Testing for unit roots and determining whether a data set is nonstationary is important for the economist who does empirical work. SAS^® enables the user to detect unit roots using an array of tests: the Dickey-Fuller, Augmented Dickey-Fuller, Phillips-Perron, and the Kwiatkowski-Phillips-Schmidt-Shin test. This paper presents a brief overview of unit roots and shows how to test for a unit root using the example of U.S. national health expenditure data.

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The importance of econometrics in the analytics toolkit is increasing every day. Econometric modeling helps uncover structural relationships in observational data. This paper highlights the many recent changes to the SAS/ETS^® portfolio that increase your power to explain the past and predict the future. Examples show how you can use Bayesian regression tools for price elasticity modeling, use state space models to gain insight from inconsistent time series, use panel data methods to help control for unobserved confounding effects, and much more.

This analysis is based on data for all transactions at four parking meters within a small area in central Copenhagen for a period of four years. The observations show the exact minute parking was bought and the amount of time for which parking was bought in each transaction. These series of at most 80,000 transactions are aggregated to the hour, day, week, and month using PROC TIMESERIES. The aggregated series of parking times and the number of transactions are analyzed for seasonality and interdependence by PROC X12, PROC UCM, and PROC VARMAX.

Many organizations need to forecast large numbers of time series that are discretely valued. These series, called count series, fall approximately between continuously valued time series, for which there are many forecasting techniques (ARIMA, UCM, ESM, and others), and intermittent time series, for which there are a few forecasting techniques (Croston's method and others). This paper proposes a technique for large-scale automatic count series forecasting and uses SAS^® Forecast Server and SAS/ETS^® software to demonstrate this technique.

As organizations strive to do more with fewer resources, many modernize their disparate PC operations to centralized server deployments. Administrators and users share many concerns about using SAS^® on a Microsoft Windows server. This paper outlines key guidelines, plus architecture and performance considerations, that are essential to making a successful transition from PC to server. This paper outlines the five key considerations for SAS customers who will change their configuration from PC-based SAS to using SAS on a Windows server: 1) Data and directory references; 2) Interactive and surrounding applications; 3) Usability; 4) Performance; 5) SAS Metadata Server.

As pollution and population continue to increase, new concepts of eco-friendly commuting evolve. One of the emerging concepts is the bicycle sharing system. It is a bike rental service on a short-term basis at a moderate price. It provides people the flexibility to rent a bike from one location and return it to another location. This business is quickly gaining popularity all over the globe. In May 2011, there were only 375 bike rental schemes consisting of nearly 236,000 bikes. However, this number jumped to 535 bike sharing programs with approximately 517,000 bikes in just a couple of years. It is expected that this trend will continue to grow at a similar pace in the future. Most of the businesses involved in this system of bike rental are faced with the challenge of balancing supply and inconsistent demand. The number of bikes needed on a particular day can vary on several factors such as season, time, temperature, wind speed, humidity, holiday and day of the week. In this paper, we have tried to solve this problem using SAS^® Forecast Studio. Incorporating the effects of all the above factors and analyzing the demand trends of the last two years, we have been able to precisely forecast the number of bikes needed on any day in the future. Also, we are able to do the scenario analysis to observe the effect of particular variables on the demand.

In many studies, a continuous response variable is repeatedly measured over time on one or more subjects. The subjects might be grouped into different categories, such as cases and controls. The study of resulting observation profiles as functions of time is called functional data analysis. This paper shows how you can use the SSM procedure in SAS/ETS^® software to model these functional data by using structural state space models (SSMs). A structural SSM decomposes a subject profile into latent components such as the group mean curve, the subject-specific deviation curve, and the covariate effects. The SSM procedure enables you to fit a rich class of structural SSMs, which permit latent components that have a wide variety of patterns. For example, the latent components can be different types of smoothing splines, including polynomial smoothing splines of any order and all L-splines up to order 2. The SSM procedure efficiently computes the restricted maximum likelihood (REML) estimates of the model parameters and the best linear unbiased predictors (BLUPs) of the latent components (and their derivatives). The paper presents several real-life examples that show how you can fit, diagnose, and select structural SSMs; test hypotheses about the latent components in the model; and interpolate and extrapolate these latent components.

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SAS/ETS provides many tools to improve the productivity of the analyst who works with time series data. This tutorial will take an analyst through the process of turning transaction-level data into a time series. The session will then cover some basic forecasting techniques that use past fluctuations to predict future events. We will then extend this modeling technique to include explanatory factors in the prediction equation.

Since the financial crisis of 2008, banks and bank holding companies in the United States have faced increased regulation. One of the recent changes to these regulations is known as the Comprehensive Capital Analysis and Review (CCAR). At the core of these new regulations, specifically under the Dodd-Frank Wall Street Reform and Consumer Protection Act and the stress tests it mandates, are a series of what-if or scenario analyses requirements that involve a number of scenarios provided by the Federal Reserve. This paper proposes frequentist and Bayesian time series methods that solve this stress testing problem using a highly practical top-down approach. The paper focuses on the value of using univariate time series methods, as well as the methodology behind these models.

