Providing software solutions since 1976
SAS/ETS® Software Examples
The ARIMA Procedure
- Example 7.1: Simulated IMA Model
- Example 7.2: Seasonal Model for the Airline Series
- Example 7.3: Model for Series J Data from Box and Jenkins
- Example 7.4: An Intervention Model for Ozone Data
- Example 7.5: Using Diagnostics to Identify ARIMA Models
- Example 7.6: Detection of Level Changes in the Nile River Data
- Example 7.7: Iterative Outlier Detection
The AUTOREG Procedure
- Example 8.1: Analysis of Real Output Series
- Example 8.2: Comparing Estimates and Models
- Example 8.3: Lack-of-Fit Study
- Example 8.4: Missing Values
- Example 8.5: Money Demand Model
- Example 8.6: Estimation of ARCH(2) Process
- Example 8.7: Estimation of GARCH-Type Models
- Example 8.8: Illustration of ODS Graphics
The COMPUTAB Procedure
- Example 9.1: Using Programming Statements
- Example 9.2: Enhancing a Report
- Example 9.3: Comparison of Actual and Budget
- Example 9.4: Consolidations
- Example 9.5: Creating an Output Data Set
- Example 9.6: A What-If Market Analysis
- Example 9.7: Cash Flows
The COPULA Procedure
The COUNTREG Procedure
The DATASOURCE Procedure
- Example 12.1: BEA National Income and Product Accounts
- Example 12.2: BLS Consumer Price Index Surveys
- Example 12.3: BLS State and Area Employment, Hours, and Earnings Surveys
- Example 12.4: DRI/McGraw-Hill Format CITIBASE Files
- Example 12.5: DRI Data Delivery Service Database
- Example 12.6: PC Format CITIBASE Database
- Example 12.7: Quarterly COMPUSTAT Data Files
- Example 12.8: Annual COMPUSTAT Data Files, V9.2 New Filetype CSAUC3
- Example 12.9: CRSP Daily NYSE/AMEX Combined Stocks
The ENTROPY Procedure
- Example 13.1: Nonnormal Error Estimation
- Example 13.2: Unreplicated Factorial Experiments
- Example 13.3: Censored Data Models in PROC ENTROPY
- Example 13.4: Use of the PDATA= Option
- Example 13.5: Illustration of ODS Graphics
The ESM Procedure
- Example 14.1: Forecasting of Time Series Data
- Example 14.2: Forecasting of Transactional Data
- Example 14.3: Specifying the Forecasting Model
- Example 14.4: Extending the Independent Variables for Multivariate Forecasts
- Example 14.5: Illustration of ODS Graphics
The EXPAND Procedure
- Example 15.1: Combining Monthly and Quarterly Data
- Example 15.2: Illustration of ODS Graphics
- Example 15.3: Interpolating Irregular Observations
- Example 15.4: Using Transformations
The FORECAST Procedure
- Example 16.1: Forecasting Auto Sales
- Example 16.2: Forecasting Retail Sales
- Example 16.3: Forecasting Petroleum Sales
The LOAN Procedure
- Example 17.1: Discount Points for Lower Interest Rates
- Example 17.2: Refinancing a Loan
- Example 17.3: Prepayments on a Loan
- Example 17.4: Output Data Sets
- Example 17.5: Piggyback Loans
The MDC Procedure
- Example 18.1: Binary Data Modeling
- Example 18.2: Conditional Logit and Data Conversion
- Example 18.3: Correlated Choice Modeling
- Example 18.4: Testing for Homoscedasticity of the Utility Function
- Example 18.5: Choice of Time for Work Trips: Nested Logit Analysis
- Example 18.6: Hausman's Specification Test
- Example 18.7: Likelihood Ratio Test
The MODEL Procedure
- Example 19.1: OLS Single Nonlinear Equation
- Example 19.2: A Consumer Demand Model
- Example 19.3: Vector AR(1) Estimation
- Example 19.4: MA(1) Estimation
- Example 19.5: Polynomial Distributed Lags by Using %PDL
- Example 19.6: General Form Equations
- Example 19.7: Spring and Damper Continuous System
- Example 19.8: Nonlinear FIML Estimation
