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 14.3 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 SPATIALREG PROC SPECTRA PROC SSM PROC STATESPACE PROC SYSLIN PROC TIMEDATA PROC TIMEID PROC TIMESERIES PROC TMODEL PROC TSCSREG PROC UCM PROC VARMAX PROC X11 PROC X13 SASECRSP Interface Engine SASEFAME Interface Engine SASEFRED Interface Engine SASEHAVR Interface Engine SASENOAA Interface Engine SASEOECD Interface Engine (NEW) SASEQUAN Interface Engine SASERAIN Interface Engine SASEWBGO 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 7.1: Simulated IMA Model
• Example 7.2: Seasonal Model for the Airline Series
• Example 7.3: Model for Series J Data from Box and Jenkins
• Example 7.4: An Intervention Model for Ozone Data
• Example 7.5: Using Diagnostics to Identify ARIMA Models
• Example 7.6: Detection of Level Changes in the Nile River Data
• Example 7.7: Iterative Outlier Detection
• Example 7.8: Test for Stationarity

---

AUTOREG Procedure

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

• Example 8.1: Analysis of Real Output Series
• Example 8.2: Comparing Estimates and Models
• Example 8.3: Lack-of-Fit Study
• Example 8.4: Missing Values
• Example 8.5: Money Demand Model
• Example 8.6: Estimation of ARCH(2) Process
• Example 8.7: Estimation of GARCH-Type Models
• Example 8.8: Illustration of ODS Graphics

---

COMPUTAB Procedure

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

• Example 9.1: Using Programming Statements
• Example 9.2: Enhancing a Report
• Example 9.3: Comparison of Actual and Budget
• Example 9.4: Consolidations
• Example 9.5: Creating an Output Data Set
• Example 9.6: Cash Flows

---

COPULA Procedure

• Getting Started: COPULA Procedure

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

---

COUNTREG Procedure

• Getting Started: COUNTREG Procedure

• Example 11.1: Basic Models
• Example 11.2: ZIP and ZINB Models for Data That Exhibit Extra Zeros
• Example 11.3: Variable Selection
• Example 11.4: Spatial Effects

---

DATASOURCE Procedure

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

• Example 12.1: BEA National Income and Product Accounts
• Example 12.2: BLS Consumer Price Index Surveys
• Example 12.3: BLS State and Area Employment, Hours, and Earnings Surveys
• Example 12.4: DRI/McGraw-Hill Format CITIBASE Files
• Example 12.5: DRI Data Delivery Service Database
• Example 12.6: PC Format CITIBASE Database
• Example 12.7: Quarterly COMPUSTAT Data Files
• Example 12.8: Annual COMPUSTAT Data Files, V9.2 New Filetype CSAUC3
• Example 12.9: CRSP Daily NYSE/AMEX Combined Stocks

---

ENTROPY Procedure

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

• Example 13.1: Nonnormal Error Estimation
• Example 13.2: Unreplicated Factorial Experiments
• Example 13.3: Censored Data Models in PROC ENTROPY
• Example 13.4: Use of the PDATA= Option
• Example 13.5: Illustration of ODS Graphics

---

ESM Procedure

• Getting Started: ESM Procedure

• Example 14.1: Forecasting of Time Series Data
• Example 14.2: Forecasting of Transactional Data
• Example 14.3: Specifying the Forecasting Model
• Example 14.4: Extending the Independent Variables for Multivariate Forecasts
• Example 14.5: Illustration of ODS Graphics

---

EXPAND Procedure

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

• Example 15.1: Combining Monthly and Quarterly Data
• Example 15.2: Illustration of ODS Graphics
• Example 15.3: Interpolating Irregular Observations
• Example 15.4: Using Transformations

---

FORECAST Procedure

---

HPCDM Procedure

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

• Example 16.1: Estimating the Probability Distribution of Insurance Payments
• Example 16.2: Using Externally Simulated Count Data
• Example 16.3: Scenario Analysis with Rich Regression Effects and BY Groups

