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SAS/ETS 13.2 User's Guide

Examples from Documentation

Select an example PROC ARIMA PROC AUTOREG PROC COMPUTAB PROC COPULA PROC COUNTREG PROC DATASOURCE PROC ENTROPY PROC ESM PROC EXPAND PROC FORECAST PROC LOAN PROC MDC PROC MODEL PROC PANEL PROC PDLREG PROC QLIM PROC SEVERITY PROC SIMLIN PROC SIMILARITY PROC SPECTRA PROC SSM PROC STATESPACE PROC SYSLIN PROC TIMEDATA PROC TIMEID PROC TIMESERIES PROC TSCSREG PROC UCM PROC VARMAX PROC X11 PROC X12 SASECRSP Interface Engine SASEFAME Interface Engine SASEFRED Interface Engine SASEXCCM Interface Engine SASEXFSD Interface Engine SASEHAVR Interface Engine

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ARIMA Procedure

• The Three Stages of ARIMA Modeling
• Identification Stage
• Estimation and Diagnostic Checking Stage
• Forecasting Stage
• Using ARIMA Procedure Statements
• General Notation for ARIMA Models
• Stationarity
• Differencing
• Subset, Seasonal, and Factored ARMA Models
• Input Variables and Regression with ARMA Errors
• Intervention Models and Interrupted Time Series
• Rational Transfer Functions and Distributed Lag Models
• Forecasting with Input Variables
• Data Requirements

• 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

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AUTOREG Procedure

• Regression with Autocorrelated Errors
• Forecasting Autoregressive Error Models
• Testing for Autocorrelation
• Stepwise Autoregression
• Testing for Heteroscedasticity
• Heteroscedasticity and GARCH Models

• 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

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COMPUTAB Procedure

• Producing a Simple Report
• Using PROC COMPUTAB
• Defining Report Layout
• Adding Computed Rows and Columns
• Enhancing the Report

• 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

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COPULA Procedure

• Getting Started: COPULA Procedure

• Example 10.1: Copula Based VaR Estimation
• Example 10.2: Simulating Default Times

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COUNTREG Procedure

• Getting Started: COUNTREG Procedure

• Example 11.1: Basic Models
• Example 11.2: ZIP and ZINB Models for Data That Exhibit Extra Zeros
• Example 11.3: Variable Selection

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DATASOURCE Procedure

• Structure of a SAS Data Set Containing Time Series Data
• Reading Data Files
• Subsetting Input Data Files
• Controlling the Frequency of Data – The INTERVAL= Option
• Selecting Time Series Variables – The KEEP and DROP Statements
• Controlling the Time Range of Data – The RANGE Statement
• Reading in Data Files Containing Cross Sections
• Obtaining Descriptive Information on Cross Sections
• Subsetting a Data File Containing Cross Sections
• Renaming Time Series Variables
• Changing the Lengths of Numeric Variables

• 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

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ENTROPY Procedure

• Simple Regression Analysis
• Using Prior Information
• Pure Inverse Problems
• Analyzing Multinomial Response Data

• 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

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ESM Procedure

• Getting Started: 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

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EXPAND Procedure

• Converting to Higher Frequency Series
• Aggregating to Lower Frequency Series
• Combining Time Series with Different Frequencies
• Interpolating Missing Values
• Requesting Different Interpolation Methods
• Using the ID Statement
• Specifying Observation Characteristics
• Converting Observation Characteristics
• Creating New Variables
• Transforming Series

• 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

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FORECAST Procedure

• Giving Dates to Forecast Values
• Computing Confidence Limits
• Form of the OUT= Data Set
• Plotting Forecasts
• Plotting Residuals
• Model Parameters and Goodness-of-Fit Statistics
• Controlling the Forecasting Method
• Introduction to Forecasting Methods
• Time Trend Models
• Time Series Methods
• Combining Time Trend with Autoregressive Models

• Example 16.1: Forecasting Auto Sales
• Example 16.2: Forecasting Retail Sales
• Example 16.3: Forecasting Petroleum Sales

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LOAN Procedure

• Analyzing Fixed Rate Loans
• Analyzing Balloon Payment Loans
• Analyzing Adjustable Rate Loans
• Analyzing Buydown Rate Loans
• Loan Repayment Schedule
• Loan Comparison

• 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

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MDC Procedure

• Conditional Logit: Estimation and Prediction
• Nested Logit Modeling
• Multivariate Normal Utility Function
• HEV and Multinomial Probit: Heteroscedastic Utility Function
• Parameter Heterogeneity: Mixed Logit

• 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

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MODEL Procedure

• Nonlinear Regression Analysis
• Nonlinear Systems Regression
• General Form Models
• Solving Simultaneous Nonlinear Equation Systems
• Monte Carlo Simulation

