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  2. SETAR (model) - Wikipedia

    en.wikipedia.org/wiki/SETAR_(model)

    SETAR models were introduced by Howell Tong in 1977 and more fully developed in the seminal paper (Tong and Lim, 1980). They can be thought of in terms of extension of autoregressive models, allowing for changes in the model parameters according to the value of weakly exogenous threshold variable z t, assumed to be past values of y, e.g. y t-d, where d is the delay parameter, triggering the ...

  3. 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]

  4. Autoregressive model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_model

    The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence relation) which should not be confused with a differential equation.

  5. Autoregressive conditional heteroskedasticity - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_conditional...

    In 2004, Claudia Klüppelberg, Alexander Lindner and Ross Maller proposed a continuous-time generalization of the discrete-time GARCH(1,1) process.The idea is to start with the GARCH(1,1) model equations

  6. Box–Jenkins method - Wikipedia

    en.wikipedia.org/wiki/Box–Jenkins_method

    Model identification and model selection: making sure that the variables are stationary, identifying seasonality in the dependent series (seasonally differencing it if necessary), and using plots of the autocorrelation (ACF) and partial autocorrelation (PACF) functions of the dependent time series to decide which (if any) autoregressive or ...

  7. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    These models and extensions to include moving average spline models are described in "Univariate Time Series Modelling and Forecasting using TSMARS: A study of threshold time series autoregressive, seasonal and moving average models using TSMARS". Bayesian MARS (BMARS) uses the same model form, but builds the model using a Bayesian approach. It ...

  8. Threshold model - Wikipedia

    en.wikipedia.org/wiki/Threshold_model

    A threshold model used in toxicology posits that anything above a certain dose of a toxin is dangerous, and anything below it safe. This model is usually applied to non-carcinogenic health hazards. Edward J. Calabrese and Linda A. Baldwin wrote: The threshold dose-response model is widely viewed as the most dominant model in toxicology. [6]

  9. Generalized additive model for location, scale and shape

    en.wikipedia.org/wiki/Generalized_additive_model...

    The first two population distribution parameters and are usually characterized as location and scale parameters, while the remaining parameter(s), if any, are characterized as shape parameters, e.g. skewness and kurtosis parameters, although the model may be applied more generally to the parameters of any population distribution with up to four ...