- Example 19.9: Circuit Estimation
- Example 19.10: Systems of Differential Equations
- Example 19.11: Monte Carlo Simulation
- Example 19.12: Cauchy Distribution Estimation
- Example 19.13: Switching Regression Example
- Example 19.14: Simulating from a Mixture of Distributions
- Example 19.15: Simulated Method of Moments-Simple Linear Regression
- Example 19.16: Simulated Method of Moments-AR(1) Process
- Example 19.17: Simulated Method of Moments-Stochastic Volatility Model
- Example 19.18: Duration Data Model with Unobserved Heterogeneity
- Example 19.19: EMM Estimation of a Stochastic Volatility Model
- Example 19.20: Illustration of ODS Graphics
The PANEL Procedure
- Example 20.1: Analyzing Demand for Liquid Assets
- Example 20.2: The Airline Cost Data: Fixtwo Model
- Example : ODS Graphics Plots
- Example 20.3: The Airline Cost Data: Further Analysis
- Example 20.4: The Airline Cost Data: Random-Effects Models
- Example 20.5: Using the FLATDATA Statement
- Example 20.6: The Cigarette Sales Data: Dynamic Panel Estimation with GMM
The PDLREG Procedure
The QLIM Procedure
- Example 22.1: Ordered Data Modeling
- Example 22.2: Tobit Analysis
- Example 22.3: Bivariate Probit Analysis
- Example 22.4: Sample Selection Model
- Example 22.5: Sample Selection Model with Truncation and Censoring
- Example 22.6: Types of Tobit Models
- Example 22.7: Stochastic Frontier Models
- Example 22.8: Bayesian Modeling
The SEVERITY Procedure
- Example 23.1: Defining a Model for Gaussian Distribution
- Example 23.2: Defining a Model for Gaussian Distribution with a Scale Parameter
- Example 23.3: Defining a Model for Mixed-Tail Distributions
- Example 23.4: Estimating Parameters Using Cramer-von Mises Estimator
- Example 23.5: Fitting a Scaled Tweedie Model with Regressors
- Example 23.6: Fitting Distributions to Interval-Censored Data
The SIMILARITY Procedure
- Example 24.1: Accumulating Transactional Data into Time Series Data
- Example 24.2: Similarity Analysis
- Example 24.3: Sliding Similarity Analysis
- Example 24.4: Searching for Historical Analogies
- Example 24.5: Clustering Time Series
The SIMLIN Procedure
The SPECTRA Procedure
The SSM Procedure
- Example 27.1: Bivariate Basic Structural Model
- Example 27.2: Two-Way Random-Effects and Autoregressive Model for Panel Data
- Example 27.3: Backcasting, Forecasting, and Interpolation
- Example 27.4: Smoothing of Repeated Measures Data
- Example 27.5: A User-Defined Trend Model
- Example 27.6: Model with Multiple ARIMA Components
- Example 27.7: Dynamic Factor Modeling
- Example 27.8: Diagnostic Plots
- Example 27.9: Variable Bandwidth Smoothing
The STATESPACE Procedure
The SYSLIN Procedure
- Example 29.1: Klein's Model I Estimated with LIML and 3SLS
- Example 29.2: Grunfeld's Model Estimated with SUR
- Example 29.3: Illustration of ODS Graphics
The TCOUNTREG Procedure (Experimental)
- Example 30.1: Basic Models
- Example 30.2: ZIP and ZINB Models for Data Exhibiting Extra Zeros
- Example 30.3: Variable Selection
The TIMEDATA Procedure (Experimental)
- Example 31.1: Accumulating Transactional Data into Time Series Data
- Example 31.2: Using User-Defined Functions and Subroutines
The TIMEID Procedure
- Example 32.1: Examining a Weekly Time ID Variable
- Example 32.2: Inferring a Date Interval
- Example 32.3: Examining Multiple BY Groups
The TIMESERIES Procedure
- Example 33.1: Accumulating Transactional Data into Time Series Data
- Example 33.2: Trend and Seasonal Analysis
- Example 33.3: Illustration of ODS Graphics
