Panel Data Regression Methods in Python

This repository implements basic panel data regression methods (fixed effects, first differences) in Python, plus some other panel data utilities.

It is built on numpy, pandas and statsmodels.

Wrapper Object

All functionality is neatly wrapped inside one object: PanelReg(). This wrapper class provides quick access to all other classes and methods, so you only need to import one class:

from panel_reg import PanelReg

Then, everything else then is implemented can be accessed from this object:

• Panel Builder: PanelReg().build()
• Fixed Effecst: PanelReg().fe()
• First Differences: PanelReg().fd()

Each method returns the object (e.g. FixedEffects), which you then instantiate based on the documention below.

Fixed Effects

The FixedEffects class implements a standard fixed effects linear regression:

 + u_{it})

To remove the unit-level effect a (and the time effect d), we demean the data:




First Differences

Panel Builder

The PanelBuilder class is written to help you create a pandas.Panel from your data, which can then be passed into one of the estimation classes. The object instance takes no argument when created:

pb = PanelBuilder()

pandas Panel

A pandas.Panel instance is essentially a 3D dataset. The first axis is called item (or entity, i.e. the units that we are following over time). The second axis is called major and is generally used for specifying time. The third axis is called minor and it refers to the actual variables we are measuring.

For more information and API reference for pandas.Panel, see: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.html

Naming dimensions

With the PanelBuilder, you can first only name/specify the three axes (without passing any data). The following methods implement this:

pb.specify_times(times)  # Specify the time dimension
pb.specify_entities(entities)  # Specify the entity/unit dimension
pb.specify_variables(variables)  # Specift the variable dimension

Where the argument passed into each of these methods is an array-like structure of unique names in that dimension.

For example, if my panel consisted of all years between 1960 and 2000, I could specify it as follows:

years = np.arange(1960,2001)  # Create a numpy array of integers between 1960 and 2000 (inclusive), could also use range()
pb.specify_times(times)  # set the times
# > 'Time dimension set to size 41'

Passing a 3D numpy array

As a pandas.Panel is just a "named"/"indexed" 3D numpy.NDArray, the PanelBuilder supports creation of the panel from a multidimensional numpy array or standard Python list. This is done with the pb.panel_from_array(multiarray) method, where multiarray is either a 3D numpy array or a 3D list.

When dimensions/index names have been passed before passing the 3D array, the dimensions of the array must match those supplied when naming. You can also name the dimensions afterwards, but then again, the dimensions must match those of the supplied array.

The dimensions of the array are expected to be (entities, times, variables).

Passing a dictionary of slices by time

Passing a dictionary of slices by entity

Creating the panel