gretl4py 0.2

Python bindings for gretl

gretl4py: Python Bindings for gretl

This package provides Python bindings to the gretl econometrics library via its official C API.

For full documentation, please visit the gretl4py project page <https://gretl.sourceforge.net/gretl4py.html>_.

.. note::

The official PDF documentation is still under development and requires updates and enhancements.

You can find useful example scripts in the demo/ subdirectory. <https://sourceforge.net/p/gretl/gretl4py/ci/v0.2/tree/demo/>_.

Package Contents

1. libgretl (including plugins) and its dependencies
2. gretl4py binary module bridging Python with libgretl via its official API
3. Python extensions supporting the binary module
4. Example Python scripts in the examples/ directory
5. Sample datasets in the data/ directory

Provided Functionality

Available Estimators:

::

ar, ar1, arima, biprobit, dpanel, duration, garch, heckit, hsk, intreg, lad, logit, logistic, midasreg, mpols, negbin, ols, panel, poisson, probit, quantreg, tobit, tsls, wls, var, vecm

Available Tests:

::

add, adf, arch, autocorr, bds, bkw, breusch-pagan, chow, coeffsum, coint, comfac, cusum, difftest, johansen, kpss, leverage, levinlin, logs, meantest, normality, normtest, omit, panel, panspec, qlrtest, reset, restrict, runs, squares, white, white-nocross, vartest, vif, xdepend

.. note::

Some tests are dataset-based, while others are model-based.

Usage

1. Loading Datasets

Supported formats: ``.gdt``, ``.gdtb``, ``.csv``, ``.dta``, ``.wf1``, ``.xls``, ``.xlsx``, ``.ods``

Use ``get_data()`` to load a dataset:

.. code-block:: python

   import importlib.resources as resources
   import gretl

   data_dir = resources.files('gretl').joinpath('data')
   d1 = gretl.get_data(str(data_dir.joinpath('bjg.gdt')))

.. note::

   The first dataset loaded is automatically set as the default.

**Bundled Datasets:**

::

   abdata.gdt, bjg.gdt, data9-7.gdt, greene19_1.gdt, grunfeld.gdt, mroz87.gdt,
   rac3d.gdt, b-g.gdt, data4-10.gdt, denmark.gdt, greene22_2.gdt, kennan.gdt,
   ooballot.gdt, tobit.gdt, bjg.csv, data4-1.gdt, gdp_midas.gdt, greene25_1.gdt,
   longley.csv, penngrow.gdt, wtp.gdt

2. Estimating Models

Basic usage pattern:

.. code-block:: python

m = gretl.ESTIMATOR() m.fit()

To pass a dataset explicitly, use the data=... keyword argument.

Example: OLS Regression

.. code-block:: python

m1 = gretl.ols(formula='g ~ const + lg').fit() print(m1)

Examples

Example scripts are located in examples/estimators/ and include:

::

ar1.py, biprobit.py, heckit.py, logit.py, ols.py, probit.py, wls.py, arima.py, duration.py, lad.py, mpols.py, panel.py, tobit.py, ar.py, garch.py, logistic.py, negbin.py, poisson.py, quantreg.py, tsls.py

To view the source of ols.py:

.. code-block:: python

import inspect import gretl.examples.estimators.ols

print(inspect.getsource(gretl.examples.estimators.ols.run_example))

To run the example:

.. code-block:: python

import gretl.examples.estimators.ols

gretl.examples.estimators.ols.run_example()

API Overview

class _gretl.Dataset

**Attributes:**

- ``is_default``
- ``source``

**Methods:**

::

   __copy__, __repr__, bwfilt, bkfilt, get_accessor, get_id, get_series,
   hpfilt, linked_models_list, new_list, new_series, print, sample,
   setobs, set_as_default, test, to_dict, to_file, varnames

``class _gretl.Model``
~~~~~~~~~~~~~~~~~~~~~~

**Methods:**

::

   fcast, fit, get_accessor, get_formula

``class _gretl.Model_NSE``

Methods:

::

restrict, test

class _gretl.Model_GretlModel_VAR

**Methods:**

::

   irf, test

``class _gretl.GretlModel_VAR_VECM``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Methods:**

::

   restrict