SAS/ETS(R) 9.2 User's Guide
General Information- Whats New in SAS/ETS
- Whats New in SAS/ETS for SAS 9.2
- Overview
- AUTOREG Procedure
- COUNTREG Procedure
- DATASOURCE Procedure
- New ESM Procedure
- MODEL Procedure
- PANEL Procedure
- QLIM Procedure
- SASECRSP Engine
- SASEFAME Engine
- SASEHAVR Engine
- New SIMILARITY Procedure (Experimental)
- UCM Procedure
- VARMAX Procedure
- X12 Procedure
- What Was New in SAS/ETS for SAS 9.1
- Overview
- Financial Functions
- Date, Time, and Datetime Functions
- SASECRSP Engine
- SASEFAME Engine
- SASEHAVR Engine
- ARIMA Procedure
- ENTROPY Procedure
- EXPAND Procedure
- MDC Procedure
- MODEL Procedure
- QLIM Procedure
- TIMESERIES Procedure
- UCM Procedure
- VARMAX Procedure
- X12 Procedure
- Time Series Forecasting System
- References
- Introduction
- Overview of SAS/ETS Software
- About This Book
- Where to Turn for More Information
- Accessing the SAS/ETS Sample Library
- Online Help System
- SAS Short Courses
- SAS Technical Support Services
- Major Features of SAS/ETS Software
- Discrete Choice and Qualitative and Limited Dependent Variable Analysis
- Regression with Autocorrelated and Heteroscedastic Errors
- Simultaneous Systems Linear Regression
- Linear Systems Simulation
- Polynomial Distributed Lag Regression
- Nonlinear Systems Regression and Simulation
- ARIMA (Box-Jenkins) and ARIMAX (Box-Tiao) Modeling and Forecasting
- Vector Time Series Analysis
- State Space Modeling and Forecasting
- Spectral Analysis
- Seasonal Adjustment
- Structural Time Series Modeling and Forecasting
- Time Series Cross-Sectional Regression Analysis
- Automatic Time Series Forecasting
- Time Series Interpolation and Frequency Conversion
- Trend and Seasonal Analysis on Transaction Databases
- Access to Financial and Economic Databases
- Spreadsheet Calculations and Financial Report Generation
- Loan Analysis, Comparison, and Amortization
- Time Series Forecasting System
- Investment Analysis System
- ODS Graphics
- Related SAS Software
- Base SAS Software
- SAS Forecast Studio
- SAS High-Performance Forecasting
- SAS/GRAPH Software
- SAS/STAT Software
- SAS/IML Software
- SAS Stat Studio
- SAS/OR Software
- SAS/QC Software
- MLE for User-Defined Likelihood Functions
- JMPSoftware
- SAS Enterprise Guide
- SASAdd-In for Microsoft Office
- SAS Enterprise MinerTMTime Series Node
- SAS Risk Products
- References
- Working with Time Series Data
- Overview
- Time Series and SAS Data Sets
- Dating Observations
- SAS Date, Datetime, and Time Values
- Reading Date and Datetime Values with Informats
- Formatting Date and Datetime Values
- The Variables DATE and DATETIME
- Sorting by Time
- Subsetting Data and Selecting Observations
- Storing Time Series in a SAS Data Set
- Standard Form of a Time Series Data Set
- Several Series with Different Ranges
- Missing Values and Omitted Observations
- Cross-Sectional Dimensions and BY Groups
- Interleaved Time Series
- Output Data Sets of SAS/ETS Procedures
- Time Series Periodicity and Time Intervals
- Specifying Time Intervals
- Using Intervals with SAS/ETS Procedures
- Time Intervals, the Time Series Forecasting System, and the Time Series Viewer
- Plotting Time Series
- Calendar and Time Functions
- Computing Dates from Calendar Variables
- Computing Calendar Variables from Dates
- Converting between Date, Datetime, and Time Values
- Computing Datetime Values
- Computing Calendar and Time Variables
- Interval Functions INTNX and INTCK
- Incrementing Dates by Intervals
- Alignment of SAS Dates
- Computing the Width of a Time Interval
- Computing the Ceiling of an Interval
- Counting Time Intervals
- Checking Data Periodicity
- Filling in Omitted Observations in a Time Series Data Set
- Using Interval Functions for Calendar Calculations
- Lags, Leads, Differences, and Summations
- The LAG and DIF Functions
- Multiperiod Lags and Higher-Order Differencing
- Percent Change Calculations
- Leading Series
- Summing Series
- Transforming Time Series
- Manipulating Time Series Data Sets
- Time Series Interpolation
- Interpolating Missing Values
- Interpolating to a Higher or Lower Frequency
- Interpolating between Stocks and Flows, Levels and Rates
- Reading Time Series Data
- Date Intervals, Formats, and Functions
- Overview
- Time Intervals
- Constructing Interval Names
- Shifted Intervals
- Beginning Dates and Datetimes of Intervals
- Summary of Interval Types
- Examples of Interval Specifications
- Date and Datetime Informats
- Date, Time, and Datetime Formats
- Alignment of SAS Dates
- Date, Time, and Datetime Functions
- References
