as the title suggests, where has the rolling function option in the ols command in Pandas migrated to in statsmodels? I can\'t seem to find it. Pandas tells me doom is in th
Adding for completeness a speedier numpy
-only solution which limits calculations only to the regression coefficients and the final estimate
Numpy rolling regression function
import numpy as np
def rolling_regression(y, x, window=60):
"""
y and x must be pandas.Series
"""
# === Clean-up ============================================================
x = x.dropna()
y = y.dropna()
# === Trim acc to shortest ================================================
if x.index.size > y.index.size:
x = x[y.index]
else:
y = y[x.index]
# === Verify enough space =================================================
if x.index.size < window:
return None
else:
# === Add a constant if needed ========================================
X = x.to_frame()
X['c'] = 1
# === Loop... this can be improved ====================================
estimate_data = []
for i in range(window, x.index.size+1):
X_slice = X.values[i-window:i,:] # always index in np as opposed to pandas, much faster
y_slice = y.values[i-window:i]
coeff = np.dot(np.dot(np.linalg.inv(np.dot(X_slice.T, X_slice)), X_slice.T), y_slice)
estimate_data.append(coeff[0] * x.values[window-1] + coeff[1])
# === Assemble ========================================================
estimate = pandas.Series(data=estimate_data, index=x.index[window-1:])
return estimate
Notes
In some specific case uses, which only require the final estimate of the regression, x.rolling(window=60).apply(my_ols)
appears to be somewhat slow
As a reminder, the coefficients for a regression can be calculated as a matrix product, as you can read on wikipedia's least squares page. This approach via numpy
's matrix multiplication can speed up the process somewhat vs using the ols in statsmodels
. This product is expressed in the line starting as coeff = ...