Có 180+ tài liệu thuộc chủ đề "Hướng dẫn sử dụng SAS / ETS"
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The plots in Output 22.3.2 show that both the lognormal and GPD distributions fit the data poorly, GPD being the worst. Output 22.3.2 Comparison of the CDF and PDF Estimates of the Fitted Models. Output 22.3.2 continued. The P-P plots of Output 22.3.3 provide a better visual confirmation that the LOGNGPD distribution fits the tail region better than the Burr...
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1592 F Chapter 23: The SIMILARITY Procedure. Therefore, the analysis results are useful for large-scale time series analysis, analogous time series forecasting, new product forecasting, or time series (temporal) data mining.. The SAS/ETS EXPAND procedure can be used for frequency conversion and transformations of time series. The TIMESERIES procedure can be used for large-scale time series analysis. The SAS/STAT DISTANCE...
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Missing values at the end of the accumulated series remain missing.. specifies a SAS date, datetime, or time value that represents the beginning of the data. specifies how beginning and ending zero values (either actual or accumulated) are interpreted in the accumulated time series. The INPUT statement lists the input numeric variables in the DATA= data set whose values are...
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1612 F Chapter 23: The SIMILARITY Procedure. If the ACCUMULATE=TOTAL option is specified, the data are accumulated as follows:. If the ACCUMULATE=AVERAGE option is specified, the data are accumulated as follows:. If the ACCUMULATE=MINIMUM option is specified, the data are accumulated as follows:. If the ACCUMULATE=MEDIAN option is specified, the data are accumulated as follows:. If the ACCUMULATE=MAXIMUM option is...
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1622 F Chapter 23: The SIMILARITY Procedure. 1624 F Chapter 23: The SIMILARITY Procedure. However, these functions cannot be directly used by the SIM- ILARITY procedure. In order to use these C language functions in the SIMILARITY procedure, two SAS language functions must be created that call these two C language functions. The following SAS statements create two user-defined SAS...
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1632 F Chapter 23: The SIMILARITY Procedure. Time Series Plots. The time series plots (SeriesPlot) illustrate the input time series to be compared. The horizontal axis represents the input series time ID values, and the vertical axis represents the input series values.. The sequence plots (SequencePlot) illustrate the target and input sequences to be compared. The horizontal axis represents the...
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Output 23.2.8 Path Distance Plot. Output 23.2.9 Path Distance Histogram. Output 23.2.10 Path Relative Distance Plot. Output 23.2.11 Path Relative Distance Histogram. Output 23.2.12 Path Limits. Output 23.2.13 Path Statistics. Output 23.2.14 Cost Plot. Output 23.2.15 Cost Statistics. Output 23.2.16 Time Warp Plot. Output 23.2.17 Time Warp Scaled Plot. Output 23.2.18 Path Plot with Warping Limits. Output 23.2.19 Warped Path...
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Example 23.3: Sliding Similarity Analysis. This example illustrates how to use sliding similarity analysis to compare two time sequences. SASHELP.WORKERS data set contains two similar time series variables ( ELECTRIC and MASONRY. The following statements create an example data set that contains two time series of differing lengths, where the variable MASONRY has the first 12 and last 7 observations...
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ENDOGENOUS variables . OUTPUT OUT=SAS-data-set options. The statements and options controlling the SIMLIN procedure are summarized in the following table.. Data Set Options. specify input data set containing structural co- efficients. specify type of estimates read from EST= data set. write reduced form coefficients and multipliers to an output data set. specify the input data set for simulation PROC SIMLIN...
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1672 F Chapter 24: The SIMLIN Procedure. Structural Coefficients for Exogenous Variables. Reduced Form. The reduced form coefficients are obtained by inverting G so that the endogenous variables can be directly expressed as functions of only lagged endogenous and exogenous variables.. Inverse Coefficient Matrix for Endogenous Variables. This is the inverse of the G matrix.. Reduced Form for Lagged Endogenous...
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The actual and predicted values for the variable C are plotted in Output 24.1.11.. Output 24.1.11 Plot of Actual and Predicted Consumption. Example 24.2: Multipliers for a Third-Order System. This example shows how to fit and simulate a single equation dynamic model with third-order lags. It then shows how to convert the third-order equation into a three equation system with...
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1692 F Chapter 25: The SPECTRA Procedure. To produce cross-spectral density estimates, specify both the CROSS option and the S option. The cross-periodogram is smoothed using the weights specified by the WEIGHTS statement in the same way as the spectral density. The squared coherency and phase estimates of the cross-spectrum are computed when the K and PH options are used.....
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1702 F Chapter 25: The SPECTRA Procedure. By default PROC SPECTRA produces no printed output.. When the WHITETEST option is specified, the SPECTRA procedure prints the following statistics for each variable in the VAR statement:. the name of the variable. the sum of the periodogram ordinates 5. ODS Table Names: SPECTRA procedure. PROC SPECTRA assigns a name to each table...
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The plot of the cross-spectrum amplitude against period for periods less than 25 observations is shown in Output 25.2.3.. (1975), Time Series: Data Analysis and Theory, New York: Holt, Rinehart and Winston, Inc.. (1966), “Fast Fourier Transforms–for Fun and Profit,” AFIPS Proceedings of the Fall Joint Computer Conference . (1957), “On Consistent Estimates of the Spectrum of a Stationary Time...
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Figure 26.3 shows a schematic representation of the partial autocorrelations, similar to the autocor- relations shown in Figure 26.2. This output shows the coefficient matrices of the vector autoregressive model at each lag.. Selected State Space Model Form and Preliminary Estimates. After the autoregressive order selection process has determined the number of lags to consider, the canonical correlation analysis phase...
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1732 F Chapter 26: The STATESPACE Procedure. specifies the upper limit to the dimension of the state vector. The DIMMAX= option can be used to limit the size of the model selected. The default is DIMMAX=10.. specifies the minimum number of lags to include in the canonical correlation analysis. specifies the multiplier of the degrees of freedom for the penalty...
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If mi n is significantly greater than 0, x 1;t C 1 j t is added to the state vector.. The variable x 1;t C 1 j t is not added to the state vector, nor are any terms x 1;t C k j t considered as possible components of the state vector. The process described for x 1;t C...
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1752 F Chapter 26: The STATESPACE Procedure. a schematic representation of the partial autocorrelation matrices, showing the significant partial autocorrelations. the Yule-Walker estimates of the autoregressive parameters for the autoregressive model with the minimum AIC. if the PRINTOUT=LONG option is specified, the autocovariance matrices of the residuals of the minimum AIC model. This is the sequence of estimated innovation variance...
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1762 F Chapter 27: The SYSLIN Procedure. Examples: SYSLIN Procedure. Example 27.1: Klein’s Model I Estimated with LIML and 3SLS. Example 27.2: Grunfeld’s Model Estimated with SUR. Example 27.3: Illustration of ODS Graphics. Overview: SYSLIN Procedure. The SYSLIN procedure estimates parameters in an interdependent system of linear regression equations.. Ordinary least squares (OLS) estimates are biased and inconsistent when current...
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1772 F Chapter 27: The SYSLIN Procedure. Figure 27.6 continued. Parameter Estimates. |t| Label Intercept Intercept p lt;.0001 Price u lt;.0001 Unit Cost. The system weighted MSE and system weighted R 2 measure the fit of the joint model obtained by stacking all the models together and performing a single regression with the stacked observations weighted by the inverse of...