- SAS Macros and Functions
- Nonlinear Optimization Methods
- Procedure Reference
- The ARIMA Procedure
- Overview: ARIMA Procedure
- Getting Started: 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
- Syntax: ARIMA Procedure
- Functional Summary
- PROC ARIMA Statement
- BY Statement
- IDENTIFY Statement
- ESTIMATE Statement
- OUTLIER Statement
- FORECAST Statement
- Details: ARIMA Procedure
- The Inverse Autocorrelation Function
- The Partial Autocorrelation Function
- The Cross-Correlation Function
- The ESACF Method
- The MINIC Method
- The SCAN Method
- Stationarity Tests
- Prewhitening
- Identifying Transfer Function Models
- Missing Values and Autocorrelations
- Estimation Details
- Specifying Inputs and Transfer Functions
- Initial Values
- Stationarity and Invertibility
- Naming of Model Parameters
- Missing Values and Estimation and Forecasting
- Forecasting Details
- Forecasting Log Transformed Data
- Specifying Series Periodicity
- Detecting Outliers
- OUT= Data Set
- OUTCOV= Data Set
- OUTEST= Data Set
- OUTMODEL= SAS Data Set
- OUTSTAT= Data Set
- Printed Output
- ODS Table Names
- Statistical Graphics
- Examples: ARIMA Procedure
- Simulated IMA Model
- Seasonal Model for the Airline Series
- Model for Series J Data from Box and Jenkins
- An Intervention Model for Ozone Data
- Using Diagnostics to Identify ARIMA Models
- Detection of Level Changes in the Nile River Data
- Iterative Outlier Detection
- References
- The AUTOREG Procedure
- Overview: AUTOREG Procedure
- Getting Started: AUTOREG Procedure
- Regression with Autocorrelated Errors
- Forecasting Autoregressive Error Models
- Testing for Autocorrelation
- Stepwise Autoregression
- Testing for Heteroscedasticity
- Heteroscedasticity and GARCH Models
- Syntax: AUTOREG Procedure
- Functional Summary
- PROC AUTOREG Statement
- BY Statement
- MODEL Statement
- HETERO Statement
- RESTRICT Statement
- TEST Statement
- OUTPUT Statement
- Details: AUTOREG Procedure
- Missing Values
- Autoregressive Error Model
- Alternative Autocorrelation Correction Methods
- GARCH, IGARCH, EGARCH, and GARCH-M Models
- R-Square Statistics and Other Measures of Fit
- Generalized Durbin-Watson Tests
- Testing
- Predicted Values
- OUT= Data Set
- OUTEST= Data Set
- Printed Output
- ODS Table Names
- ODS Graphics
- Examples: AUTOREG Procedure
- Analysis of Real Output Series
- Comparing Estimates and Models
- Lack-of-Fit Study
- Missing Values
- Money Demand Model
- Estimation of ARCH(2) Process
- Illustration of ODS Graphics
- References
- The COMPUTAB Procedure
- Overview: COMPUTAB Procedure
- Getting Started: COMPUTAB Procedure
- Producing a Simple Report
- Using PROC COMPUTAB
- Defining Report Layout
- Adding Computed Rows and Columns
- Enhancing the Report
- Syntax: COMPUTAB Procedure
- Functional Summary
- PROC COMPUTAB Statement
- COLUMNS Statement
- ROWS Statement
- CELL Statement
- INIT Statement
- Programming Statements
- BY Statement
- SUMBY Statement
- NOTRANS Option
- Program Flow Example
- Order of Calculations
- Column Selection
- Controlling Execution within Row and Column Blocks
- Program Flow
- Direct Access to Table Cells
- Reserved Words
- Missing Values
- OUT= Data Set
- Examples: COMPUTAB Procedure
- The COUNTREG Procedure
- Overview: COUNTREG Procedure
- Getting Started: COUNTREG Procedure
- Syntax: COUNTREG Procedure
- Functional Summary
- PROC COUNTREG Statement
- BOUNDS Statement
- BY Statement
- INIT Statement
- MODEL Statement
- OUTPUT Statement
- RESTRICT Statement
- ZEROMODEL Statement
- Details: COUNTREG Procedure
- Missing Values
- Poisson Regression
- Negative Binomial Regression
- Zero-Inflated Count Regression Overview
- Zero-Inflated Poisson Regression
- Zero-Inflated Negative Binomial Regression
- Computational Resources
- Nonlinear Optimization Options
- Covariance Matrix Types
- Displayed Output
- OUTPUT OUT= Data Set
- OUTEST= Data Set
- ODS Table Names
- Examples: COUNTREG Procedure
- References
- The DATASOURCE Procedure
- Overview: DATASOURCE Procedure
- Getting Started: 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
- Syntax: DATASOURCE Procedure
- PROC DATASOURCE Statement
- KEEP Statement
- DROP Statement
- KEEPEVENT Statement
- DROPEVENT Statement
- WHERE Statement
- RANGE Statement
- ATTRIBUTE Statement
- FORMAT Statement
- LABEL Statement
- LENGTH Statement
- RENAME Statement
- Details: DATASOURCE Procedure
- Examples: DATASOURCE Procedure
- BEA National Income and Product Accounts
- BLS Consumer Price Index Surveys
- BLS State and Area Employment, Hours, and Earnings Surveys
- DRI/McGraw-Hill Format CITIBASE Files
- DRI Data Delivery Service Database
- PC Format CITIBASE Database
- Quarterly COMPUSTAT Data Files
- Annual COMPUSTAT Data Files, V9.2 New Filetype CSAUC3
- CRSP Daily NYSE/AMEX Combined Stocks
- Data Elements Reference: DATASOURCE Procedure
- BEA Data Files
- BLS Data Files
- Global Insight DRI Data Files
- COMPUSTAT Data Files
- CRSP Stock Files
- FAME Information Services Databases
- Haver Analytics Data Files
- IMF Data Files
- OECD Data Files
- References
- The ENTROPY Procedure
- Overview: ENTROPY Procedure
- Getting Started: ENTROPY Procedure
- Simple Regression Analysis
- Using Prior Information
- Pure Inverse Problems
- Analyzing Multinomial Response Data
- Syntax: ENTROPY Procedure
- Functional Summary
- PROC ENTROPY Statement
- BOUNDS Statement
- BY Statement
- ID Statement
- MODEL Statement
- PRIORS Statement
- RESTRICT Statement
- TEST Statement
- WEIGHT Statement
- Details: ENTROPY Procedure
- Generalized Maximum Entropy
- Generalized Cross Entropy
- Normed Moment Generalized Maximum Entropy
- Maximum Entropy-Based Seemingly Unrelated Regression
- Generalized Maximum Entropy for Multinomial Discrete Choice Models
- Censored or Truncated Dependent Variables
- Information Measures
- Parameter Covariance For GCE
- Parameter Covariance For GCE-NM
- Statistical Tests
- Missing Values
- Input Data Sets
- Output Data Sets
- ODS Table Names
- ODS Graphics
- Examples: ENTROPY Procedure
- Nonnormal Error Estimation
- Unreplicated Factorial Experiments
- Censored Data Models in PROC ENTROPY
- Use of the PDATA= Option
- Illustration of ODS Graphics
- References
- The ESM Procedure
- Overview: ESM Procedure
- Getting Started: ESM Procedure
- Syntax: ESM Procedure
- Details: ESM Procedure
- Accumulation
- Missing Value Interpretation
- Transformations
- Parameter Estimation
- Missing Value Modeling Issues
- Forecasting
- Inverse Transformations
- Statistics of Fit
- Forecast Summation
- Data Set Output
- Printed Output
- ODS Table Names
- ODS Graphics
- Examples: ESM Procedure
- The EXPAND Procedure
- Overview: EXPAND Procedure
- Getting Started: 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
- Syntax: EXPAND Procedure
- Details: EXPAND Procedure
- Frequency Conversion
- Identifying Observations
- Range of Output Observations
- Extrapolation
- OBSERVED= Option
- Conversion Methods
- Transformation Operations
- OUT= Data Set
- OUTEST= Data Set
- ODS Graphics
- Examples: EXPAND Procedure
- Combining Monthly and Quarterly Data
- Illustration of ODS Graphics
- Interpolating Irregular Observations
- Using Transformations
- References
- The FORECAST Procedure
- Overview: FORECAST Procedure
- Getting Started: 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
- Syntax: FORECAST Procedure
- Details: FORECAST Procedure
- Missing Values
- Data Periodicity and Time Intervals
- Forecasting Methods
- Specifying Seasonality
- Data Requirements
- OUT= Data Set
- OUTEST= Data Set
- Examples: FORECAST Procedure
- References
- The LOAN Procedure
- Overview: LOAN Procedure
- Getting Started: LOAN Procedure
- Analyzing Fixed Rate Loans
- Analyzing Balloon Payment Loans
- Analyzing Adjustable Rate Loans
- Analyzing Buydown Rate Loans
- Loan Repayment Schedule
- Loan Comparison
- Syntax: LOAN Procedure
- Functional Summary
- PROC LOAN Statement
- FIXED Statement
- BALLOON Statement
- ARM Statement
- BUYDOWN Statement
- COMPARE Statement
- Details: LOAN Procedure
- Computational Details
- Loan Comparison Details
- OUT= Data Set
- OUTCOMP= Data Set
- OUTSUM= Data Set
- Printed Output
- ODS Table Names
- Examples: LOAN Procedure
- Discount Points for Lower Interest Rates
- Refinancing a Loan
- Prepayments on a Loan
- Output Data Sets
- Piggyback Loans
- References
- The MDC Procedure
- Overview: MDC Procedure
- Getting Started: 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
- Syntax: MDC Procedure
- Functional Summary
- PROC MDC Statement
- MDCDATA Statement
- BOUNDS Statement
- BY Statement
- ID Statement
- MODEL Statement
- NEST Statement
- NLOPTIONS Statement
- OUTPUT Statement
- RESTRICT Statement
- UTILITY Statement
- Details: MDC Procedure
- Multinomial Discrete Choice Modeling
- Multinomial Logit and Conditional Logit
- Heteroscedastic Extreme-Value Model
- Mixed Logit Model
- Multinomial Probit
- Nested Logit
- Decision Tree and Nested Logit
- Model Fit and Goodness-of-Fit Statistics
- OUTEST= Data Set
- ODS Table Names
- Examples: MDC Procedure
- Binary Data Modeling
- Conditional Logit and Data Conversion
- Correlated Choice Modeling
