Resources

• Products & Solutions
• System Requirements
• Samples
• Install Center
• Third-Party Software Reference
• Documentation
  • What's New in SAS
  • Product Index A-Z
  • SAS Viya
  • SAS 9.4
  • SAS 9.3 and Earlier
• Papers
• Focus Areas

SAS/ETS 14.1 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 HPCDM PROC HPCOPULA PROC HPCOUNTREG PROC HPPANEL PROC HPQLIM PROC HPSEVERITY 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 X13 SASECRSP Interface Engine SASEFAME Interface Engine SASEFRED Interface Engine SASEHAVR Interface Engine SASEQUAN Interface Engine SASEXCCM Interface Engine SASEXFSD Interface Engine

---

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 8.1: Simulated IMA Model
• Example 8.2: Seasonal Model for the Airline Series
• Example 8.3: Model for Series J Data from Box and Jenkins
• Example 8.4: An Intervention Model for Ozone Data
• Example 8.5: Using Diagnostics to Identify ARIMA Models
• Example 8.6: Detection of Level Changes in the Nile River Data
• Example 8.7: Iterative Outlier Detection

---

AUTOREG Procedure

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

• Example 9.1: Analysis of Real Output Series
• Example 9.2: Comparing Estimates and Models
• Example 9.3: Lack-of-Fit Study
• Example 9.4: Missing Values
• Example 9.5: Money Demand Model
• Example 9.6: Estimation of ARCH(2) Process
• Example 9.7: Estimation of GARCH-Type Models
• Example 9.8: Illustration of ODS Graphics

---

COMPUTAB Procedure

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

• Example 10.1: Using Programming Statements
• Example 10.2: Enhancing a Report
• Example 10.3: Comparison of Actual and Budget
• Example 10.4: Consolidations
• Example 10.5: Creating an Output Data Set
• Example 10.6: A What-If Market Analysis
• Example 10.7: Cash Flows

---

COPULA Procedure

• Getting Started: COPULA Procedure

• Example 11.1: Copula Based VaR Estimation
• Example 11.2: Simulating Default Times

---

COUNTREG Procedure

• Getting Started: COUNTREG Procedure

• Example 12.1: Basic Models
• Example 12.2: ZIP and ZINB Models for Data That Exhibit Extra Zeros
• Example 12.3: Variable Selection

---

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 13.1: BEA National Income and Product Accounts
• Example 13.2: BLS Consumer Price Index Surveys
• Example 13.3: BLS State and Area Employment, Hours, and Earnings Surveys
• Example 13.4: DRI/McGraw-Hill Format CITIBASE Files
• Example 13.5: DRI Data Delivery Service Database
• Example 13.6: PC Format CITIBASE Database
• Example 13.7: Quarterly COMPUSTAT Data Files
• Example 13.8: Annual COMPUSTAT Data Files, V9.2 New Filetype CSAUC3
• Example 13.9: CRSP Daily NYSE/AMEX Combined Stocks

---

ENTROPY Procedure

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

• Example 14.1: Nonnormal Error Estimation
• Example 14.2: Unreplicated Factorial Experiments
• Example 14.3: Censored Data Models in PROC ENTROPY
• Example 14.4: Use of the PDATA= Option
• Example 14.5: Illustration of ODS Graphics

---

ESM Procedure

• Getting Started: ESM Procedure

• Example 15.1: Forecasting of Time Series Data
• Example 15.2: Forecasting of Transactional Data
• Example 15.3: Specifying the Forecasting Model
• Example 15.4: Extending the Independent Variables for Multivariate Forecasts
• Example 15.5: Illustration of ODS Graphics

---

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 16.1: Combining Monthly and Quarterly Data
• Example 16.2: Illustration of ODS Graphics
• Example 16.3: Interpolating Irregular Observations
• Example 16.4: Using Transformations

---

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 17.1: Forecasting Auto Sales
• Example 17.2: Forecasting Retail Sales
• Example 17.3: Forecasting Petroleum Sales

---

HPCDM Procedure

• Estimating a Simple Compound Distribution Model
• Analyzing the Effect of Parameter Uncertainty on the Compound Distribution
• Scenario Analysis

• Example 18.1: Estimating the Probability Distribution of Insurance Payments
• Example 18.2: Using Externally Simulated Count Data