• 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

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PANEL Procedure

• Getting Started: 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

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PDLREG Procedure

• Introductory Example

• Example 21.1: Industrial Conference Board Data
• Example 21.2: Money Demand Model

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QLIM Procedure

• Introductory Example: Binary Probit and Logit Models

• 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

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SEVERITY Procedure

• A Simple Example of Fitting Predefined Distributions
• An Example with Left-Truncation and Right-Censoring
• An Example of Modeling Regression Effects

• Example 23.1: Defining a Model for Gaussian Distribution
• Example 23.2: Defining a Model for the Gaussian Distribution with a Scale Parameter
• Example 23.3: Defining a Model for Mixed-Tail Distributions
• Example 23.4: Estimating Parameters Using Cramér-von Mises Estimator
• Example 23.5: Fitting a Scaled Tweedie Model with Regressors
• Example 23.6: Fitting Distributions to Interval-Censored Data
• Example 23.7: Defining a Finite Mixture Model That Has a Scale Parameter
• Example 23.8: Predicting Mean and Value-at-Risk by Using Scoring Functions
• Example 23.9: Scale Regression with Rich Regression Effects

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SIMILARITY Procedure

• Getting Started: 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

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SIMLIN Procedure

• Prediction and Simulation

• Example 25.1: Simulating Klein’s Model I
• Example 25.2: Multipliers for a Third-Order System

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SPECTRA Procedure

• Getting Started: SPECTRA Procedure

• Example 26.1: Spectral Analysis of Sunspot Activity
• Example 26.2: Cross-Spectral Analysis

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SSM Procedure

• Getting Started: SSM Procedure

• Example 27.1: Bivariate Basic Structural Model
• Example 27.2: Panel Data: Random-Effects and Autoregressive Models
• Example 27.3: Backcasting, Forecasting, and Interpolation
• Example 27.4: Longitudinal Data: Smoothing of Repeated Measures
• 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 and Structural Break Analysis
• Example 27.9: Longitudinal Data: Variable Bandwidth Smoothing
• Example 27.10: A Transfer Function Model for the Gas Furnace Data
• Example 27.11: Panel Data: Dynamic Panel Model for the Cigar Data
• Example 27.12: Multivariate Modeling: Long-Term Temperature Trends
• Example 27.13: Bivariate Model: Sales of Mink and Muskrat Furs
• Example 27.14: Factor Model: Now-Casting the US Economy
• Example 27.15: Longitudinal Data: Lung Function Analysis

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STATESPACE Procedure

• Automatic State Space Model Selection
• Specifying the State Space Model

• Example 28.1: Series J from Box and Jenkins

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SYSLIN Procedure

• An Example Model
• Variables in a System of Equations
• Using PROC SYSLIN
• OLS Estimation
• Two-Stage Least Squares Estimation
• LIML, K-Class, and MELO Estimation
• SUR, 3SLS, and FIML Estimation
• Computing Reduced Form Estimates
• Restricting Parameter Estimates
• Testing Parameters
• Saving Residuals and Predicted Values
• Plotting Residuals

• 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

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TIMEDATA Procedure

• Getting Started: TIMEDATA Procedure

• Example 30.1: Accumulating Transactional Data into Time Series Data
• Example 30.2: Using User-Defined Functions and Subroutines
• Example 30.3: Using Auxiliary Data Sets with PROC TIMEDATA

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TIMEID Procedure

• Getting Started: TIMEID Procedure

• Example 31.1: Examining a Weekly Time ID Variable
• Example 31.2: Inferring a Date Interval
• Example 31.3: Examining Multiple BY Groups

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TIMESERIES Procedure

• Getting Started: TIMESERIES Procedure

• Example 32.1: Accumulating Transactional Data into Time Series Data
• Example 32.2: Trend and Seasonal Analysis
• Example 32.3: Illustration of ODS Graphics
• Example 32.4: Illustration of Spectral Analysis
• Example 32.5: Illustration of Singular Spectrum Analysis

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UCM Procedure

• A Seasonal Series with Linear Trend

• Example 34.1: The Airline Series Revisited
• Example 34.2: Variable Star Data
• Example 34.3: Modeling Long Seasonal Patterns
• Example 34.4: Modeling Time-Varying Regression Effects
• Example 34.5: Trend Removal Using the Hodrick-Prescott Filter
• Example 34.6: Using Splines to Incorporate Nonlinear Effects
• Example 34.7: Detection of Level Shift
• Example 34.8: ARIMA Modeling