- Testing for Homoscedasticity of the Utility Function
- Choice of Time for Work Trips: Nested Logit Analysis
- Hausmans Specification and Likelihood Ratio Tests
- Acknowledgments: MDC Procedure
- References
- The MODEL Procedure
- Overview: MODEL Procedure
- Getting Started: MODEL Procedure
- Nonlinear Regression Analysis
- Nonlinear Systems Regression
- General Form Models
- Solving Simultaneous Nonlinear Equation Systems
- Monte Carlo Simulation
- Syntax: MODEL Procedure
- Functional Summary
- PROC MODEL Statement
- BOUNDS Statement
- BY Statement
- CONTROL Statement
- ENDOGENOUS Statement
- ERRORMODEL Statement
- ESTIMATE Statement
- EXOGENOUS Statement
- FIT Statement
- ID Statement
- INCLUDE Statement
- INSTRUMENTS Statement
- LABEL Statement
- MOMENT Statement
- OUTVARS Statement
- PARAMETERS Statement
- Programming Statements
- RANGE Statement
- RESET Statement
- RESTRICT Statement
- SOLVE Statement
- TEST Statement
- VAR Statement
- WEIGHT Statement
- Details: Estimation by the MODEL Procedure
- Estimation Methods
- Properties of the Estimates
- Minimization Methods
- Convergence Criteria
- Troubleshooting Convergence Problems
- Iteration History
- Computer Resource Requirements
- Testing for Normality
- Heteroscedasticity
- Testing for Autocorrelation
- Transformation of Error Terms
- Error Covariance Structure Specification
- Ordinary Differential Equations
- Restrictions and Bounds on Parameters
- Tests on Parameters
- Hausman Specification Test
- Chow Tests
- Profile Likelihood Confidence Intervals
- Choice of Instruments
- Autoregressive Moving-Average Error Processes
- Distributed Lag Models and the %PDL Macro
- Input Data Sets
- Output Data Sets
- ODS Table Names
- ODS Graphics
- Details: Simulation by the MODEL Procedure
- Solution Modes
- Multivariate t Distribution Simulation
- Alternate Distribution Simulation
- Mixtures of DistributionsCopulas
- Solution Mode Output
- Goal Seeking: Solving for Right-Hand-Side Variables
- Numerical Solution Methods
- Numerical Integration
- Limitations
- SOLVE Data Sets
- Programming Language Overview: MODEL Procedure
- Variables in the Model Program
- Equation Translations
- Derivatives
- Mathematical Functions
- Functions across Time
- Language Differences
- Storing Programs in Model Files
- Diagnostics and Debugging
- Analyzing the Structure of Large Models
- Examples: MODEL Procedure
- OLS Single Nonlinear Equation
- A Consumer Demand Model
- Vector AR(1) Estimation
- MA(1) Estimation
- Polynomial Distributed Lags by Using %PDL
- General Form Equations
- Spring and Damper Continuous System
- Nonlinear FIML Estimation
- Circuit Estimation
- Systems of Differential Equations
- Monte Carlo Simulation
- Cauchy Distribution Estimation
- Switching Regression Example
- Simulating from a Mixture of Distributions
- Simulated Method of MomentsSimple Linear Regression
- Simulated Method of MomentsAR(1) Process
- Simulated Method of MomentsStochastic Volatility Model
- Duration Data Model with Unobserved Heterogeneity
- EMM Estimation of a Stochastic Volatility Model
- Illustration of ODS Graphics
- References
- The PANEL Procedure
- Overview: PANEL Procedure
- Getting Started: PANEL Procedure
- Syntax: PANEL Procedure
- Functional Summary
- PROC PANEL Statement
- BY Statement
- CLASS Statement
- FLATDATA Statement
- ID Statement
- INSTRUMENT Statement
- LAG, ZLAG, XLAG, SLAG or CLAG Statement
- MODEL Statement
- OUTPUT Statement
- RESTRICT Statement
- TEST Statement
- Details: PANEL Procedure
- Missing Values
- Computational Resources
- Restricted Estimates
- Notation
- The One-Way Fixed-Effects Model
- The Two-Way Fixed-Effects Model
- Balanced Panels
- Unbalanced Panels
- Between Estimators
- Pooled Estimator
- The One-Way Random-Effects Model
- The Two-Way Random-Effects Model
- Parks Method (Autoregressive Model)
- Da Silva Method (Variance-Component Moving-Average Model)
- Dynamic Panel Estimator
- Specification Testing For Dynamic Panel
- Instrument Choice
- Linear Hypothesis Testing
- Heteroscedasticity-Corrected Covariance Matrices
- R-Square
- Specification Tests
- Troubleshooting
- ODS Graphics
- The OUTPUT OUT= Data Set
- The OUTEST= Data Set
- The OUTTRANS= Data Set
- Printed Output
- ODS Table Names
- Example: PANEL Procedure
- Analyzing Demand for Liquid Assets
- The Airline Cost Data: Fixtwo Model
- ODS Graphics Plots
- The Airline Cost Data: Further Analysis
- The Airline Cost Data: Random-Effects Models
- Using the FLATDATA Statement
- The Cigarette Sales Data: Dynamic Panel Estimation with GMM
- References: PANEL Procedure
- The PDLREG Procedure