---

HPCOPULA Procedure

• Getting Started: HPCOPULA Procedure

• Example 17.1: Simulating Default Times

---

HPCOUNTREG Procedure

• Getting Started: HPCOUNTREG Procedure

• Example 18.1: High-Performance Zero-Inflated Poisson Model

---

HPPANEL Procedure

• Getting Started: HPPANEL Procedure

• Example 19.1: One-Way Random-Effects High-Performance Model

---

HPQLIM Procedure

• Getting Started: HPQLIM Procedure

• Example 20.1: High-Performance Model with Censoring
• Example 20.2: 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 21.1: Defining a Model for Gaussian Distribution
• Example 21.2: Defining a Model for the Gaussian Distribution with a Scale Parameter
• Example 21.3: Defining a Model for Mixed-Tail Distributions
• Example 21.4: Fitting a Scaled Tweedie Model with Regressors
• Example 21.5: Fitting Distributions to Interval-Censored Data
• Example 21.6: Benefits of Distributed and Multithreaded Computing
• Example 21.7: Estimating Parameters Using the Cramér–von Mises Estimator
• Example 21.8: Defining a Finite Mixture Model That Has a Scale Parameter
• Example 21.9: Predicting Mean and Value-at-Risk by Using Scoring Functions
• Example 21.10: 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 22.1: Discount Points for Lower Interest Rates
• Example 22.2: Refinancing a Loan
• Example 22.3: Prepayments on a Loan
• Example 22.4: Output Data Sets
• Example 22.5: 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 23.1: Binary Data Modeling
• Example 23.2: Conditional Logit and Data Conversion
• Example 23.3: Correlated Choice Modeling
• Example 23.4: Testing for Homoscedasticity of the Utility Function
• Example 23.5: Choice of Time for Work Trips: Nested Logit Analysis
• Example 23.6: Hausman’s Specification Test
• Example 23.7: 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 24.1: OLS Single Nonlinear Equation
• Example 24.2: A Consumer Demand Model
• Example 24.3: Vector AR(1) Estimation
• Example 24.4: MA(1) Estimation
• Example 24.5: Polynomial Distributed Lags by Using %PDL
• Example 24.6: General Form Equations
• Example 24.7: Spring and Damper Continuous System
• Example 24.8: Nonlinear FIML Estimation
• Example 24.9: Circuit Estimation
• Example 24.10: Systems of Differential Equations
• Example 24.11: Monte Carlo Simulation
• Example 24.12: Cauchy Distribution Estimation
• Example 24.13: Switching Regression Example
• Example 24.14: Simulating from a Mixture of Distributions
• Example 24.15: Simulated Method of Moments—Simple Linear Regression
• Example 24.16: Simulated Method of Moments—AR(1) Process
• Example 24.17: Simulated Method of Moments—Stochastic Volatility Model
• Example 24.18: Duration Data Model with Unobserved Heterogeneity
• Example 24.19: EMM Estimation of a Stochastic Volatility Model
• Example 24.20: Illustration of ODS Graphics
• Example 24.21: A Translog Cost Function and Derived Demands
• Example 24.22: Reducing Parameter Variance in a Tree Biomass Model

---

PANEL Procedure

• Getting Started: PANEL Procedure

• Example 25.1: The Airline Cost Data: Fixed Effects
• Example 25.2: The Airline Cost Data: First Difference or Fixed Effects
• Example 25.3: Analyzing Demand for Liquid Assets: Random Effects
• Example 25.4: Panel Study of Income Dynamics (PSID): Hausman-Taylor Models
• Example 25.5: Cigarette Sales Data: Dynamic Panel Estimation
• Example 25.6: Using the FLATDATA Statement

---

PDLREG Procedure

• Introductory Example

• Example 26.1: Industrial Conference Board Data
• Example 26.2: Money Demand Model

---

QLIM Procedure

• Introductory Example: Binary Probit and Logit Models

• Example 27.1: Ordered Data Modeling
• Example 27.2: Tobit Analysis
• Example 27.3: Bivariate Probit Analysis
• Example 27.4: Sample Selection Model
• Example 27.5: Sample Selection Model with Truncation and Censoring
• Example 27.6: Types of Tobit Models
• Example 27.7: Stochastic Frontier Models
• Example 27.8: 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 28.1: Defining a Model for Gaussian Distribution
• Example 28.2: Defining a Model for the Gaussian Distribution with a Scale Parameter
• Example 28.3: Defining a Model for Mixed-Tail Distributions
• Example 28.4: Estimating Parameters Using the Cramér–von Mises Estimator
• Example 28.5: Fitting a Scaled Tweedie Model with Regressors
• Example 28.6: Fitting Distributions to Interval-Censored Data
• Example 28.7: Defining a Finite Mixture Model That Has a Scale Parameter
• Example 28.8: Predicting Mean and Value-at-Risk by Using Scoring Functions
• Example 28.9: Scale Regression with Rich Regression Effects

---

SIMILARITY Procedure

• Getting Started: SIMILARITY Procedure

• Example 29.1: Accumulating Transactional Data into Time Series Data
• Example 29.2: Similarity Analysis
• Example 29.3: Sliding Similarity Analysis
• Example 29.4: Searching for Historical Analogies
• Example 29.5: Clustering Time Series

---

SIMLIN Procedure

• Prediction and Simulation

• Example 30.1: Simulating Klein’s Model I
• Example 30.2: Multipliers for a Third-Order System

---

SPATIALREG Procedure

• Getting Started: SPATIALREG Procedure

• Example 31.1: Columbus Crime Data
• Example 31.2: Models with Spatial ID Matching
• Example 31.3: Fitting Multiple Models
• Example 31.4: Compact Representation of a Spatial Weights Matrix
• Example 31.5: Taylor and Chebyshev Approximations

