Có 40+ tài liệu thuộc chủ đề "Hướng dẫn sử dụng SAS / ETS"
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computationally expensive, one of the (dual) quasi-Newton or conjugate gradient algorithms may be more efficient.. Newton-Raphson Optimization with Line Search (NEWRAP). and the Hessian matrix H. thus, it requires that the objective function have continuous first- and second-order derivatives inside the feasible region. If second-order derivatives are computed efficiently and precisely, the NEWRAP method can perform well for medium-sized to...
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The ARIMA Procedure. Overview: ARIMA Procedure. Getting Started: ARIMA Procedure. Using ARIMA Procedure Statements. Subset, Seasonal, and Factored ARMA Models. Intervention Models and Interrupted Time Series. Syntax: ARIMA Procedure. IDENTIFY Statement. ESTIMATE Statement. Details: ARIMA Procedure. The Inverse Autocorrelation Function. The Partial Autocorrelation Function. 194 F Chapter 7: The ARIMA Procedure. Examples: ARIMA Procedure. Example 7.2: Seasonal Model for the...
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Figure 7.8 Parameter Estimates for AR(1) Model. The ARIMA Procedure. The table of parameter estimates lists the parameters in the model. for each parameter, the table shows the estimated value and the standard error and t value for the estimate. The table also indicates the lag at which the parameter appears in the model.. In this case, there are two...
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X i;t is the ith input time series or a difference of the ith input series at time t k i is the pure time delay for the effect of the ith input series. i .B/ is the numerator polynomial of the transfer function for the ith input series ı i .B/ is the denominator polynomial of the transfer function...
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222 F Chapter 7: The ARIMA Procedure. The numerator factors for a transfer function for an input series are like the MA part of the ARMA model for the noise series.. The denominator factors for a transfer function for an input series are like the AR part of the ARMA model for the noise series. in the INPUT= option. For...
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If differencing is specified for a variable in the CROSSCORR=. DATA=SAS-data-set. specifies the input SAS data set that contains the time series. If the DATA= option is omitted, the DATA= data set specified in the PROC ARIMA statement is used. if the DATA= option is omitted from the PROC ARIMA statement as well, the most recently created data set is...
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242 F Chapter 7: The ARIMA Procedure. If the INTERVAL= option is not used, the last input value of the ID= variable is incremented by one for each forecast period to extrapolate the ID values for forecast observations.. See Chapter 4, “Date Intervals, Formats, and Functions,” for information about valid INTERVAL= values.. The value of the INTERVAL= option is used...
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Marquardt’s method is used for the nonlinear least squares iterations. Numerical approximations of the derivatives of the sum-of-squares function are taken by using a fixed delta (controlled by the DELTA= option).. The methods do not always converge successfully for a given set of data, particularly if the starting values for the parameters are not close to the least squares estimates.....
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x t 1 / and V t is the covariance matrix of the vector .x 1. Finite memory forecasts minimize the mean squared error of prediction if the parameters of the ARMA model are known exactly. (In most cases, the parameters of the ARMA model are estimated, so the predictors are not true best linear forecasts.). If the response series...
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272 F Chapter 7: The ARIMA Procedure. option is specified in the ESTIMATE statement. The OUTSTAT data set contains the following:. _MODLABEL_, a character variable that contains the model label, if it is provided by using the label option in the ESTIMATE statement (otherwise this variable is not created).. _TYPE_, a character variable that contains the estimation method used. _STAT_,...
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282 F Chapter 7: The ARIMA Procedure. Output 7.1.1 Correlation Analysis from the First IDENTIFY Statement. The results of the second IDENTIFY statement are shown in Output 7.1.2. Output 7.1.2 Correlation Analysis from the Second IDENTIFY Statement. O 2 D 0:82:) Moreover, the graphical analysis of the residuals shows no model inadequacies (see Output 7.1.4 and Output 7.1.5).. The ESTIMATE...
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Output 7.2.8 Plot of the Forecast for the Original Series. Example 7.3: Model for Series J Data from Box and Jenkins. First, the input series X is modeled with a univariate ARMA model. Next, the dependent series Y is cross-correlated with the input series. Next, a transfer function model is fit with no structure on the noise term. then, the...
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identify var=ozone(12). The ESTIMATE statement results are shown in Output 7.4.1 and Output 7.4.2.. Output 7.4.1 Parameter Estimates. MA lt;.0001 1 ozone 0. MA lt;.0001 12 ozone 0. NUM lt;.0001 0 x1 0. NUM lt;.0001 0 summer 0. Output 7.4.2 Model Summary. Output 7.4.2 continued. The FORECAST statement results are shown in Output 7.4.3.. Output 7.4.3 Forecasts. The following SAS...
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(1971), The Statistical Analysis of Time Series, New York: John Wiley &. Andrews and Herzberg (1985), A Collection of Problems from Many Fields for the Student and Research Worker, New York: Springer–Verlag.. (1979), “An Algorithm for the Exact Likelihood of a Mixed Autoregressive Moving- Average Process,” Biometrika, 66, 59.. (1980), “Finite Sample Properties of Estimators for Autoregressive Moving-Average Models,” Journal...
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Figure 8.1 Autocorrelated Time Series. However, the simple time trend model is convenient for illustrating regression with autocorrelated errors, and the series Y shown in Figure 8.1 is used in the following introductory examples.. To use the AUTOREG procedure, specify the input data set in the PROC AUTOREG statement and specify the regression model in a MODEL statement. The following...
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332 F Chapter 8: The AUTOREG Procedure. Figure 8.8 continued. ylag lt;.0001. Stepwise Autoregression. Once you determine that autocorrelation correction is needed, you must select the order of the autoregressive error model to use. One way to select the order of the autoregressive error model is stepwise autoregression. To use stepwise autoregression, specify the BACKSTEP option, and specify a large...
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Using the TYPE= option along with the GARCH= option enables you to control the constraints placed on the estimated GARCH parameters. You can also use the TYPE= option to specify the exponential form of the GARCH model, called the EGARCH model, or other types of GARCH models, namely the quadratic GARCH (QGARCH), threshold GARCH (TGARCH), and power GARCH (PGARCH) models....
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specifies the parameter to determine the radius for BDS test. The BDS test sets up the radius as r D D , where is the standard deviation of the time series to be tested.. specifies the way to calculate the p-values. specifies the type of the time series (residuals) to be tested. The values of the Z=. Y specifies the...
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The values of the METHOD= option are as follows:. requests the estimation to the first contiguous sequence of data with no missing values.. specifies the optimization technique when the GARCH or heteroscedasticity model is estimated.. HETERO Statement. The HETERO statement specifies variables that are related to the heteroscedasticity of the residuals and the way these variables are used to model...
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The unconditional sum of squares for the model, S, is S D n 0 V 1 n D e 0 e. The full log likelihood function for the autoregressive error model is. For the ML method, the likelihood function is maximized by minimizing an equivalent sum-of-squares function.. This is the case for very regular data sets, such as an exact...