- Overview: PDLREG Procedure
- Getting Started: PDLREG Procedure
- Syntax: PDLREG Procedure
- Functional Summary
- PROC PDLREG Statement
- BY Statement
- MODEL Statement
- OUTPUT Statement
- RESTRICT Statement
- Details: PDLREG Procedure
- Missing Values
- Polynomial Distributed Lag Estimation
- Autoregressive Error Model Estimation
- OUT= Data Set
- Printed Output
- ODS Table Names
- Examples: PDLREG Procedure
- References
- The QLIM Procedure
- Overview: QLIM Procedure
- Getting Started: QLIM Procedure
- Syntax: QLIM Procedure
- Functional Summary
- PROC QLIM Statement
- BOUNDS Statement
- BY Statement
- CLASS Statement
- ENDOGENOUS Statement
- HETERO Statement
- INIT Statement
- MODEL Statement
- NLOPTIONS Statement
- OUTPUT Statement
- RESTRICT Statement
- TEST Statement
- WEIGHT Statement
- Details: QLIM Procedure
- Ordinal Discrete Choice Modeling
- Limited Dependent Variable Models
- Stochastic Frontier Production and Cost Models
- Heteroscedasticity and Box-Cox Transformation
- Bivariate Limited Dependent Variable Modeling
- Selection Models
- Multivariate Limited Dependent Models
- Tests on Parameters
- Output to SAS Data Set
- OUTEST= Data Set
- Naming
- ODS Table Names
- Examples: QLIM Procedure
- Ordered Data Modeling
- Tobit Analysis
- Bivariate Probit Analysis
- Sample Selection Model
- Sample Selection Model with Truncation and Censoring
- Types of Tobit Models
- Stochastic Frontier Models
- References
- The SIMILARITY Procedure
- Overview: SIMILARITY Procedure
- Getting Started: SIMILARITY Procedure
- Syntax: SIMILARITY Procedure
- Functional Summary
- PROC SIMILARITY Statement
- BY Statement
- FCMPOPT Statement
- ID Statement
- INPUT Statement
- TARGET Statement
- Details: SIMILARITY Procedure
- Accumulation
- Missing Value Interpretation
- Zero Value Interpretation
- Time Series Transformation
- Time Series Differencing
- Time Series Missing Value Trimming
- Time Series Descriptive Statistics
- Input and Target Sequences
- Sliding Sequences
- Time Warping
- Sequence Normalization
- Sequence Scaling
- Similarity Measures
- User-Defined Functions and Subroutines
- Output Data Sets
- OUT= Data Set
- OUTMEASURE= Data Set
- OUTSUM= Data Set
- OUTSEQUENCE= Data Set
- OUTPATH= Data Set
- STATUS Variable Values
- Printed Output
- ODS Tables Names
- ODS Graphics
- Examples: SIMILARITY Procedure
- Accumulating Transactional Data into Time Series Data
- Similarity Analysis
- Sliding Similarity Analysis
- Searching for Historical Analogies
- References
- The SIMLIN Procedure
- Overview: SIMLIN Procedure
- Getting Started: SIMLIN Procedure
- Syntax: SIMLIN Procedure
- Functional Summary
- PROC SIMLIN Statement
- BY Statement
- ENDOGENOUS Statement
- EXOGENOUS Statement
- ID Statement
- LAGGED Statement
- OUTPUT Statement
- Details: SIMLIN Procedure
- Defining the Structural Form
- Computing the Reduced Form
- Dynamic Multipliers
- Multipliers for Higher Order Lags
- EST= Data Set
- DATA= Data Set
- OUTEST= Data Set
- OUT= Data Set
- Printed Output
- ODS Table Names
- Examples: SIMLIN Procedure
- References
- The SPECTRA Procedure
- Overview: SPECTRA Procedure
- Getting Started: SPECTRA Procedure
- Syntax: SPECTRA Procedure
- Details: SPECTRA Procedure
- Input Data
- Missing Values
- Computational Method
- Kernels
- White Noise Test
- Transforming Frequencies
- OUT= Data Set
- Printed Output
- ODS Table Names: SPECTRA procedure
- Examples: SPECTRA Procedure
- References
- The STATESPACE Procedure
- Overview: STATESPACE Procedure
- Getting Started: STATESPACE Procedure
- Syntax: STATESPACE Procedure
- Functional Summary
- PROC STATESPACE Statement
- BY Statement
- FORM Statement
- ID Statement
- INITIAL Statement
- RESTRICT Statement
- VAR Statement
- Details: STATESPACE Procedure
- Missing Values
- Stationarity and Differencing
- Preliminary Autoregressive Models
- Canonical Correlation Analysis
- Parameter Estimation
- Forecasting
- Relation of ARMA and State Space Forms
- OUT= Data Set
- OUTAR= Data Set
- OUTMODEL= Data Set
- Printed Output
- ODS Table Names
- Examples: STATESPACE Procedure
- References
- The SYSLIN Procedure
- Overview: SYSLIN Procedure
- Getting Started: 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
- Syntax: SYSLIN Procedure
- Functional Summary
- PROC SYSLIN Statement
- BY Statement
- ENDOGENOUS Statement
- IDENTITY Statement
- INSTRUMENTS Statement
- MODEL Statement
- OUTPUT Statement
- RESTRICT Statement
- SRESTRICT Statement
- STEST Statement
- TEST Statement
- VAR Statement
- WEIGHT Statement
- Details: SYSLIN Procedure
- Input Data Set
- Estimation Methods