- Example 33.4: Illustration of Spectral Analysis
- Example 33.5: Illustration of Singular Spectrum Analysis
The UCM Procedure
- Example 35.1: The Airline Series Revisited
- Example 35.2: Variable Star Data
- Example 35.3: Modeling Long Seasonal Patterns
- Example 35.4: Modeling Time-Varying Regression Effects
- Example 35.5: Trend Removal Using the Hodrick-Prescott Filter
- Example 35.6: Using Splines to Incorporate Nonlinear Effects
- Example 35.7: Detection of Level Shift
- Example 35.8: ARIMA Modeling
The VARMAX Procedure
- Example 36.1: Analysis of U.S. Economic Variables
- Example 36.2: Analysis of German Economic Variables
- Example 36.3: Numerous Examples
- Example 36.4: Illustration of ODS Graphics
The X11 Procedure
- Example 37.1: Component Estimation-Monthly Data
- Example 37.2: Components Estimation-Quarterly Data
- Example 37.3: Outlier Detection and Removal
The X12 Procedure
- Example 38.1: ARIMA Model Identification
- Example 38.2: Model Estimation
- Example 38.3: Seasonal Adjustment
- Example 38.4: RegARIMA Automatic Model Selection
- Example 38.5: Automatic Outlier Detection
- Example 38.6: User-Defined Regressors
- Example 38.7: MDLINFOIN= and MDLINFOOUT= Data Sets
- Example 38.8: Setting Regression Parameters
- Example 38.9: Creating an MDLINFO= Data Set for Use with the PICKMDL Statement
- Example 38.10: Illustration of ODS Graphics
- Example 38.11: AUXDATA= Data Set
The SASECRSP Interface Engine
- Example 39.1: Specifying PERMNOs and RANGE on the LIBNAME Statement
- Example 39.2: Using the LIBNAME Statement to Access All Keys
- Example 39.3: Accessing One PERMNO Using No RANGE
- Example 39.4: Specifying Keys Using the INSET= Option
- Example 39.5: Specifying Ranges for Individual Keys with the INSET= Option
- Example 39.6: Converting Dates By Using the CRSP Date Functions
- Example 39.7: Comparing Different Ways of Accessing CCM Data
- Example 39.8: Comparing PERMNO and GVKEY Access of CRSP Stock Data
- Example 39.9: Using Fiscal Date Range Restriction
- Example 39.10: Using Different Types of Range Restrictions in the INSET
- Example 39.11: Using INSET Ranges with the LIBNAME RANGE Option
The SASEXCCM Interface Engine
- Example 40.1: Retrieving SALE Data for One GVKEY
- Example 40.2: Retrieving SALE Data for Multiple Companies
- Example 40.3: Retrieving Data in Different Keysets
- Example 40.4: Retrieving Items with Global Options
- Example 40.5: Retrieving All GVKEYs and Company Names
- Example 40.6: Retrieving Stock Time Series by PERMNO
- Example 40.7: Retrieving Stock and Indices Monthly Time Series by INDNO
- Example 40.8: Retrieving Stock and Indices Daily Time Series by INDNO
- Example 40.9: Retrieving Information for Availability of Group INDNOs
- Example 40.10: Retrieving Daily Group Time Series by INDNO= Option
- Example 40.11: Retrieving Monthly Group Time Series by INDNO= Option
The SASEFAME Interface Engine
- Example 41.1: Converting an Entire Fame Database
- Example 41.2: Reading Time Series from the Fame Database
- Example 41.3: Writing Time Series to the SAS Data Set
- Example 41.4: Limiting the Time Range of Data
- Example 41.5: Creating a View Using the SQL Procedure and SASEFAME
- Example 41.6: Reading Other Fame Data Objects with the FAMEOUT= Option
- Example 41.7: Remote Fame Access Using Fame CHLI
- Example 41.8: Selecting Time Series Using CROSSLIST= Option and KEEP Statement
- Example 41.9: Selecting Time Series Using CROSSLIST= Option and Fame Namelist
- Example 41.10: Selecting Time Series Using CROSSLIST= Option and WHERE=TICK
