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  2. Vector autoregression - Wikipedia

    en.wikipedia.org/wiki/Vector_autoregression

    A VAR with p lags can always be equivalently rewritten as a VAR with only one lag by appropriately redefining the dependent variable. The transformation amounts to stacking the lags of the VAR(p) variable in the new VAR(1) dependent variable and appending identities to complete the precise number of equations. For example, the VAR(2) model

  3. Bayesian vector autoregression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_vector_autoregression

    In statistics and econometrics, Bayesian vector autoregression (BVAR) uses Bayesian methods to estimate a vector autoregression (VAR) model. BVAR differs with standard VAR models in that the model parameters are treated as random variables, with prior probabilities, rather than fixed values.

  4. Variance decomposition of forecast errors - Wikipedia

    en.wikipedia.org/wiki/Variance_decomposition_of...

    Download as PDF; Printable version; In other projects Wikidata item; Appearance. ... This can be changed to a VAR(1) structure by writing it in companion form (see ...

  5. Autoregressive model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_model

    The impulse response of a system is the change in an evolving variable in response to a change in the value of a shock term k periods earlier, as a function of k. Since the AR model is a special case of the vector autoregressive model, the computation of the impulse response in vector autoregression#impulse response applies here.

  6. Chow test - Wikipedia

    en.wikipedia.org/wiki/Chow_test

    Applications of the Chow test Structural break (slopes differ) Program evaluation (intercepts differ) At = there is a structural break; separate regressions on the subintervals [,] and [,] delivers a better model than the combined regression (dashed) over the whole interval.

  7. Bayesian structural time series - Wikipedia

    en.wikipedia.org/wiki/Bayesian_structural_time...

    The technique for time series decomposition. In this step, a researcher can add different state variables: trend, seasonality, regression, and others. Spike-and-slab method. In this step, the most important regression predictors are selected. Bayesian model averaging. Combining the results and prediction calculation.

  8. Structural break - Wikipedia

    en.wikipedia.org/wiki/Structural_break

    There are many statistical packages that can be used to find structural breaks, including R, [17] GAUSS, and Stata, among others. For example, a list of R packages for time series data is summarized at the changepoint detection section of the Time Series Analysis Task View, [18] including both classical and Bayesian methods. [19] [9]

  9. SmartPLS - Wikipedia

    en.wikipedia.org/wiki/SmartPLS

    The Newest addition is the SmartPLS4. The software released to the general public in 2022 is an easy to use tool for Structural Equation Modelling. To estimate the model in SmartPLS, the model has to be estimated at two levels that include the measurement model assessment and structural model assessment.