Lastly, and most importantly, a pool workfile must contain one or more pool objects, each of which contains a (possibly different) description of the pooled structure of your workfile in the form of rules specifying the user-defined naming convention for your series.
For example, if you have time series data for an economic variable like investment that differs for each of 10 firms, you should have 10 separate investment series in the workfile with names that follow the user-defined convention. Pooled Time Series, Cross-Section DataĬific variable, you should have a separate series corresponding to each cross-section/ variable combination. For each cross-section spe-ĥ66Chapter 35. Second, the pool workfile should contain EViews series that follow a user-defined naming convention. For example, if you want to work with data for some firms from 1932 to 1954, and data for other firms from 1930 to 1950, you should create a workfile ranging from 1930 to 1954. The range of your workfile should represent the earliest and latest dates or observations you wish to consider for any of the cross-section units. First, a pool workfile is an ordinary EViews workfile structured to match the time series dimension of your data. There are several characteristics of an EViews workfile that allow it to be used with pooled time series, crosssection data. The Pool WorkfileThe first step in working with pooled data is to set up a pool workfile. Working with Panel Data, on page 615 and Chapter 37. Working with panel structured data in EViews is described in Chapter 36. This type of data is typically termed panel data. Note that the data structures described in this chapter should be distinguished from data where there are large numbers of cross-sectional units.
The remainder of this chapter will describe how to set up your data to work with pools, and how to define and work with pool objects. The EViews object that manages time series/cross-section data is called a pool. EViews will help you manage your data, perform operations in either the time series or the crosssection dimension, and apply estimation methods that account for the pooled structure of your data. EViews provides a number of specialized tools to help you work with pooled data. We term such data pooled time series, cross-section data. Or perhaps you have state level data on unemployment observed over time. For example, you may have time series data on GDP for a number of European nations. Pooled Time Series, Cross-Section DataData often contain information on a relatively small number of cross-sectional units observed over time. Panel Estimation, beginning on page 647 describes estimation in panel structured workfiles.Ĭhapter 35. Working with Panel Data, beginning on page 615, outlines the basics of working with panel workfiles. Once a workfile is structured as a panel workfile, EViews provides you with different tools for working with data in the workfile, and for estimating equation specifications using both the data and the panel structure. Advanced Workfiles, beginning on page 213 of Users Guide I, we describe the basics of structuring a workfile for use with panel data. Stacked panel data are described separately: In Chapter 9. Pooled Time Series, Cross-Section Data, on page 565 outlines tools for working with pooled time series, cross-section data, and estimating standard equation specifications which account for the pooled structure of the data. Pooled data structures are discussed first: Chapter 35. The discussion of these data is divided into parts. Generally speaking, we distinguish between the two by noting that pooled time-series, cross-section data refer to data with relatively few cross-sections, where variables are held in cross-section specific individual series, while panel data correspond to data with large numbers of cross-sections, with variables held in single series in stacked form. Panel and Pooled DataPanel and pool data involve observations that possess both cross-section, and within-crosssection identifiers.