---

SPECTRA Procedure

• Getting Started: SPECTRA Procedure

• Example 32.1: Spectral Analysis of Sunspot Activity
• Example 32.2: Cross-Spectral Analysis

---

SSM Procedure

• Getting Started: SSM Procedure

• Example 33.1: Bivariate Basic Structural Model
• Example 33.2: Panel Data: Random-Effects and Autoregressive Models
• Example 33.3: Backcasting, Forecasting, and Interpolation
• Example 33.4: Longitudinal Data: Smoothing of Repeated Measures
• Example 33.5: A User-Defined Trend Model
• Example 33.6: Model with Multiple ARIMA Components
• Example 33.7: A Dynamic Factor Model for the Yield Curve
• Example 33.8: Diagnostic Plots and Structural Break Analysis
• Example 33.9: Longitudinal Data: Variable Bandwidth Smoothing
• Example 33.10: A Transfer Function Model for the Gas Furnace Data
• Example 33.11: Panel Data: Dynamic Panel Model for the Cigar Data
• Example 33.12: Multivariate Modeling: Long-Term Temperature Trends
• Example 33.13: Bivariate Model: Sales of Mink and Muskrat Furs
• Example 33.14: Factor Model: Now-Casting the US Economy
• Example 33.15: Longitudinal Data: Lung Function Analysis
• Example 33.16: Temporal Distribution: Estimating Monthly GDP
• Example 33.17: Temporal Aggregation: Triannual Nile River Level
• Example 33.18: Invariance of the Marginal Likelihood under Linear Rescaling of the Diffuse Effects

---

STATESPACE Procedure

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

• Example 34.1: 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 35.1: Klein’s Model I Estimated with LIML and 3SLS
• Example 35.2: Grunfeld’s Model Estimated with SUR
• Example 35.3: Illustration of ODS Graphics

---

TIMEDATA Procedure

• Getting Started: TIMEDATA Procedure

• Example 36.1: Accumulating Transactional Data into Time Series Data
• Example 36.2: Using User-Defined Functions and Subroutines
• Example 36.3: Using Auxiliary Data Sets with PROC TIMEDATA

---

TIMEID Procedure

• Getting Started: TIMEID Procedure

• Example 37.1: Examining a Weekly Time ID Variable
• Example 37.2: Inferring a Date Interval
• Example 37.3: Examining Multiple BY Groups

---

TIMESERIES Procedure

• Getting Started: TIMESERIES Procedure

• Example 38.1: Accumulating Transactional Data into Time Series Data
• Example 38.2: Trend and Seasonal Analysis
• Example 38.3: Illustration of ODS Graphics
• Example 38.4: Illustration of Spectral Analysis
• Example 38.5: Singular Spectrum Analysis

---

TMODEL Procedure

• Getting Started: TMODEL Procedure

• Example 39.1: Thread Allocation Using the Performance Statement
• Example 39.2: Random-Effects Parameter Estimation

---

TSCSREG Procedure

• Specifying the Input Data
• Unbalanced Data
• Specifying the Regression Model
• Estimation Techniques
• Introductory Example

• Example : Examples: The TSCSREG Procedure

---

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
• Example 41.9: Extracting A Business Cycle
• Example 41.10: A Transfer-Function Model for the Italian Traffic Accident Data

---

VARMAX Procedure

• Vector Autoregressive Model
• Bayesian Vector Autoregressive Model
• Vector Error Correction Model
• Bayesian Vector Error Correction Model
• Vector Autoregressive Fractionally Integrated Moving Average 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: Conditional Forecasts and Scenario Analysis
• Example 42.6: Numerous Examples
• Example 42.7: 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

• Setup for SAS and FAME
• 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
• Example 47.18: Remote Access Using the MCADBS Server

---

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

---

SASENOAA Interface Engine

• Getting Started: SASENOAA Interface Engine

• Example 50.1: Retrieving Severe Storm Warning Data with ID= Option for a Specific Date
• Example 50.2: Retrieving a Preliminary Local Storm Report by Using a Bounding Box
• Example 50.3: Retrieving Mesocyclone Data for a Specific Date
• Example 50.4: Retrieving Hail Data for One Weather Station
• Example 50.5: Retrieving Tornado Vortex Signature Data within a Distance Specified by a Center and a Radius
• Example 50.6: Retrieving Digital Mesocyclone Detection Algorithm Data for a Specific Date
• Example 50.7: Retrieving Tornado Vortex Signature Data Statistics by Using Tile Summary Statistics
• Example 50.8: Retrieving Tornado Vortex Signature Data by Using Tile Coordinates
• Example 50.9: Mapping Hail Data in a Geospatial Framework (KMZ Format) for a Specific Weather Station
• Example 50.10: Mapping Hail Data in a Geospatial Framework (SHP Format) for a Specific Weather Station

---

SASEOECD Interface Engine New Interface Engine!

