Panel data regression python

Panel data regression with fixed effects using Python

Solution 2: as you mentioned above I changed my code in the following way: I transformed the stacks into two dataframes I concated them into a single multi index dataframe ran the regression and added time effects the regression now works very nicely! However I would like to add time effects in the future.

Panel data regression with fixed effects using Python

I have the following panel stored in df :

state	district	year	y	constant	x1	x2	time
0	01	01001	2009	12	1	0.956007	639673	1
1	01	01001	2010	20	1	0.972175	639673	2
2	01	01001	2011	22	1	0.988343	639673	3
3	01	01002	2009	0	1	0	33746	1
4	01	01002	2010	1	1	0.225071	33746	2
5	01	01002	2011	5	1	0.450142	33746	3
6	01	01003	2009	0	1	0	45196	1
7	01	01003	2010	5	1	0.427477	45196	2
8	01	01003	2011	9	1	0.854955	45196	3

• y is the number of protests in each district
• constant is a column full of ones
• x1 is the proportion of the district’s area covered by a mobile network provider
• x2 is the population count in each district (note that it is fixed in time)

How can I run the following model in Python?

# Transform `x2` to match model df['x2'] = df['x2'].multiply(df['time'], axis=0) # District fixed effects df['delta'] = pd.Categorical(df['district']) # State-time fixed effects df['eta'] = pd.Categorical(df['state'] + df['year'].astype(str)) # Set indexes df.set_index(['district','year']) from linearmodels.panel import PanelOLS m = PanelOLS(dependent=df['y'], exog=df[['constant','x1','x2','delta','eta']]) 

ValueError: exog does not have full column rank. If you wish to proceed with model estimation irrespective of the numerical accuracy of coefficient estimates, you can set rank_check=False.

I dug around the documentation and the solution turned out to be quite simple.

After setting the indexes and turning the fixed effect columns to pandas.Categorical types (see question above):

# Import model from linearmodels.panel import PanelOLS # Model m = PanelOLS(dependent=df['y'], exog=df[['constant','x1','x2']], entity_effects=True, time_effects=False, other_effects=df['eta']) m.fit(cov_type='clustered', cluster_entity=True) 

That is, DO NOT pass your fixed effect columns to exog .

You should pass them to entity_effects (boolean), time_effects (boolean) or other_effects (pandas.Categorical).

Stata — Fixed Effects OLS Regression: Difference, I’d like to perform a fixed effects panel regression with two IVs (x1 and x2) and one DV (y), using robust standard errors. and Python interpret the …

A Panel regression in Python

I’m trying to run a panel regression on pandas Dataframes:

Currently I have two dataframes each containing 52 rows(dates)*99 columns(99stocks) :Markdown file with data representation

est=sm.OLS(Stockslist,averages).fit() est.summary() 

I get the ValueError: shapes (52,99) and (52,99) not aligned: 99 (dim 1) != 52 (dim 0)

Can somebody point me out what I am doing wrong? The model is simply y(i,t)=x(i,t)+error term so no intercept. However I would like to add time effects in the future.

Try the below — I’ve copied the stock data from the above link and added random data for the x column. For a panel regression you need a ‘MultiIndex’ as mentioned in the comments.

df = pd.DataFrame(df.set_index('dates').stack()) df.columns = ['y'] df['x'] = np.random.random(size=****(df.index)) df.info() MultiIndex: 100 entries, (2015-04-03 00:00:00, AB INBEV) to (2015-05-01 00:00:00, ZC.PA) Data columns (total 2 columns): y 100 non-null float64 x 100 non-null float64 dtypes: float64(2) memory usage: 2.3+ KB regression = PanelOLS(y=df['y'], x=df[['x']]) regression -------------------------Summary of Regression Analysis------------------------- Formula: Y ~ + Number of Observations: 100 Number of Degrees of Freedom: 2 R-squared: 0.0042 Adj R-squared: -0.0060 Rmse: 0.2259 F-stat (1, 98): 0.4086, p-value: 0.5242 Degrees of Freedom: model 1, resid 98 -----------------------Summary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- x -0.0507 0.0794 -0.64 0.5242 -0.2063 0.1048 intercept 2.1952 0.0448 49.05 0.0000 2.1075 2.2829 ---------------------------------End of Summary--------------------------------- 

as you mentioned above I changed my code in the following way:

1. I transformed the stacks into two dataframes
2. I concated them into a single multi index dataframe
3. ran the regression and added time effects

 MultiIndex: 5096 entries, (2015-04-03 00:00:00, AB INBEV) to (25/03/16, ZC.PA) Data columns (total 2 columns): indvalues 5096 non-null float64 avgvalues 5096 non-null float64 dtypes: float64(2) memory usage: 119.4+ KB from pandas.stats.plm import PanelOLS regression=PanelOLS(y=df["indvalues"], x=df[["avgvalues"]], time_effects=True) 

the regression now works very nicely! Thank you Stefan Jansen

Python — Panel regression gives error «exog does not, Teams. Q&A for work. Connect and share knowledge within a single location that is structured and easy to search. Learn more

Statsmodel: panel regression

I am currently using from pandas.stats.plm import PanelOLS to run Panel regressions. I am needing to switch to statsmodel so that I can ouput heteroskedastic robust results. I have been unable to find notation on calling a panel regression for statsmodel. In general, I find the documentation for statsmodel not very user friendly. Is someone familiar with panel regression syntax in statsmodel?

the linearmodels package is created to extend the statsmodels package to pandelOLS (see https://github.com/bashtage/linearmodels). Here is the example from the package doc:

import numpy as np from statsmodels.datasets import grunfeld data = grunfeld.load_pandas().data data.year = data.year.astype(np.int64) # MultiIndex, entity - time data = data.set_index(['firm','year']) from linearmodels import PanelOLS mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True) res = mod.fit(cov_type='clustered', cluster_entity=True) 

Python Pandas: Balance an unbalanced dataset (for, This way may be easier to understand and can be easily adapted to more complicated scenarios. Let’s say we have an unbalanced panel df and three …

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Implementation of panel data regression (first differences, fixed effects) in python (numpy, pandas)

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README.md

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 .

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.

The FixedEffects class implements a standard fixed effects linear regression:

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

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:

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.

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'

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

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