real investment reduced form regression model residual covariance restrictions sample SEQ models serial correlation series analysis series data series models Sims simultaneous equation models specification stationary system of equations Table test statistics Tests for Lag theory tion trends typically uncorrelated unit root univariate VECM vector autoregression VMA representation Williams and Collins x2 test zero