- Example 41.11: Selecting Boolean Case Series with the FAMEOUT= Option
- Example 41.12: Selecting Numeric Case Series with the FAMEOUT= Option
- Example 41.13: Selecting Date Case Series with the FAMEOUT= Option
- Example 41.14: Selecting String Case Series with the FAMEOUT= Option
- Example 41.15: Extracting Source for Formulas
- Example 41.16: Reading Time Series by Defining Fame Expression Groups in the INSET= Option with the KEEP= Clause
- Example 41.17: Optimizing Cache Sizes with the TUNEFAME= and TUNECHLI= Options
The SASEHAVR Interface Engine
- Example 42.1: Examining the Contents of a Haver Database
- Example 42.2: Viewing Quarterly Time Series from a Haver Database
- Example 42.3: Viewing Monthly Time Series from a Haver Database
- Example 42.4: Viewing Weekly Time Series from a Haver Database
- Example 42.5: Viewing Daily Time Series from a Haver Database
- Example 42.6: Limiting the Range of Time Series from a Haver Database
- Example 42.7: Using the WHERE Statement to Subset Time Series from a Haver Database
- Example 42.8: Using the KEEP Option to Subset Time Series from a Haver Database
- Example 42.9: Using the SOURCE Option to Subset Time Series from a Haver Database
- Example 42.10: Using the GROUP Option to Subset Time Series from a Haver Database
- Example 42.11: Using the OUTSELECT=ON Option to View the Key Selection Variables in a Haver Database
- Example 42.12: Selecting Variables Based on Short Source Key Code
- Example 42.13: Selecting Variables Based on Geography Key Codes
The SASEXFSD Interface Engine
- Example 43.1: Retrieving Price Data for One Company
- Example 43.2: Retrieving Price and Sales Data for Multiple Companies
- Example 43.3: Retrieving Book Value Data for One Company by Using Relative Dates
- Example 43.4: Retrieving Multiple Screen Items for Multiple Companies for One Date
- Example 43.5: Retrieving Data by Using ISON and ISONParams
- Example 43.6: Retrieving Multiple Items for Multiple Companies from an OFDB File
- Example 43.7: Retrieving a List of Securities from an OFDB file
- Example 43.8: Retrieving a List of CUSIPs from a Screen File
- Example 43.9: Retrieving Standardized Economic Items for Multiple Countries
Web Examples
- Plotting Time Series Data
- Computing Marginal Effects for Discrete Dependent Variable Models
- Calculating Economic Indices
- Testing for Returns to Scale in a Cobb-Douglas Production Function
- Estimating GARCH Models
- Analysis of Unobserved Component Models Using PROC UCM
- Tourism Demand Modeling and Forecasting with PROC VARMAX
- Multiple Imputation for a GARCH(1,1) Model
- Calculating Price Elasticity of Demand
- Efficient Method of Moments Estimation of a Stochastic Volatility Model
- Estimating an Almost Ideal Demand System Model
- Calculating Elasticites in an Almost Ideal Demand System
- Heteroscedastic Two-Stage Least Squares Regression with PROC MODEL
- Heteroscedastic Modeling of the Fed Funds Rate
- Chow Test for Structural Breaks
- Bivariate Granger Causality Test
- Fitting a Capital Asset Pricing Model
- Estimating a Consumption-Based Asset Pricing Model
- Efficiency Test for Estimators by Simulation
- A Specification Test for Non-Nested Models
- Regression Model with Correction of Heteroscedasticity
- Transforming the Frequency of Time Series Data
- Forecasting a Seasonal ARMA Process
- Accounting for Missing Observations in Time Series Data
- Overlaying Multiple Forecast Methods in Time Series Plots
- Bootstrapping Correct Critical Values in Tests for Structural Change
