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  2. Vector autoregression - Wikipedia

    en.wikipedia.org/wiki/Vector_autoregression

    Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. VAR is a type of stochastic process model. VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series .

  3. Autoregressive model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_model

    Together with the moving-average (MA) model, it is a special case and key component of the more general autoregressive–moving-average (ARMA) and autoregressive integrated moving average (ARIMA) models of time series, which have a more complicated stochastic structure; it is also a special case of the vector autoregressive model (VAR), which ...

  4. Bayesian vector autoregression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_vector_autoregression

    This type model can be estimated with Eviews, Stata, Python [8] or R [9] Statistical Packages. Recent research has shown that Bayesian vector autoregression is an appropriate tool for modelling large data sets. [10]

  5. Variance decomposition of forecast errors - Wikipedia

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

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  6. Autoregressive moving-average model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_moving...

    The notation ARMAX(p, q, b) refers to a model with p autoregressive terms, q moving average terms and b exogenous inputs terms. The last term is a linear combination of the last b terms of a known and external time series d t {\displaystyle d_{t}} .

  7. Structural equation modeling - Wikipedia

    en.wikipedia.org/wiki/Structural_equation_modeling

    Overall, the contributions that can be made by structural equation modeling depend on careful and detailed model assessment, even if a failing model happens to be the best available. An additional controversy that touched the fringes of the previous controversies awaits ignition.

  8. Error correction model - Wikipedia

    en.wikipedia.org/wiki/Error_correction_model

    Forecasts from such a model will still reflect cycles and seasonality that are present in the data. However, any information about long-run adjustments that the data in levels may contain is omitted and longer term forecasts will be unreliable. This led Sargan (1964) to develop the ECM methodology, which retains the level information. [4] [5]

  9. Johansen test - Wikipedia

    en.wikipedia.org/wiki/Johansen_test

    In statistics, the Johansen test, [1] named after Søren Johansen, is a procedure for testing cointegration of several, say k, I(1) time series. [2] This test permits more than one cointegrating relationship so is more generally applicable than the Engle-Granger test which is based on the Dickey–Fuller (or the augmented) test for unit roots in the residuals from a single (estimated ...