This project implements the repeated surveys Kalman filter of Jo Thori Lind found in the papers here and here.
It is based on the author's original source written for the Ox language.
Suppose we observe a population of individuals at several points in time. At each point in time, we can measure the mean of a variable of interest in the population. But if the true mean of the population is changing smoothly and gradually over time, we might imagine that the measurement of a mean at any particular point in time might be improved by considering data from the other observed time slices. This is the idea behind the Kalman filter--exploit the temporal dynamics to smooth out fluctuations in point-in-time estimates.
See examples for further illustration and applications of the Kalman filter.
Installation requires numpy and scipy. Clone the repo and then run the setup script
git clone https://github.com/rwalk/rsk
cd rsk
python setup.py installOnce the project has stabilized, we'll probably put it up on pypi to make it pip installable.
We have a few tests that check the results of our python implementation against the original Ox implementation. To run these tests, from the root of the project execute:
python -m unittestBecause panel data/cross sectional time series data can be quite tricky to manage, we've implemented the PanelSeries interface to streamline computation
with the RSK filter. There are several ways to use PanelSeries.
To use a Pandas DataFrames with the RSK model, we need to convert it to a PanelSeries. For this, PanelSeries offers a simple from_df method:
import pandas as pd
import scipy as sp
import random
from rsk import RSK
from rsk.panel import PanelSeries
# Imagine we survey residents of Endor and ask them to estimate the number of Ewoks and the number of Rebels present
# in their region.
data = [
["0", "Eastern Territory of Endor", 2, 1],
["0", "Eastern Territory of Endor", 0, 23],
["0", "Eastern Territory of Endor", 5, -19],
["0", "Western Territory of Endor", 1, 1],
["0", "Western Territory of Endor", -1, 2],
["0", "Western Territory of Endor", 8,9],
["1", "Eastern Territory of Endor", 1,0],
["1", "Eastern Territory of Endor", 0, 22],
["1", "Eastern Territory of Endor", 4, -17],
["1", "Western Territory of Endor", 2,0],
["1", "Western Territory of Endor", 0,0],
["1", "Western Territory of Endor", 7,10]
]
# order of the rows in the data frame doesn't matter...
random.shuffle(data)
df = pd.DataFrame.from_records(data, columns=["time", "region", "ewoks", "rebels"])
# specify the time and group variable names, as well as the names of the numeric columns to which we want to apply the RSK filter
panel_series = PanelSeries.from_df(data, "time", "region", "ewoks", "rebels")
# apply RSK filtering
# this example has 2 variables "rebels" and "ewoks" and two groups "Western" and "Eastern". So we are computing four filtered means
# If we apply a random walk, then n_alpha=4 so that each each mean can evolve according to a_i[t+1] = a_i[t] + e
translation_matrix = sp.eye(4)
transition_matrix = sp.eye(4)
a0 = 0.0001*sp.ones((4,1))
Q0 = 0.001*sp.eye(4)
rsk = RSK(transition_matrix, translation_matrix)
fitted_means = rsk.fit_em(panel_series, a0, Q0)
print(fitted_means)The time and group indices specify the index of the column in the csv for the time and group identifier
variables. In this case jedi.csv should look like this:
time,region,ewoks,rebels
0,Eastern Territory of Endor,2,1
0,Eastern Territory of Endor,0,23
0,Eastern Territory of Endor,5,-19
0,Western Territory of Endor,1,1
0,Western Territory of Endor,-1,2
0,Western Territory of Endor,8,9
1,Eastern Territory of Endor,1,0
1,Eastern Territory of Endor,0,22
1,Eastern Territory of Endor,4,-17
1,Western Territory of Endor,2,0
1,Western Territory of Endor,0,0
1,Western Territory of Endor,7,10
To create a PanelSeries from this file:
from rsk.panel import PanelSeries
time_index, group_index = 0,1
panel_series = PanelSeries.from_csv("jedi.csv", time_index, group_index, header=True)All variables except for the group and time identifiers must be numeric.
The RSK filter is implemented in the RSK class. Initialize the class with the transition and translation matrices:
from rsk import RSK
rsk_filter = RSK(transition_matrix, translation_matrix)The transition matrix is an n_alpha by n_alpha array modelling the transition dynamics of the latent alpha vector.
The translation matrix is an n_vars by n_alpha array mapping the latent vector alpha back into fitted sample means.
To apply the repeated surveys Kalman filter, call the fit method on an RSK instance, passing in a PanelSeries object:
fitted_means = rsk_filter.fit(panel_series, a0, Q0, Q, sigma=None)In most cases, Q and sigma are unknown and will need to be estimated. In this case, use the fit_em method
fitted_means, sigma = rsk_filter.fit_em(panel_series, a0, Q0, sigma0)This method runs the EM algorithm developed by Lind. When working with fit_em, the algorithm may sometimes fail to converege.
When this happens, using a smaller Q0 may help.
The resulting fitted_means object from fit and fit_em is an n_periods by n_vars matrix containing the means estimated by the RSK algorithm. After fit has been applied, the rsk.alpha vector and other fitted parameters become available as attributes of the RSK
instance.
| Variable | Code | Description |
|---|---|---|
| T | n_periods | Number of point in time measurements |
| N | n_individuals | Number of individuals |
| m | n_vars | Number of observed variables per individual per time slice |
| n | n_alpha | Length of the α vector |
| F | transition_matrix | Markov transition matrix |
| Z | translation_matrix | Translates α into group means μ |