In 2014, for the first time, mid-market banks (consisting of banks and bank holding companies with $10-$50 billion in consolidated assets) were required to submit Capital Stress Tests to the federal regulators under the Dodd-Frank Act Stress Testing (DFAST). This is a process large banks have been going through since 2011. However, mid-market banks are not positioned to commit as many resources to their annual stress tests as their largest peers. Limited human and technical resources, incomplete or non-existent detailed historical data, lack of enterprise-wide cross-functional analytics teams, and limited exposure to rigorous model validations are all challenges mid-market banks face. While there are fewer deliverables required from the DFAST banks, the scrutiny the regulators are placing on the analytical modes is just as high as their expectations for Comprehensive Capital Analysis and Review (CCAR) banks. This session discusses the differences in how DFAST and CCAR banks execute their stress tests, the challenges facing DFAST banks, and potential ways DFAST banks can leverage the analytics behind this exercise.

The bookBot Identity: January 2013. With no memory of it from the past, students and faculty at NC State awake to find the Hunt Library just opened, and inside it, the mysterious and powerful bookBot. A true physical search engine, the bookBot, without thinking, relentlessly pursues, captures, and delivers to the patron any requested book (those things with paper pages--remember?) from the Hunt Library. The bookBot Supremacy: Some books were moved from the central campus library to the new Hunt Library. Did this decrease overall campus circulation or did the Hunt Library and its bookBot reign supreme in increasing circulation? The bookBot Ultimatum: To find out if the opening of the Hunt Library decreased or increased overall circulation. To address the bookBot Ultimatum, the Circulation Statistics Investigation (CSI) team uses the power of SAS^® analytics to model library circulation before and after the opening of the Hunt Library. The bookBot Legacy: Join us for the adventure-filled story. Filled with excitement and mystery, this talk is bound to draw a much bigger crowd than had it been more honestly titled Intervention Analysis for Library Data. Tools used are PROC ARIMA, PROC REG, and PROC SGPLOT.

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Did you ever wonder how large US bank holding companies (BHCs) perform stress testing? I had the pleasure to be a part of this process on the model building end, and now I perform model validation. As with everything that is new and uncertain, there is much room for the discovery process. This presentation explains how banks in general perform time series modeling of different loans and credits to establish the bank's position during simulated stress. You learn the basic process behind model building and validation for Comprehensive Capital Analysis and Review (CCAR) purposes, which includes, but is not limited to, back testing, sensitivity analysis, scenario analysis, and model assumption testing. My goal is to gain your interest in the areas of challenging current modeling techniques and looking beyond standard model assumption testing to assess the true risk behind the formulated model and its consequences. This presentation examines the procedures that happen behind the scenes of any code's syntax to better explore statistics that play crucial roles in assessing model performance and forecasting. Forecasting future periods is the process that needs more attention and a better understanding because this is what the CCAR is really all about. In summary, this presentation engages professionals and students to dig dipper into every aspect of time series forecasting.

How does historical production data relate a story about subsurface oil and gas reservoirs? Business and domain experts must perform accurate analysis of reservoir behavior using only rate and pressure data as a function of time. This paper introduces innovative data-driven methodologies to forecast oil and gas production in unconventional reservoirs that, owing to the nature of the tightness of the rocks, render the empirical functions less effective and accurate. You learn how implementations of the SAS^® MODEL procedure provide functional algorithms that generate data-driven type curves on historical production data. Reservoir engineers can now gain more insight to the future performance of the wells across their assets. SAS enables a more robust forecast of the hydrocarbons in both an ad hoc individual well interaction and in an automated batch mode across the entire portfolio of wells. Examples of the MODEL procedure arising in subsurface production data analysis are discussed, including the Duong data model and the stretched exponential data model. In addressing these examples, techniques for pattern recognition and for implementing TREE, CLUSTER, and DISTANCE procedures in SAS/STAT^® are highlighted to explicate the importance of oil and gas well profiling to characterize the reservoir. The MODEL procedure analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations. Primary uses of the MODEL procedure are estimation, simulation, and forecasting of nonlinear simultaneous equation models, and generating type curves that fit the historical rate production data. You will walk through several advanced analytical methodologies that implement the SEMMA process to enable hypotheses testing as well as directed and undirected data mining techniques. SAS^® Visual Analytics Explorer drives the exploratory data analysis to surface trends and relationships, and the data QC workflows ensure a robust input space for the performance forecasting methodologies that are visualized in a web-based thin client for interactive interpretation by reservoir engineers.

When you are analyzing your data and building your models, you often find out that the data cannot be used in the intended way. Systematic pattern, incomplete data, and inconsistencies from a business point of view are often the reason. You wish you could get a complete picture of the quality status of your data much earlier in the analytic lifecycle. SAS^® analytics tools like SAS^® Visual Analytics help you to profile and visualize the quality status of your data in an easy and powerful way. In this session, you learn advanced methods for analytic data quality profiling. You will see case studies based on real-life data, where we look at time series data from a bird's-eye-view and interactively profile GPS trackpoint data from a sail race.

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Many retail and consumer packaged goods (CPG) companies are now keeping track of what their customers purchased in the past, often through some form of loyalty program. This record keeping is one example of how modern corporations are building data sets that have a panel structure, a data structure that is also pervasive in insurance and finance organizations. Panel data (sometimes called longitudinal data) can be thought of as the joining of cross-sectional and time series data. Panel data enable analysts to control for factors that cannot be considered by simple cross-sectional regression models that ignore the time dimension. These factors, which are unobserved by the modeler, might bias regression coefficients if they are ignored. This paper compares several methods of working with panel data in the PANEL procedure and discusses how you might benefit from using multiple observations for each customer. Sample code is available.

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