• Using the OECD Graphical User Interface

• Example 51.1: Retrieving OECD Gross Domestic Product Data for One Region
• Example 51.2: Retrieving the Short-Term Labor Market Statistics for Australia
• Example 51.3: Retrieving Bank Profitability Statistics for USA, NMEC, and RUS
• Example 51.4: Retrieving Fisheries and Aquaculture Employment for the Czech Republic
• Example 51.5: Retrieving the Trade by Enterprise Characteristics by Ownership Statistics for the United Kingdom

---

SASEQUAN Interface Engine

• Getting Started: SASEQUAN Interface Engine

• Example 52.1: Retrieving Historical Price Data for Oil India Limited
• Example 52.2: Retrieving Data by Using Three Quandl Codes
• Example 52.3: Retrieving Data for the NIKKEI JASDAQ Stock Average Index
• Example 52.4: Collapsing Data for the Nikkei JASDAQ Stock Average Index
• Example 52.5: Transforming Data for the Nikkei JASDAQ Stock Average Index
• Example 52.6: Reading Data from Multiple Quandl Data Sets to Merge Multiple Time Series

---

SASERAIN Interface Engine

• Getting Started: SASERAIN Interface Engine

• Example 53.1: Retrieving Weather Forecast Data for One Location
• Example 53.2: Retrieving the Two-Day Local Weather Forecast for One Location
• Example 53.3: Retrieving the Local Weather Forecast for One Location
• Example 53.4: Retrieving the Local Weather Forecast for Three Locations
• Example 53.5: Retrieving Current Conditions for One Location
• Example 53.6: Retrieving Historical Weather Data for Two Cities for a Date Range

SASEWBGO Interface Engine

• Getting Started: SASEWBGO Interface Engine

• Example 54.1: Reading Gross Domestic Product Data
• Example 54.2: Retrieving Data for All Countries
• Example 54.3: Setting the Number of Observations Retrieved in One Page of Data
• Example 54.4: Sorting Time Series in Descending Order Using the Sort= Option
• Example 54.5: Retrieving a List of Indicators for a Specified Source Using the URL= Option
• Example 54.6: Retrieving a List of Indicators for a Specified Topic Using the URL= Option
• Example 54.7: Retrieving Quarterly External Debt Statistics for Multiple Countries
• Example 54.8: Retrieving Monthly Global Economic Monitor Commodities for the World
• Example 54.9: Retrieving the Full Range of Data in One Page

---

SASEXCCM Interface Engine

• Getting Started: SASEXCCM Interface Engine

• Example 55.1: Retrieving SALE Data for One GVKEY
• Example 55.2: Retrieving SALE Data for Multiple Companies
• Example 55.3: Retrieving Data from Different Keysets
• Example 55.4: Retrieving Items by Using Global Options
• Example 55.5: Retrieving All GVKEYs and Company Names
• Example 55.6: Retrieving Stock Time Series by PERMNO
• Example 55.7: Retrieving Stock and Indices Monthly Time Series by INDNO
• Example 55.8: Retrieving Stock and Indices Daily Time Series by INDNO
• Example 55.9: Retrieving Information for Availability of Group INDNOs
• Example 55.10: Retrieving Daily Group Time Series by the INDNO= Option
• Example 55.11: Retrieving Monthly Group Time Series by the INDNO= Option

---

SASEXFSD Interface Engine

• Getting Started: SASEXFSD Interface Engine

• Example 56.1: Retrieving Standardized Economic Items for Multiple Countries
• Example 56.2: Retrieving Economic Items by Using the FQL Syntax for Function
• Example 56.3: Using ECON_EXPR_DATA with the FQL Syntax for Function Returns
• Example 56.4: Using SPEC_ID_DATA with the FQL Economic Download Syntax
• Example 56.5: Using Multiple Database Sources with the FQL Syntax
• Example 56.6: Retrieving Price Data for One Company
• Example 56.7: Retrieving Price and Sales Data for Multiple Companies
• Example 56.8: Retrieving Book Value Data for One Company by Using Relative Dates
• Example 56.9: Retrieving Multiple Screen Items for Multiple Companies
• Example 56.10: Retrieving Data by Using the ISON= and ISONPARAMS= Options
• Example 56.11: Retrieving Benchmark Data by Using the CUTOFF= Option
• Example 56.12: Retrieving Benchmark Data by Using the MATCHDATE= Option
• Example 56.13: Retrieving Multiple Items for Multiple Companies from an OFDB File
• Example 56.14: Retrieving a List of Securities from an OFDB File
• Example 56.15: Retrieving a List of CUSIPs from a Screen File

---