---

HPCOPULA Procedure

• Getting Started: HPCOPULA Procedure

• Example 19.1: Simulating Default Times

---

HPCOUNTREG Procedure

• Getting Started: HPCOUNTREG Procedure

• Example 20.1: High-Performance Zero-Inflated Poisson Model

---

HPPANEL Procedure

• Getting Started: HPPANEL Procedure

• Example 21.1: One-Way Random-Effects High-Performance Model

---

HPQLIM Procedure

• Getting Started: HPQLIM Procedure

• Example 22.1: High-Performance Model with Censoring
• Example 22.2: Bayesian High-Performance Model with Censoring

---

HPSEVERITY 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: Fitting a Scaled Tweedie Model with Regressors
• Example 23.5: Fitting Distributions to Interval-Censored Data
• Example 23.6: Benefits of Distributed and Multithreaded Computing
• Example 23.7: Estimating Parameters Using Cramér-von Mises Estimator
• Example 23.8: Defining a Finite Mixture Model That Has a Scale Parameter
• Example 23.9: Predicting Mean and Value-at-Risk by Using Scoring Functions
• Example 23.10: Scale Regression with Rich Regression Effects

---

LOAN Procedure

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

• Example 24.1: Discount Points for Lower Interest Rates
• Example 24.2: Refinancing a Loan
• Example 24.3: Prepayments on a Loan
• Example 24.4: Output Data Sets
• Example 24.5: Piggyback Loans

---

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 25.1: Binary Data Modeling
• Example 25.2: Conditional Logit and Data Conversion
• Example 25.3: Correlated Choice Modeling
• Example 25.4: Testing for Homoscedasticity of the Utility Function
• Example 25.5: Choice of Time for Work Trips: Nested Logit Analysis
• Example 25.6: Hausman’s Specification Test
• Example 25.7: Likelihood Ratio Test

---

MODEL Procedure

• Nonlinear Regression Analysis
• Nonlinear Systems Regression
• General Form Models
• Solving Simultaneous Nonlinear Equation Systems
• Working with Model Files
• Monte Carlo Simulation

• Example 26.1: OLS Single Nonlinear Equation
• Example 26.2: A Consumer Demand Model
• Example 26.3: Vector AR(1) Estimation
• Example 26.4: MA(1) Estimation
• Example 26.5: Polynomial Distributed Lags by Using %PDL
• Example 26.6: General Form Equations
• Example 26.7: Spring and Damper Continuous System
• Example 26.8: Nonlinear FIML Estimation
• Example 26.9: Circuit Estimation
• Example 26.10: Systems of Differential Equations
• Example 26.11: Monte Carlo Simulation
• Example 26.12: Cauchy Distribution Estimation
• Example 26.13: Switching Regression Example
• Example 26.14: Simulating from a Mixture of Distributions
• Example 26.15: Simulated Method of Moments—Simple Linear Regression
• Example 26.16: Simulated Method of Moments—AR(1) Process
• Example 26.17: Simulated Method of Moments—Stochastic Volatility Model
• Example 26.18: Duration Data Model with Unobserved Heterogeneity
• Example 26.19: EMM Estimation of a Stochastic Volatility Model
• Example 26.20: Illustration of ODS Graphics
• Example 26.21: A Translog Cost Function and Derived Demands
• Example 26.22: Reducing Parameter Variance in a Tree Biomass Model

---

PANEL Procedure

• Getting Started: PANEL Procedure

• Example 27.1: Analyzing Demand for Liquid Assets
• Example 27.2: The Airline Cost Data: Fixtwo Model
• Example 27.3: The Airline Cost Data: Further Analysis
• Example 27.4: The Airline Cost Data: Random-Effects Models
• Example 27.5: Panel Study of Income Dynamics (PSID): Hausman-Taylor Models
• Example 27.6: The Cigarette Sales Data: Dynamic Panel Estimation with GMM
• Example 27.7: Using the FLATDATA Statement

---

PDLREG Procedure

• Introductory Example

• Example 28.1: Industrial Conference Board Data
• Example 28.2: Money Demand Model