- ANOVA Table for Instrumental Variables Methods
- The R-Square Statistics
- Computational Details
- Missing Values
- OUT= Data Set
- OUTEST= Data Set
- OUTSSCP= Data Set
- Printed Output
- ODS Table Names
- ODS Graphics
- Examples: SYSLIN Procedure
- Kleins Model I Estimated with LIML and 3SLS
- Grunfelds Model Estimated with SUR
- Illustration of ODS Graphics
- References
- The TIMESERIES Procedure
- Overview: TIMESERIES Procedure
- Getting Started: TIMESERIES Procedure
- Syntax: TIMESERIES Procedure
- Functional Summary
- PROC TIMESERIES Statement
- BY Statement
- CORR Statement
- CROSSCORR Statement
- DECOMP Statement
- ID Statement
- SEASON Statement
- TREND Statement
- VAR and CROSSVAR Statements
- Details: TIMESERIES Procedure
- Accumulation
- Missing Value Interpretation
- Time Series Transformation
- Time Series Differencing
- Descriptive Statistics
- Seasonal Decomposition
- Correlation Analysis
- Cross-Correlation Analysis
- Data Set Output
- OUT= Data Set
- OUTCORR= Data Set
- OUTCROSSCORR= Data Set
- OUTDECOMP= Data Set
- OUTSEASON= Data Set
- OUTSUM= Data Set
- OUTTREND= Data Set
- STATUS Variable Values
- Printed Output
- ODS Table Names
- ODS Graphics Names
- Examples: TIMESERIES Procedure
- Accumulating Transactional Data into Time Series Data
- Trend and Seasonal Analysis
- Illustration of ODS Graphics
- References
- The TSCSREG Procedure
- Overview: The TSCSREG Procedure
- Getting Started: The TSCSREG Procedure
- Specifying the Input Data
- Unbalanced Data
- Specifying the Regression Model
- Estimation Techniques
- Introductory Example
- Syntax: The TSCSREG Procedure
- Details: The TSCSREG Procedure
- Examples: The TSCSREG Procedure
- Acknowledgments: TSCSREG Procedure
- References: TSCSREG Procedure
- The UCM Procedure
- Overview: UCM Procedure
- Getting Started: UCM Procedure
- Syntax: UCM Procedure
- Functional Summary
- PROC UCM Statement
- AUTOREG Statement
- BLOCKSEASON Statement
- BY Statement
- CYCLE Statement
- DEPLAG Statement
- ESTIMATE Statement
- FORECAST Statement
- ID Statement
- IRREGULAR Statement
- LEVEL Statement
- MODEL Statement
- NLOPTIONS Statement
- OUTLIER Statement
- RANDOMREG Statement
- SEASON Statement
- SLOPE Statement
- SPLINEREG Statement
- SPLINESEASON Statement
- Details: UCM Procedure
- An Introduction to Unobserved Component Models
- The UCMs as State Space Models
- Outlier Detection
- Missing Values
- Parameter Estimation
- Computational Issues
- Displayed Output
- Statistical Graphics
- ODS Table Names
- ODS Graph Names
- OUTFOR= Data Set
- OUTEST= Data Set
- Statistics of Fit
- Examples: UCM Procedure
- The Airline Series Revisited
- Variable Star Data
- Modeling Long Seasonal Patterns
- Modeling Time-Varying Regression Effects Using the RANDOMREG Statement
- Trend Removal Using the Hodrick-Prescott Filter
- Using Splines to Incorporate Nonlinear Effects
- Detection of Level Shift
- ARIMA Modeling
- References
- The VARMAX Procedure
- Overview: VARMAX Procedure
- Getting Started: 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
- Syntax: VARMAX Procedure
- Functional Summary
- PROC VARMAX Statement
- BY Statement
- CAUSAL Statement
- COINTEG Statement
- ID Statement
- MODEL Statement
- GARCH Statement
- NLOPTIONS Statement
- OUTPUT Statement
- RESTRICT Statement
- TEST Statement
- Details: VARMAX Procedure
- Missing Values
- VARMAX Model
- Dynamic Simultaneous Equations Modeling
- Impulse Response Function
- Forecasting
- Tentative Order Selection
- VAR and VARX Modeling
- Bayesian VAR and VARX Modeling
- VARMA and VARMAX Modeling
- Model Diagnostic Checks
- Cointegration
- Vector Error Correction Modeling
- I(2) Model
- Multivariate GARCH Modeling
- Output Data Sets
- OUT= Data Set
- OUTEST= Data Set
- OUTHT= Data Set
- OUTSTAT= Data Set
- Printed Output
- ODS Table Names
- ODS Graphics
- Computational Issues
- Examples: VARMAX Procedure
- Analysis of U.S. Economic Variables
- Analysis of German Economic Variables
- Numerous Examples
- Illustration of ODS Graphics
- References
- The X11 Procedure
- Overview: X11 Procedure
- Getting Started: X11 Procedure
- Syntax: X11 Procedure
- Functional Summary
- PROC X11 Statement
- ARIMA Statement
- BY Statement
- ID Statement
- MACURVES Statement
- MONTHLY Statement
- OUTPUT Statement
- PDWEIGHTS Statement
- QUARTERLY Statement
- SSPAN Statement
- TABLES Statement
- VAR Statement
- Details: X11 Procedure
- Historical Development of X-11
- Implementation of the X-11 Seasonal Adjustment Method
- Computational Details for Sliding Spans Analysis
- Data Requirements
- Missing Values
