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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. Autoregressive moving-average model - Wikipedia

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

    [3] The augmented Dickey–Fuller test assesses the stability of IMF and trend components. For stationary time series, the ARMA model is used, while for non-stationary series, LSTM models are used to derive abstract features. The final value is obtained by reconstructing the predicted outcomes of each time series.

  4. 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 ...

  5. Autoregressive integrated moving average - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_integrated...

    Stata includes ARIMA modelling (using its arima command) as of Stata 9. StatSim: includes ARIMA models in the Forecast web app. Teradata Vantage has the ARIMA function as part of its machine learning engine. TOL (Time Oriented Language) is designed to model ARIMA models (including SARIMA, ARIMAX and DSARIMAX variants) .

  6. 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.

  7. Partial least squares path modeling - Wikipedia

    en.wikipedia.org/wiki/Partial_least_squares_path...

    The structural model represents the relationships between the latent variables. An iterative algorithm solves the structural equation model by estimating the latent variables by using the measurement and structural model in alternating steps, hence the procedure's name, partial. The measurement model estimates the latent variables as a weighted ...

  8. Structural estimation - Wikipedia

    en.wikipedia.org/wiki/Structural_estimation

    Different combinations of structural parameters can imply the same reduced-form parameters, so structural estimation must go beyond the direct relationship between variables. [3] [5] Many economists now use the term "reduced form" to mean statistical estimation without reference to a specific economic model.

  9. 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.