---

QLIM Procedure

• Introductory Example: Binary Probit and Logit Models

• Example 29.1: Ordered Data Modeling
• Example 29.2: Tobit Analysis
• Example 29.3: Bivariate Probit Analysis
• Example 29.4: Sample Selection Model
• Example 29.5: Sample Selection Model with Truncation and Censoring
• Example 29.6: Types of Tobit Models
• Example 29.7: Stochastic Frontier Models
• Example 29.8: Bayesian Modeling

• Example : Introductory Example: Binary Probit and Logit Models

---

SEVERITY Procedure

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

• Example 30.1: Defining a Model for Gaussian Distribution
• Example 30.2: Defining a Model for the Gaussian Distribution with a Scale Parameter
• Example 30.3: Defining a Model for Mixed-Tail Distributions
• Example 30.4: Estimating Parameters Using Cramér-von Mises Estimator
• Example 30.5: Fitting a Scaled Tweedie Model with Regressors
• Example 30.6: Fitting Distributions to Interval-Censored Data
• Example 30.7: Defining a Finite Mixture Model That Has a Scale Parameter
• Example 30.8: Predicting Mean and Value-at-Risk by Using Scoring Functions
• Example 30.9: Scale Regression with Rich Regression Effects

---

SIMILARITY Procedure

• Getting Started: SIMILARITY Procedure

• Example 31.1: Accumulating Transactional Data into Time Series Data
• Example 31.2: Similarity Analysis
• Example 31.3: Sliding Similarity Analysis
• Example 31.4: Searching for Historical Analogies
• Example 31.5: Clustering Time Series

---

SIMLIN Procedure

• Prediction and Simulation

• Example 32.1: Simulating Klein’s Model I
• Example 32.2: Multipliers for a Third-Order System

---

SPECTRA Procedure

• Getting Started: SPECTRA Procedure

• Example 33.1: Spectral Analysis of Sunspot Activity
• Example 33.2: Cross-Spectral Analysis

---

SSM Procedure

• Getting Started: SSM Procedure

• Example 34.1: Bivariate Basic Structural Model
• Example 34.2: Panel Data: Random-Effects and Autoregressive Models
• Example 34.3: Backcasting, Forecasting, and Interpolation
• Example 34.4: Longitudinal Data: Smoothing of Repeated Measures
• Example 34.5: A User-Defined Trend Model
• Example 34.6: Model with Multiple ARIMA Components
• Example 34.7: Dynamic Factor Modeling
• Example 34.8: Diagnostic Plots and Structural Break Analysis
• Example 34.9: Longitudinal Data: Variable Bandwidth Smoothing
• Example 34.10: A Transfer Function Model for the Gas Furnace Data
• Example 34.11: Panel Data: Dynamic Panel Model for the Cigar Data
• Example 34.12: Multivariate Modeling: Long-Term Temperature Trends
• Example 34.13: Bivariate Model: Sales of Mink and Muskrat Furs
• Example 34.14: Factor Model: Now-Casting the US Economy
• Example 34.15: Longitudinal Data: Lung Function Analysis

---

STATESPACE Procedure

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

• Example 35.1: Series J from Box and Jenkins

---

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 36.1: Klein’s Model I Estimated with LIML and 3SLS
• Example 36.2: Grunfeld’s Model Estimated with SUR
• Example 36.3: Illustration of ODS Graphics

---

TIMEDATA Procedure

• Getting Started: TIMEDATA Procedure

• Example 37.1: Accumulating Transactional Data into Time Series Data
• Example 37.2: Using User-Defined Functions and Subroutines
• Example 37.3: Using Auxiliary Data Sets with PROC TIMEDATA

---

TIMEID Procedure

• Getting Started: TIMEID Procedure

• Example 38.1: Examining a Weekly Time ID Variable
• Example 38.2: Inferring a Date Interval
• Example 38.3: Examining Multiple BY Groups

---

TIMESERIES Procedure

• Getting Started: TIMESERIES Procedure

• Example 39.1: Accumulating Transactional Data into Time Series Data
• Example 39.2: Trend and Seasonal Analysis
• Example 39.3: Illustration of ODS Graphics
• Example 39.4: Illustration of Spectral Analysis
• Example 39.5: Illustration of Singular Spectrum Analysis

