User’s Guide : EViews Fundamentals : Workfile Basics : What is a Workfile?
What is a Workfile?
Workfiles and Datasets
At a basic level, a workfile is simply a container for EViews objects (see “Object Basics”). Most of your work in EViews will involve objects that are contained in a workfile, so your first step in any project will be to create a new workfile or to load an existing workfile into memory.
Every workfile contains one or more workfile pages, each with its own objects. A workfile page may be thought of as a subworkfile or subdirectory that allows you to organize the data within the workfile.
For most purposes, you may treat a workfile page as though it were a workfile (just as a subdirectory is also a directory) since there is often no practical distinction between the two. Indeed, in the most common setting where a workfile contains only a single page, the two are completely synonymous. Where there is no possibility of confusion, we will use the terms “workfile” and “workfile page” interchangeably.
Workfiles and Datasets
While workfiles and workfile pages are designed to hold a variety of EViews objects, such as equations, graphs, and matrices, their primary purpose is to hold the contents of datasets. A dataset is defined here as a data rectangle, consisting of a set of observations on one or more variables—for example, a time series of observations on the variables GDP, investment, and interest rates, or perhaps a random sample of observations containing individual incomes and tax liabilities.
Key to the notion of a dataset is the idea that each observation in the dataset has a unique identifier, or ID. Identifiers usually contain important information about the observation, such as a date, a name, or perhaps an identifying code. For example, annual time series data typically use year identifiers (“1990”, “1991”, ...), while cross-sectional state data generally use state names or abbreviations (“AL”, “AK”, ..., “WY”). More complicated identifiers are associated with longitudinal data, where one typically uses both an individual ID and a date ID to identify each observation.
Observation IDs are often, but not always, included as a part of the dataset. Annual datasets, for example, usually include a variable containing the year associated with each observation. Similarly, large cross-sectional survey data typically include an interview number used to identify individuals.
In other cases, observation IDs are not provided in the dataset, but external information is available. You may know, for example, that the 21 otherwise unidentified observations in a dataset are for consecutive years beginning in 1990 and continuing to 2010.
In the rare case were there is no additional identifying information, one may simply use a set of default integer identifiers that enumerate the observations in the dataset (“1”, “2”, “3”, ...).
Since the primary purpose of every workfile page is to hold the contents of a single dataset, each page must contain information about observation identifiers. Once identifier information is provided, the workfile page provides context for working with observations in the associated dataset, allowing you to use dates, handle lags, or work with longitudinal data structures.