- Prior Daily Weights and Trading-Day Regression
- Adjustment for Prior Factors
- The YRAHEADOUT Option
- Effect of Backcast and Forecast Length
- Details of Model Selection
- OUT= Data Set
- The OUTSPAN= Data Set
- OUTSTB= Data Set
- OUTTDR= Data Set
- Printed Output
- ODS Table Names
- Examples: X11 Procedure
- References
- The X12 Procedure
- Overview: X12 Procedure
- Getting Started: X12 Procedure
- Syntax: X12 Procedure
- Functional Summary
- PROC X12 Statement
- BY Statement
- ID Statement
- EVENT Statement
- INPUT Statement
- ADJUST Statement
- ARIMA Statement
- ESTIMATE Statement
- FORECAST Statement
- IDENTIFY Statement
- AUTOMDL Statement
- OUTPUT Statement
- OUTLIER Statement
- REGRESSION Statement
- TABLES Statement
- TRANSFORM Statement
- USERDEFINED Statement
- VAR Statement
- X11 Statement
- Details: X12 Procedure
- Missing Values
- Combined Test for the Presence of Identifiable Seasonality
- Computations
- Displayed Output/ODS Table Names/OUTPUT Tablename Keywords
- ODS Graphics
- Special Data Sets
- Examples: X12 Procedure
- Model Identification
- Model Estimation
- Seasonal Adjustment
- RegARIMA Automatic Model Selection
- Automatic Outlier Detection
- User-Defined Regressors
- MDLINFOIN= and MDLINFOOUT= Data Sets
- Setting Regression Parameters
- Illustration of ODS Graphics
- Acknowledgments: X12 Procedure
- References
- Data Access Engines
- The SASECRSP Interface Engine
- Overview: SASECRSP Interface Engine
- Getting Started: SASECRSP Interface Engine
- Structure of a SAS Data Set Containing Time Series Data
- Reading CRSP Data Files
- Using the SAS DATA Step
- Using SAS Procedures
- Using CRSP Date Formats, Informats, and Functions
- Syntax: SASECRSP Interface Engine
- Details: SASECRSP Interface Engine
- Using the Inset Option
- The SAS Output Data Set
- Understanding CRSP Date Formats, Informats, and Functions
- Examples: SASECRSP Interface Engine
- Specifying PERMNOs and RANGE on the LIBNAME Statement
- Using the LIBNAME Statement to Access All Keys
- Accessing One PERMNO Using No RANGE
- Specifying Keys Using the INSET= Option
- Specifying Ranges for Individual Keys with the INSET= Option
- Converting Dates Using the CRSP Date Functions
- Comparing Different Ways of Accessing CCM Data
- Comparing PERMNO and GVKEY Access of CRSP Stock Data
- Using Fiscal Date Range Restriction
- Using Different Types of Range Restrictions in the INSET
- Using INSET Ranges with the LIBNAME RANGE Option
- Data Elements Reference: SASECRSP Interface Engine
- References
- Acknowledgments
- The SASEFAME Interface Engine
- Overview: SASEFAME Interface Engine
- Getting Started: SASEFAME Interface Engine
- Structure of a SAS Data Set Containing Time Series Data
- Reading and Converting FAME Database Time Series
- Using the SAS DATA Step
- Using SAS Procedures
- Using the SAS Windowing Environment
- Using Remote FAME Data Access
- Creating Views of Time Series Using SASEFAME LIBNAME Options
- Syntax: SASEFAME Interface Engine
- Details: SASEFAME Interface Engine
- Examples: SASEFAME Interface Engine
- Converting an Entire FAME Database
- Reading Time Series from the FAME Database
- Writing Time Series to the SAS Data Set
- Limiting the Time Range of Data
- Creating a View Using the SQL Procedure and SASEFAME
- Reading Other FAME Data Objects with the FAMEOUT= Option
- Remote FAME Access Using FAME CHLI
- Selecting Time Series Using CROSSLIST= Option and KEEP Statement
- Selecting Time Series Using CROSSLIST= Option with a FAME Namelist of Tickers
- Selecting Time Series Using CROSSLIST= Option with INSET= and WHERE=TICK
- Selecting Series Using FAMEOUT= Option
- References: SASEFAME Interface Engine
- Acknowledgments
- The SASEHAVR Interface Engine
- Overview: SASEHAVR Interface Engine
- Getting Started: SASEHAVR Interface Engine
- Structure of a SAS Data Set Containing Time Series Data
- Reading and Converting HAVER DLX Time Series
- Using the SAS DATA Step
- Using the SAS Windowing Environment
- Syntax: SASEHAVR Interface Engine
- Details: SASEHAVR Interface Engine
- Examples: SASEHAVR Interface Engine
- Examining the Contents of a HAVER Database
- Viewing Quarterly Time Series from a HAVER Database
- Viewing Monthly Time Series from a HAVER Database
- Viewing Weekly Time Series from a HAVER Database
- Viewing Daily Time Series from a HAVER Database
- Limiting the Range of Time Series from a HAVER Database
- Using the WHERE Statement to Subset Time Series from a HAVER Database
- Using the KEEP Option to Subset Time Series from a HAVER Database
- Using the SOURCE Option to Subset Time Series from a HAVER Database
