tv-slr
Implementation of time-varying SLR using OLS estimates
Idea
One of the basic assumptions of the general linear model is that the parameters are constant over time. It has been often suggested that this may not be the valid assumption to make. In cross section studies there can be heterogeneity in the parameters across different units, where as in time series studies there can be variation over time in the parameters... paper link
Installation
pip install tvslr
Usage
In python
from tvslr.tvslr import TVSLR
"""
X:= numpy array containing the independent feature vectors
y:= numpy array containing dependent variable
n:= subset size (must be greater than number of independent features including intercept variable)
"""
reg = TVSLR(X, y, n)
betas = reg.run()
print(betas)
print("R-squared:", reg.cod)
print("Adj. R-squared:", reg.adj_cod)
In command prompt
python -m tvslr <excel filename> <sheetname> <subset size>
Metadata
Release files for tvslr 2.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tvslr-2.0.0.tar.gz | 4.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tvslr-2.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.9 kB
Release files / tvslr-2.0.0.tar.gz
| Download URL | tvslr-2.0.0.tar.gz |
|---|---|
| Size | 4.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.1 CPython/3.9.7
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Release files / tvslr-2.0.0-py3-none-any.whl
| Download URL | tvslr-2.0.0-py3-none-any.whl |
|---|---|
| Size | 5.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
cbdb5d172b42f23e80da23043dfc57893bf11859d708d09f0ffcc7914ecb836d
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f05d9798fbc94834e28489ff404a3b342e74c6ccd2a5b1400cc4694aa24a97eb
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.7
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