Panel or Pool?
Posted: Fri Feb 25, 2011 12:30 pm
Most data comes in one of three forms, Time Series, where each observation is identified by a time or date, Cross-section, where each observation is identified by a unique ID (such as State or Country), or by a mixture of the two, commonly called Panel or Pooled data, where each observation is identified by both a time/date and a unique ID (USA 1990, for example).
Most EViews users are very familiar with using Time Series data inside EViews. The standard EViews workfile is often created by specifying a date range and frequency, thus creating a time series structure. Working with panel data is not so familiar to many users however, so this thread will provide some hints and definitions.
There are two distinct ways to work with panel data inside EViews. The first, called the Pool Object has been in EViews for a long time. The second, called the Panel Workfile, was introduced in EViews 5 and is still a relatively new concept to many long time EViews users.
First we will discuss the pool workfile, then the pool object, then panel workfiles, and finally a comparison of the two. If you are already comfortable with pools and panels, and are just interested in the advantages/disadvantages of both, skip down to the last post.
Further, if you want more information than this thread can give, we thoroughly recommend the description given in Richard Startz's book, EViews Illustrated.
The Pool Workfile
The pool object is a way of structuring your panel data inside a standard time-series workfile. An example of such a workfile can be seen below:
The workfile can be downloaded from here (although note the data is actually just random data).
The workfile itself is a standard time series workfile. Notice how the Range statement indicates that it is quarterly data running from 1990Q1 to 2010Q4 (for a total of 84 observations). Although the structure of the workfile is time-series, the workfile does contain cross-sectional information. There are three types of data in the workfile - GDP, inflation (infl) and unemployment (unemp). There are 4 cross-sections, USA, JPN, UK and FRA. Each cross-section (countries in this case) has a separate series for each data type. Thus there is a series called GDP_FRA which contains GDP data for FRA over the periods 1990Q1-2010Q4, there is a series called UNEMP_JPN which contains unemployment data for JPN, etc.... Note that the naming of each series follows a defined pattern, in this case, the name of the data variable followed by an underscore, then the name of the country.
One of the advantages of this type of workfile structure is that it is very easy to perform within-country analysis. For example, say you want to regress unemployment in France on GDP in France and inflation in France. To do this, you can just specify the variables in the normal manner:
(remember this is random data!)
In a similar fashion you could easily regress across countries, to say regress unemployment in France against unemployment in Japan and unemployment in the USA.
Similarly it is easy to graph unemployment in the UK and GDP in the UK together, simply by selecting the two corresponding series as a group and graphing the group:
Most EViews users are very familiar with using Time Series data inside EViews. The standard EViews workfile is often created by specifying a date range and frequency, thus creating a time series structure. Working with panel data is not so familiar to many users however, so this thread will provide some hints and definitions.
There are two distinct ways to work with panel data inside EViews. The first, called the Pool Object has been in EViews for a long time. The second, called the Panel Workfile, was introduced in EViews 5 and is still a relatively new concept to many long time EViews users.
First we will discuss the pool workfile, then the pool object, then panel workfiles, and finally a comparison of the two. If you are already comfortable with pools and panels, and are just interested in the advantages/disadvantages of both, skip down to the last post.
Further, if you want more information than this thread can give, we thoroughly recommend the description given in Richard Startz's book, EViews Illustrated.
The Pool Workfile
The pool object is a way of structuring your panel data inside a standard time-series workfile. An example of such a workfile can be seen below:
The workfile can be downloaded from here (although note the data is actually just random data).
The workfile itself is a standard time series workfile. Notice how the Range statement indicates that it is quarterly data running from 1990Q1 to 2010Q4 (for a total of 84 observations). Although the structure of the workfile is time-series, the workfile does contain cross-sectional information. There are three types of data in the workfile - GDP, inflation (infl) and unemployment (unemp). There are 4 cross-sections, USA, JPN, UK and FRA. Each cross-section (countries in this case) has a separate series for each data type. Thus there is a series called GDP_FRA which contains GDP data for FRA over the periods 1990Q1-2010Q4, there is a series called UNEMP_JPN which contains unemployment data for JPN, etc.... Note that the naming of each series follows a defined pattern, in this case, the name of the data variable followed by an underscore, then the name of the country.
One of the advantages of this type of workfile structure is that it is very easy to perform within-country analysis. For example, say you want to regress unemployment in France on GDP in France and inflation in France. To do this, you can just specify the variables in the normal manner:
(remember this is random data!)
In a similar fashion you could easily regress across countries, to say regress unemployment in France against unemployment in Japan and unemployment in the USA.
Similarly it is easy to graph unemployment in the UK and GDP in the UK together, simply by selecting the two corresponding series as a group and graphing the group: