pydynpd 0.2.0

A package to estimate dynamic panel data model using difference GMM and system GMM.

pydynpd: Dynamic panel estimation for Difference and System GMM (generalized method-of-moments)

pydynpd is the first python package to implement Difference and System GMM [1][2][3] to estimate dynamic panel data models.

Below is a typical dynamic panel data model:

In the equation above, x is a predetermined variable that is potentially correlated with past errors, s is a strictly exogenous variable, and u is fixed effect.

Features supported:

• Differene and System GMM
• One-step, two-step, and iterative estimates
• First-difference and forward orthogonal deviation transformations
• Robust standard errors. For two-step GMM, the calculation suggested by Windmeijer (2005) is used.
• Hansen over-identification test
• Arellano-Bond test for autocorrelation
• Time dummies
• Collapse GMM instruments to limit instrument proliferation
• Search for models based on users' request, rather than just run the model specified by users as other packages do

Installation:

pip install pydynpd

This package requires: numpy, scipy, pandas, and PrettyTable

Usage:

import pandas as pd
from  pydynpd import regression

df = pd.read_csv("data.csv")
command_str='n L(1:2).n w k  | gmm(n, 2:4) gmm(w, 1:3)  iv(k) | timedumm  nolevel'
mydpd = regression.abond(command_str, df, ['id', 'year'])

result:

Dynamic panel-data estimation, two-step difference GMM
 Group variable: id             Number of obs = 611    
 Time variable: year            Number of groups = 140 
 Number of instruments = 42                            
+-----------+------------+---------------------+------------+-----------+
|     n     |   coef.    | Corrected Std. Err. |     z      |   P>|z|   |
+-----------+------------+---------------------+------------+-----------+
|    L1.n   | 0.2710675  |      0.1382542      | 1.9606462  | 0.0499203 |
|    L2.n   | -0.0233928 |      0.0419665      | -0.5574151 | 0.5772439 |
|     w     | -0.5668527 |      0.2092231      | -2.7093219 | 0.0067421 |
|     k     | 0.3613939  |      0.0662624      | 5.4539824  | 0.0000000 |
| year_1979 | 0.0011898  |      0.0092322      | 0.1288765  | 0.8974554 |
| year_1980 | -0.0316432 |      0.0116155      | -2.7242254 | 0.0064453 |
| year_1981 | -0.0900163 |      0.0206593      | -4.3571693 | 0.0000132 |
| year_1982 | -0.0996210 |      0.0296036      | -3.3651654 | 0.0007650 |
| year_1983 | -0.0693308 |      0.0404276      | -1.7149347 | 0.0863572 |
| year_1984 | -0.0614505 |      0.0475525      | -1.2922666 | 0.1962648 |
+-----------+------------+---------------------+------------+-----------+
Hansen test of overid. restrictions: chi(32) = 32.666 Prob > Chi2 = 0.434
Arellano-Bond test for AR(1) in first differences: z = -1.29 Pr > z =0.198
Arellano-Bond test for AR(2) in first differences: z = -0.31 Pr > z =0.760

Tutorial

A detailed tutorial is here.

Similar packages

The objective of the package is similar to the following open-source packages:

Package	Language	version
plm	R	2.6-1
panelvar	R	0.5.3
pdynmc	R	0.9.7

To compare pydynpd with similar packages, we performed performance tests. More specifically, in each test for each package we run 100 times to estimate the same model with the same data. For verification, the tests also include Stata package xtabond2 though Stata is a commercial software. We use xtabond2 for regression result verification because it is the most popular package in estimating dynamic panel models. Figure below is from one of the tests. Note that directly comparing xtabond2's speed with R or Python packages is a little unfair because the calculation part of xtabond2 was compiled while pydynpd and the three R packages are interpreted; xtabond2 should have a clear advantage on speed.

Though developed in pure python, pydynpd is not far behind of xtabond2. Moreover, it is significanly faster than the three R packages which are interpreted scripts just like pydynpd.

A detailed description of the tests can be found here

FAQs

1. How to extract coefficients from regression? For example, if you run:

df = pd.read_csv("data.csv")
mydpd = regression.abond('n L(1:2).n w k  | gmm(n, 2:4) gmm(w, 1:3)  iv(k)  ', df, ['id', 'year'])

The output regression table will be

+------+------------+---------------------+------------+-----------+-----+
|  n   |   coef.    | Corrected Std. Err. |     z      |   P>|z|   |     |
+------+------------+---------------------+------------+-----------+-----+
| L1.n | 0.9453810  |      0.1429764      | 6.6121470  | 0.0000000 | *** |
| L2.n | -0.0860069 |      0.1082318      | -0.7946553 | 0.4268140 |     |
|  w   | -0.4477795 |      0.1521917      | -2.9422068 | 0.0032588 |  ** |
|  k   | 0.1235808  |      0.0508836      | 2.4286941  | 0.0151533 |  *  |
| _con | 1.5630849  |      0.4993484      | 3.1302492  | 0.0017466 |  ** |
+------+------------+---------------------+------------+-----------+-----+

If you want to programably extract a value, for example, the first z value (6.6121470) then you can add the following:

>>>mydpd.models[0].regression_table.iloc[0]['z_value']
6.6121469997085915

Basically, the object mydpd returned above contains models because pydynpd allows us to run and compare multiple models at the same time. By default, it only contains one model which is models[0]. A model has a regression table which is a pandas dataframe:

 >>>mydpd.models[0].regression_table

  variable  coefficient   std_err   z_value       p_value  sig
0     L1.n     0.945381  0.142976  6.612147  3.787856e-11  ***
1     L2.n    -0.086007  0.108232 -0.794655  4.268140e-01     
2        w    -0.447780  0.152192 -2.942207  3.258822e-03   **
3        k     0.123581  0.050884  2.428694  1.515331e-02    *
4     _con     1.563085  0.499348  3.130249  1.746581e-03   **

So you can extract any value from this dataframe.

Contributing

There are several ways to contribute to pydynpd:

Submit issue/bug reports here, or try to fix the problem yourself and then submit a pull request.

Browse the source code and see if anything looks out of place - let us know!

References

[1] Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies, 58(2), 277-297.

[2] Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1), 29-51.

[3] Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, 87(1), 115-143.

[4] Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The stata journal, 9(1), 86-136.

[5] Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of econometrics, 126(1), 25-51.