---

UCM Procedure

• A Seasonal Series with Linear Trend

• Example 41.1: The Airline Series Revisited
• Example 41.2: Variable Star Data
• Example 41.3: Modeling Long Seasonal Patterns
• Example 41.4: Modeling Time-Varying Regression Effects
• Example 41.5: Trend Removal Using the Hodrick-Prescott Filter
• Example 41.6: Using Splines to Incorporate Nonlinear Effects
• Example 41.7: Detection of Level Shift
• Example 41.8: ARIMA Modeling

---

VARMAX Procedure

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

• Example 42.1: Analysis of United States Economic Variables
• Example 42.2: Analysis of German Economic Variables
• Example 42.3: Analysis of Restricted Cointegrated Systems
• Example 42.4: Analysis of Euro Foreign Exchange Reference Rates
• Example 42.5: Numerous Examples
• Example 42.6: Illustration of ODS Graphics

---

X11 Procedure

• Basic Seasonal Adjustment
• X-11-ARIMA

• Example 43.1: Component Estimation—Monthly Data
• Example 43.2: Components Estimation—Quarterly Data
• Example 43.3: Outlier Detection and Removal

---

X13 Procedure

• Basic Seasonal Adjustment

• Example 45.1: ARIMA Model Identification
• Example 45.2: Model Estimation
• Example 45.3: Seasonal Adjustment
• Example 45.4: RegARIMA Automatic Model Selection
• Example 45.5: Automatic Outlier Detection
• Example 45.6: User-Defined Regressors
• Example 45.7: MDLINFOIN= and MDLINFOOUT= Data Sets
• Example 45.8: Setting Regression Parameters
• Example 45.9: Creating an MDLINFO= Data Set for Use with the PICKMDL Statement
• Example 45.10: Illustration of ODS Graphics
• Example 45.11: AUXDATA= Data Set

---

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 46.1: Specifying PERMNOs and Range in the LIBNAME Statement
• Example 46.2: Using the LIBNAME Statement to Access All Keys
• Example 46.3: Accessing One PERMNO without the RANGE= Option
• Example 46.4: Specifying Keys by Using the INSET= Option
• Example 46.5: Specifying Ranges for Individual Keys with the INSET= Option
• Example 46.6: Converting Dates by Using the CRSP Date Functions

---

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 47.1: Converting an Entire Fame Database
• Example 47.2: Reading Time Series from the Fame Database
• Example 47.3: Writing Time Series to the SAS Data Set
• Example 47.4: Limiting the Time Range of Data
• Example 47.5: Creating a View Using the SQL Procedure and the SASEFAME Engine
• Example 47.6: Reading Other Fame Data Objects with the FAMEOUT= Option
• Example 47.7: Remote Fame Access by Using Fame CHLI
• Example 47.8: Selecting Time Series by Using the CROSSLIST= Option and KEEP Statement
• Example 47.9: Selecting Time Series by Using the CROSSLIST= Option and Fame Namelist
• Example 47.10: Selecting Time Series by Using the CROSSLIST= Option and WHERE=TICK
• Example 47.11: Selecting Boolean Case Series with the FAMEOUT= Option
• Example 47.12: Selecting Numeric Case Series with the FAMEOUT= Option
• Example 47.13: Selecting Date Case Series with the FAMEOUT= Option
• Example 47.14: Selecting String Case Series with the FAMEOUT= Option
• Example 47.15: Extracting Source for Formulas
• Example 47.16: Reading Time Series by Defining Fame Expression Groups in the INSET= Option with the KEEP= Clause
• Example 47.17: Optimizing Cache Sizes with the TUNEFAME= and TUNECHLI= Options

---

SASEFRED Interface Engine

• Getting Started: SASEFRED Interface Engine

• Example 48.1: Retrieving Data for Multiple Time Series
• Example 48.2: Retrieving Data by Using the Vintage Date
• Example 48.3: Selecting Time Series When Native Frequency Is Less Than Requested Frequency
• Example 48.4: Selecting Time Series When Native Frequency Is Greater Than Requested Frequency
• Example 48.5: Specifying One Series ID with Multiple Vintage Dates for the OUTPUT=2 Option
• Example 48.6: Specifying Two Series IDs with Multiple Vintage Dates and Descending Sort Order
• Example 48.7: Vintage Dates for a Specific Series with the URL= Option
• Example 48.8: Series for a Specific Release with the URL= Option
• Example 48.9: Series for Specific Tags with the URL= Option
• Example 48.10: Categories for a Specific Series with the URL= Option
• Example 48.11: Categories for a Specific Source with the URL= Option
• Example 48.12: Series for a Specific Category with the URL= Option
• Example 48.13: Sources for Today’s Date with the URL= Option
• Example 48.14: Releases Available for Today’s Date with the URL= Option

---

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 49.1: Examining the Contents of a Haver Database
• Example 49.2: Viewing Quarterly Time Series from a Haver Database
• Example 49.3: Viewing Monthly Time Series from a Haver Database
• Example 49.4: Viewing Weekly Time Series from a Haver Database
• Example 49.5: Viewing Daily Time Series from a Haver Database
• Example 49.6: Limiting the Range of Time Series from a Haver Database
• Example 49.7: Using the WHERE Statement to Subset Time Series from a Haver Database
• Example 49.8: Using the KEEP Option to Subset Time Series from a Haver Database
• Example 49.9: Using the SOURCE Option to Subset Time Series from a Haver Database
• Example 49.10: Using the GROUP Option to Subset Time Series from a Haver Database
• Example 49.11: Using the OUTSELECT=ON Option to View the Key Selection Variables in a Haver Database
• Example 49.12: Selecting Variables Based on Short Source Key Code
• Example 49.13: Selecting Variables Based on Geography Key Codes

---

SASEQUAN Interface Engine

• Getting Started: SASEQUAN Interface Engine

• Example 50.1: Retrieving Historical Price Data for Oil India Limited
• Example 50.2: Retrieving Data by Using Three Quandl Codes
• Example 50.3: Selecting IBM and Google Quandl Codes for All Financial Ratios
• Example 50.4: Retrieving Data for the JASDAQ-TOP20 Exchange Traded Fund (ETF)
• Example 50.5: Collapsing Data for the JASDAQ-TOP20 Exchange Traded Fund (ETF)
• Example 50.6: Transforming Data for the JASDAQ-TOP20 Exchange Traded Fund (ETF)
• Example 50.7: Reading Superset Data for Multiple Time Series

---

SASEXCCM Interface Engine

• Getting Started: SASEXCCM Interface Engine

• Example 51.1: Retrieving SALE Data for One GVKEY
• Example 51.2: Retrieving SALE Data for Multiple Companies
• Example 51.3: Retrieving Data from Different Keysets
• Example 51.4: Retrieving Items by Using Global Options
• Example 51.5: Retrieving All GVKEYs and Company Names
• Example 51.6: Retrieving Stock Time Series by PERMNO
• Example 51.7: Retrieving Stock and Indices Monthly Time Series by INDNO
• Example 51.8: Retrieving Stock and Indices Daily Time Series by INDNO
• Example 51.9: Retrieving Information for Availability of Group INDNOs
• Example 51.10: Retrieving Daily Group Time Series by the INDNO= Option
• Example 51.11: Retrieving Monthly Group Time Series by the INDNO= Option

---

SASEXFSD Interface Engine

• Getting Started: SASEXFSD Interface Engine

• Example 52.1: Retrieving Standardized Economic Items for Multiple Countries
• Example 52.2: Retrieving Economic Items by Using the FQL Syntax for Function
• Example 52.3: Using ECON_EXPR_DATA with the FQL Syntax for Function Returns
• Example 52.4: Using SPEC_ID_DATA with the FQL Economic Download Syntax
• Example 52.5: Using Multiple Database Sources with the FQL Syntax
• Example 52.6: Retrieving Price Data for One Company
• Example 52.7: Retrieving Price and Sales Data for Multiple Companies
• Example 52.8: Retrieving Book Value Data for One Company by Using Relative Dates
• Example 52.9: Retrieving Multiple Screen Items for Multiple Companies
• Example 52.10: Retrieving Data by Using the ISON= and ISONPARAMS= Options
• Example 52.11: Retrieving Benchmark Data by Using the CUTOFF= option
• Example 52.12: Retrieving Benchmark Data by Using the MATCHDATE= Option
• Example 52.13: Retrieving Multiple Items for Multiple Companies from an OFDB File
• Example 52.14: Retrieving a List of Securities from an OFDB file
• Example 52.15: Retrieving a List of CUSIPs from a Screen File

---