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VARMAX Procedure

• Vector Autoregressive Process
• Bayesian Vector Autoregressive Process
• Vector Error Correction Model
• Bayesian Vector Error Correction Model
• Vector Autoregressive Process with Exogenous Variables
• Parameter Estimation and Testing on Restrictions
• Causality Testing

• Example 35.1: Analysis of U.S. Economic Variables
• Example 35.2: Analysis of German Economic Variables
• Example 35.3: Numerous Examples
• Example 35.4: Illustration of ODS Graphics

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X11 Procedure

• Basic Seasonal Adjustment
• X-11-ARIMA

• Example 36.1: Component Estimation—Monthly Data
• Example 36.2: Components Estimation—Quarterly Data
• Example 36.3: Outlier Detection and Removal

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X12 Procedure

• Basic Seasonal Adjustment

• Example 37.1: ARIMA Model Identification
• Example 37.2: Model Estimation
• Example 37.3: Seasonal Adjustment
• Example 37.4: RegARIMA Automatic Model Selection
• Example 37.5: Automatic Outlier Detection
• Example 37.6: User-Defined Regressors
• Example 37.7: MDLINFOIN= and MDLINFOOUT= Data Sets
• Example 37.8: Setting Regression Parameters
• Example 37.9: Creating an MDLINFO= Data Set for Use with the PICKMDL Statement
• Example 37.10: Illustration of ODS Graphics
• Example 37.11: AUXDATA= Data Set

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SASECRSP Interface Engine

• Structure of a SAS Data Set That Contains Time Series Data
• Reading CRSP Data Files
• Using the SAS DATA Step
• Using SAS Procedures
• Using the SAS Windowing Environment
• Using CRSP Date Formats, Informats, and Functions

• Example 38.1: Specifying PERMNOs and Range in the LIBNAME Statement
• Example 38.2: Using the LIBNAME Statement to Access All Keys
• Example 38.3: Accessing One PERMNO without the RANGE= Option
• Example 38.4: Specifying Keys by Using the INSET= Option
• Example 38.5: Specifying Ranges for Individual Keys with the INSET= Option
• Example 38.6: Converting Dates by Using the CRSP Date Functions

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SASEXCCM Interface Engine

• Getting Started: 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 from Different Keysets
• Example 40.4: Retrieving Items by Using 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 the INDNO= Option
• Example 40.11: Retrieving Monthly Group Time Series by the INDNO= Option

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SASEFAME Interface Engine

• Structure of a SAS Data Set That Contains Time Series Data
• Reading and Converting Fame Database Time Series
• Using the SAS DATA Step
• Using SAS Procedures
• Using the SAS Windowing Environment
• Remote Fame Data Access
• Creating Views of Time Series by Using SASEFAME LIBNAME Options

• 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 the SASEFAME Engine
• Example 41.6: Reading Other Fame Data Objects with the FAMEOUT= Option
• Example 41.7: Remote Fame Access by Using Fame CHLI
• Example 41.8: Selecting Time Series by Using the CROSSLIST= Option and KEEP Statement
• Example 41.9: Selecting Time Series by Using the CROSSLIST= Option and Fame Namelist
• Example 41.10: Selecting Time Series by Using the 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

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SASEHAVR Interface Engine

• Setting Up the Haver Analytics DLX Application Programming Interface
• Structure of a SAS Data Set That Contains Time Series Data
• Reading and Converting Haver DLX Time Series
• Using the SAS DATA Step
• Using the SAS Windowing Environment

• 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

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SASEXFSD Interface Engine

• Getting Started: SASEXFSD Interface Engine

• Example 44.1: Retrieving Standardized Economic Items for Multiple Countries
• Example 44.2: Retrieving Economic Items by Using the FQL Syntax for Function
• Example 44.3: Using ECON_EXPR_DATA with the FQL Syntax for Function Returns
• Example 44.4: Using SPEC_ID_DATA with the FQL Economic Download Syntax
• Example 44.5: Using Multiple Database Sources with the FQL Syntax
• Example 44.6: Retrieving Price Data for One Company
• Example 44.7: Retrieving Price and Sales Data for Multiple Companies
• Example 44.8: Retrieving Book Value Data for One Company by Using Relative Dates
• Example 44.9: Retrieving Multiple Screen Items for Multiple Companies
• Example 44.10: Retrieving Data by Using the ISON= and ISONPARAMS= Options
• Example 44.11: Retrieving Benchmark Data by Using the CUTOFF= option
• Example 44.12: Retrieving Benchmark Data by Using the MATCHDATE= Option
• Example 44.13: Retrieving Multiple Items for Multiple Companies from an OFDB File
• Example 44.14: Retrieving a List of Securities from an OFDB file
• Example 44.15: Retrieving a List of CUSIPs from a Screen File

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