- Using the GROUP Option to Subset Time Series from a HAVER Database
- Data Elements Reference: SASEHAVR Interface Engine
- References
- Acknowledgments
- Time Series Forecasting System
- Overview of the Time Series Forecasting System
- Getting Started with Time Series Forecasting
- The Time Series Forecasting Window
- Outline of the Forecasting Process
- Specify the Input Data Set
- Provide a Valid Time ID Variable
- Select and Fit a Forecasting Model for Each Series
- Produce the Forecasts
- Save Your Work
- Summary
- The Input Data Set
- Automatic Model Fitting Window
- Produce Forecasts Window
- Forecasting Projects
- Develop Models Window
- Model Viewer
- Creating Time ID Variables
- Creating a Time ID Value from a Starting Date and Frequency
- Using Observation Numbers as the Time ID
- Creating a Time ID from Other Dating Variables
- Specifying Forecasting Models
- Series Diagnostics
- Models to Fit Window
- Automatic Model Selection
- Smoothing Model Specification Window
- ARIMA Model Specification Window
- Factored ARIMA Model Specification Window
- Custom Model Specification Window
- Editing the Model Selection List
- Forecast Combination Model Specification Window
- Incorporating Forecasts from Other Sources
- Choosing the Best Forecasting Model
- Time Series Viewer Features
- Model Viewer Prediction Error Analysis
- The Model Selection Criterion
- Sorting and Selecting Models
- Comparing Models
- Controlling the Period of Evaluation and Fit
- Refitting and Reevaluating Models
- Using Hold-out Samples
- Using Predictor Variables
- Linear Trend
- Time Trend Curves
- Regressors
- Adjustments
- Dynamic Regressor
- Interventions
- The Intervention Specification Window
- Specifying a Trend Change Intervention
- Specifying a Level Change Intervention
- Modeling Complex Intervention Effects
- Fitting the Intervention Model
- Limitations of Intervention Predictors
- Seasonal Dummies
- References
- Command Reference
- Window Reference
- Overview
- Adjustments Selection Window
- AR/MA Polynomial Specification Window
- ARIMA Model Specification Window
- ARIMA Process Specification Window
- Automatic Model Fitting Window
- Automatic Model Fitting Results Window
- Automatic Model Selection Options Window
- Custom Model Specification Window
- Data Set Selection Window
- Default Time Ranges Window
- Develop Models Window
- Differencing Specification Window
- Dynamic Regression Specification Window
- Dynamic Regressors Selection Window
- Error Model Options Window
- External Forecast Model Specification Window
- Factored ARIMA Model Specification Window
- Forecasting Process Details
- Forecasting Process Summary
- Parameter Estimation
- Model Evaluation
- Forecasting
- Forecast Combination Models
- External or User-Supplied Forecasts
- Adjustments
- Series Transformations
- Smoothing Models
- Smoothing Model Calculations
- Missing Values
- Predictions and Prediction Errors
- Smoothing Weights
- Equations for the Smoothing Models
- ARIMA Models
- Predictor Series
- Series Diagnostic Tests
- Statistics of Fit
- References
- Investment Analysis
- Overview
- Portfolios
- The File Menu
- Tasks
- Creating a New Portfolio
- Saving a Portfolio
- Opening an Existing Portfolio
- Saving a Portfolio to a Different Name
- Selecting Investments within a Portfolio
- Dialog and Utility Guide
- Investments
- The Investment Menu
- Tasks
- Loan Tasks
- Specifying Savings Terms to Create an Account Summary
- Depreciation Tasks
- Bond Tasks
- Generic Cashflow Tasks
- Dialog Box Guide
- Computations
- Analyses
- The Analyze Menu
- Tasks
- Performing Time Value Analysis
- Computing an Internal Rate of Return
- Performing a Benefit-Cost Ratio Analysis
- Computing a Uniform Periodic Equivalent
- Performing a Breakeven Analysis
- Dialog Box Guide
- Details
Index here
| Product | Release |
|---|---|
| SAS/ETS | 9.2 |
- Type
- Usage and Reference
- Copyright Date
- March 2008
- Last Updated
- 16Jan2009
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| Previous Page | | | Next Page |
| What’s New in SAS/ETS |
| TIMESERIES Procedure |
The new TIMESERIES procedure analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format. The procedure can perform trend and seasonal analysis on the transactions. After the transactional data are accumulated, time domain and frequency domain analysis can be performed on the resulting time series. The procedure produces numerous graphical results related to time series analysis.
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Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
