Có 140+ tài liệu thuộc chủ đề "Hướng dẫn sử dụng SAS / ETS"
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Figure 18.75 Solve Step Summary Output. The MODEL Procedure Model Summary Model Variables 1. Number of Statements 1 Program Lag Length 1 Model Variables LHUR. The second page of output, shown in Figure 18.76, gives more information on the failed observation.. Figure 18.76 Solve Step Error Message. The MODEL Procedure. Dynamic Single-Equation Forecast. NOTE: Additional information on the values of...
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The convergence properties of the Jacobi and Seidel solution methods remain significantly poorer than the default Newton’s method.. Both the Newton’s method and the Jacobi method are order-invariant in the sense that the order in which equations are specified in the model program has no effect on the operation of the iterative solution process. In order-invariant methods, the values of...
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1202 F Chapter 18: The MODEL Procedure. Equation variable names can appear in parts of the PROC MODEL printed output, and they can be used in the model program. The meaning of these prefixes is detailed in the section “Equation Translations” on page 1204.. Parameters can be given values or can be estimated by fitting the model to data. If...
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The lag length is infinite, and PROC MODEL prints an error message and stops. The following equation is valid and results in a lag length for the Y equation equal to the lag length of YHAT:. Initially, the lags of RESID.Y are missing, and the ZLAG function replaces the missing residuals with 0s, their unconditional expected values.. ZLAG0(x ) returns...
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(1) arg=PRED.y argsave=y. PRED.y <- _temp2 _temp4 Oper eeocf at . @PRED.y/@a <- 1 Oper * at . @PRED.y/@b <- @1dt1_2 @1dt1_6 Oper * at . @PRED.y/@c <- @1dt1_8 @1dt1_13 5 Stmt Assign line 3934 column. (1) arg=RESID.y argsave=y. RESID.y <- PRED.y ACTUAL.y Oper eeocf at . @RESID.y/@a <- @PRED.y/@a Oper = at . @RESID.y/@b <- @PRED.y/@b Oper = at...
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The plot is shown in Output 18.1.4.. Output 18.1.4 Residual for Population Model (Actual–Predicted). The residuals do not appear to be independent, and the model could be modified to explain the remaining nonrandom errors.. Example 18.2: A Consumer Demand Model. A portion of the printed output produced by this example is shown in Output 18.2.1 through Output 18.2.3.. Output 18.2.1...
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1242 F Chapter 18: The MODEL Procedure. title1 'Example of MA(1) Error Process Using Grunfeld''s Model';. title2 'MA(1) Error Process Using Unconditional Least Squares';. proc model data=grunfeld model=grunmod;. Output 18.4.1 PROC MODEL Results by Using ULS Estimation. Example of MA(1) Error Process Using Grunfeld's Model MA(1) Error Process Using Unconditional Least Squares. The MODEL Procedure. The estimation summary from the...
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Output 18.6.3 Listing of OUTEST= Data Set Created in the FIT Statement. Output 18.6.4 Listing of OUT= Data Set Created in the First SOLVE Statement. proc model model=model;. The output data set produced by the final SOLVE statement is shown in Output 18.6.5.. Output 18.6.5 Listing of OUT= Data Set Created in the Second SOLVE Statement. Example 18.7: Spring and...
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You can estimate the parameters in the preceding equation by using the following SAS statements:. The results of the estimation are shown in Output 18.9.1.. Output 18.9.1 Circuit Estimation. Circuit Model Estimation Example The MODEL Procedure. NOTE: The model was singular. In this case, the model equation is such that there is linear dependency that causes biased results and inflated...
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The parameter estimates and ANOVA table from this run are shown in Output 18.13.1.. Output 18.13.1 Parameter Estimates from the Switching Regression. The test results shown in Output 18.13.2 suggest that the variance of the housing starts, SIG1 and SIG2, are significantly different in the two regimes. Output 18.13.2 Test Results for Switching Regression. Example 18.14: Simulating from a Mixture...
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1282 F Chapter 18: The MODEL Procedure. proc model data=regdata;. The output of the MODEL procedure is shown in Output 18.15.1:. Output 18.15.1 PROC MODEL Output. Simple regression model The MODEL Procedure. Output 18.15.1 continued. The 2 Equations to Estimate Y = F(a(1), b(x), s) ysq = F(a, b, s) Instruments 1 x. Example 18.16: Simulated Method of Moments—AR(1) Process....
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The output of the MODEL procedure is shown in Output 18.19.1.. Output 18.19.1 PROC MODEL Output. Example 18.20: Illustration of ODS Graphics. These graphical displays are requested by specifying the ODS GRAPHICS ON statement. For information about the graphics available in the MODEL procedure, see the section “ODS Graphics” on page 1165.. The following statements show how to generate ODS...
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(1985), Stiff Computation, New York: Oxford University Press.. (1974), “The Nonlinear Two-Stage Least-Squares Estimator,” Journal of Econometrics . (1977), “The Maximum Likelihood Estimator and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model,” Econometrica . Andersen, T.G., Chung, H-J., and Sorensen, B.E. (1999), “Efficient Method of Moments Estimation of a Stochastic Volatility Model: A Monte Carlo...
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1312 F Chapter 19: The PANEL Procedure. ODS Graphics plots can now be produced by the PANEL procedure. Getting Started: PANEL Procedure. This section demonstrates the use of the PANEL procedure.. Specifying the Input Data. The PANEL procedure is similar to other regression procedures in SAS. The input data set used by PROC PANEL must be sorted by cross section...
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The INSTRUMENTS statement denotes which variables are used in the moment condition equations of the dynamic panel estimator. The following options can be used with the INSTRUMENTS statement.. specifies that a dependent variable be used at an appropriate lag as an instrument.. specifies a list of variables whose future realizations can be correlated with the error term but whose present...
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1332 F Chapter 19: The PANEL Procedure. For the balanced data case, T i D T for all i . Let X and y be the independent and dependent variables arranged by cross section and by time within each cross section. The One-Way Fixed-Effects Model. The specification for the one-way fixed-effects model is u i t D i C i...
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where tr() is the trace operator on a square matrix.. Solving this system produces the variance components. However, there is no guarantee of positive variance components. The Nerlove method for estimating variance components can be obtained by setting VCOMP = NL. The Nerlove method (see Baltagi 1995, page 17) is assured to give estimates of the variance components that are...
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The covariance matrix, denoted by V, can be written in the form V D a 2 .I N ˝ J T / C b 2 .J N ˝ I T / C. Thus, the covariance matrix of the vector of observations y has the form Var.y/ D. The estimator of ˇ is a two-step GLS-type estimator—that is, GLS with the...
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If the regressors are nonrandom, then it is possible to write the variance of the estimated ˇ as the following:. The effect of structure in the variance covariance matrix can be ameliorated by using generalized least squares (GLS), provided that 1 can be calculated. Using 1 , you premultiply both sides of the regression equation,. Estimation of the...
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Example: PANEL Procedure. Example 19.1: Analyzing Demand for Liquid Assets. The demand function for the demand deposits is estimated under three error structures while demand equations for time deposits and savings and loan (S&L) association shares are calculated using the Parks method. label d = 'Per Capita Demand Deposits' t = 'Per Capita Time Deposits'. s = 'Per Capita S...