Last Update: February 21, 2022
Linearity in Parameters: Ramsey RESET Test in Python can be done using
linear_reset function found within
statsmodels.stats.diagnostic module for evaluating whether linear regression fitted values non-linear combinations explain dependent variable. Main parameters within
linear_reset function are
res with original model results,
power with augmented model added independent variables powers,
test_type with original model fitted values, independent variables or independent variables first principal component as augmented model added independent variables and
use_f with logical value on whether an F-test or chi-square test should be done.
As example, we can do Ramsey RESET test on multiple linear regression of house price explained by its lot size and number of bedrooms using data included within
AER R package
HousePrices object .
First, we import
statmodels package for data downloading, multiple linear regression fitting and Ramsey RESET test .
In : import statsmodels.api as sm import statsmodels.formula.api as smf import statsmodels.stats.diagnostic as smd
Second, we create
houseprices data object using
get_rdataset function and display first five rows and three columns of data using
head data frame method to view its structure.
In : houseprices = sm.datasets.get_rdataset(dataname="HousePrices", package="AER", cache=True).data print(houseprices.iloc[:, 0:3].head())
Out : price lotsize bedrooms 0 42000.0 5850 3 1 38500.0 4000 2 2 49500.0 3060 3 3 60500.0 6650 3 4 61000.0 6360 2
Third, we fit model with
ols function using variables within
houseprices data object and store results within
mlr object. Within
ols function, parameter
formula = “price ~ lotsize + bedrooms” fits model where house price is explained by its lot size and number of bedrooms.
In : mlr = smf.ols(formula="price ~ lotsize + bedrooms", data=houseprices).fit()
Fourth, as example again, we do Ramsey RESET test using
linear_reset function, store results in
resettest object and print it. Within
linear_reset function, parameters
res=mlr includes original model results,
power=2 adds squared independent variable to augmented model,
test_type="fitted" adds original model fitted values as augmented model independent variable and
use_f=True does F-test. Notice that
linear_reset function parameters
use_f=True were only included as educational examples which can be modified according to your needs.
In : resettest = smd.linear_reset(res=mlr, power=2, test_type="fitted", use_f=True) print(resettest)
Out : <F test: F=array([[10.63462745]]), p=0.0011796522160904715, df_denom=542, df_num=1>
My online courses are hosted at Teachable website.
For more details on this concept, you can view my Linear Regression in Python Course.
 Data Description: Sales prices of houses sold in the city of Windsor, Canada, during July, August and September, 1987.
Original Source: Anglin, P., and Gencay, R. (1996). Semiparametric Estimation of a Hedonic Price Function. Journal of Applied Econometrics, 11, 633–648.
 statsmodels Python package: Seabold, Skipper, and Josef Perktold. (2010). “statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference.