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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| Introduction |
| Spectral Analysis |
The SPECTRA procedure provides spectral analysis and cross-spectral analysis of time series. The SPECTRA procedure includes the following features:
efficient calculation of periodogram and smoothed periodogram using fast finite Fourier transform and Chirp-Z algorithms
multiple spectral analysis, including raw and smoothed spectral and cross-spectral function estimates, with user-specified window weights
choice of kernel for smoothing
output of the following spectral estimates to a SAS data set:
Fourier sine and cosine coefficients
periodogram
smoothed periodogram
cospectrum
quadrature spectrum
amplitude
phase spectrum
squared coherency
Fisher’s Kappa and Bartlett’s Kolmogorov-Smirnov test statistic for testing a null hypothesis of white noise
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Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
