What is the best method of estimating a ratio of regression coefficients $\beta_1/\beta_2$ under the usual assumptions / in practice? I have two relatively well approximated signals $X_1, X_2$ and they have been multiplied by unknown coefficients $\beta_1, \beta_2$. Then the output has been drenched in other noise signals with relatively normal and zero mean distributions, thus yielding the standard regression model, where $Y$ is the observed data


Estimating the ratio by standard OLS is not straight forward, since (assuming independence - which should not be assumed in general) $$\mathbb{E}(\hat \beta_1/\hat \beta_2) =\mathbb{E}(\hat \beta_1)\mathbb{E}({\frac{1}{\hat \beta_2}})=\infty,$$ showing that the trivial estimator of using the ratio of OLS estimates $\hat \beta_1/\hat \beta_2$ is biased when estimating $\beta_1 /\beta_2 = \mathbb{E}(\hat \beta_1)/\mathbb{E}(\hat \beta_2)$. The last equality is due to the infinite variance of the reciprocal normal distribution.

What is an unbiased and the most efficient way of estimating the ratio? Is there a source for rigorously developed estimators for the scenario? Particularly I am interested in obtaining as sharp CI as possible.

Below is a straight forward code for trying different estimators. Decreasing the frequency difference between the sinusoids 1600, 1623 makes the problem more difficult and increases the bias. Note that the toy example may not generalize to more complicated scenarios, but functions as a useful work bench.

import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
def ratioestimator(beta1=0.5, beta2=1.0, N=40000, n=500):
    for i in range(N):
        eps = np.random.normal(0,3,n)
        M = sm.OLS(Y, np.column_stack((np.ones(len(X1)),X1,X2))).fit()
# Analysis:  

print("Mean of estimates: ", np.mean(b_strap))
print("Median of estimates: ", np.median(b_strap))
print("10% CI of estimates:", np.percentile(b_strap,45),np.percentile(b_strap,55))
print("50% CI of estimates:", np.percentile(b_strap,25),np.percentile(b_strap,75))
print("80% CI of estimates:", np.percentile(b_strap,10),np.percentile(b_strap,90))
print("95% CI of estimates:", np.percentile(b_strap,2.5),np.percentile(b_strap,97.5))

  • $\begingroup$ Why does this show the "trivial estimator" is biased? You seem to conflate distributions of estimates with distributions of coefficients; but regardless, under your initial assumptions the expectation of the ratio is undefined. // The median will be biased because the ratio will usually have a skewed distribution. // Bootstrapping cannot rescue you when you are estimating an undefined quantity. // So, please clarify whether you are interested in the ratio of estimated coefficients or ratio of the coefficients themselves; and if it's the latter, what are your distributional assumptions? $\endgroup$
    – whuber
    Aug 28, 2023 at 20:32
  • $\begingroup$ @whuber More detail added. I am interested in the actual population ratio of the coefficients. The assumptions are "usual" regression assumptions (Gauss-Markov, even normality), however the question is still motivated by achieving a practical estimator. $\endgroup$ Aug 28, 2023 at 20:42
  • $\begingroup$ @whuber And to be clear, the estimator does not need to rely on OLS at all, any method suffices. $\endgroup$ Aug 28, 2023 at 20:46
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    $\begingroup$ @whuber I have once more edited the question. Hopefully it is now clear what the context and motivation is. $\endgroup$ Aug 29, 2023 at 0:25
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    $\begingroup$ FWIW, the distribution of the ratio is known and complicated (it can be multimodal): see en.wikipedia.org/wiki/…. The question has to come down to what properties you require of any estimator, such as being unbiased, or minimum variance, or robust, or easily computed, or something else. Secondary considerations include how much data you have, the odds that $\hat\beta_2$ is positive, and how accurate the "usual assumptions" are concerning their implications for this distribution. $\endgroup$
    – whuber
    Aug 29, 2023 at 20:22

1 Answer 1


Reformulate the problem and maybe use nonlinear regression. If your regression model is $$ y_i=\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2} + \epsilon_i $$ and the interest parameter is $\theta =\frac{\beta_1}{\beta_2}$ write the model as $$y_i=\beta_0 + \theta \beta_2 x_{i1} + \beta_2 x_{i2} + \epsilon_i = \\ \beta_0 + \beta_2 \left\{ \theta x_{1i} +x_{i2} \right\} + \epsilon_i $$ This model is no longer linear in the parameters, but it can be fitted via nonlinear least squares as at Confidence interval for GLM or the maximum of a function?. Similar ideas can be adapted for other kinds of regression models.

Then finally you can do inference for $\theta$ using profile likelihood, search this site, and also see the linked post above.


Adding an example with comparisions, first simulating some data with R:

b0 <- 1
b1 <- 1
b2 <- -1   # theta=-1

set.seed(7*11*13) # My public seed

x1 <- seq(from=-5,  to=5,  by=1/3)
x2 <- sample(x1)   # Random permutation

Y <- b0 + b1*x1  +  b2*x2  +  rnorm(length(x1), 0, 2)

mydata <- data.frame(Y, x1, x2)

mod_lm <- lm(Y ~ x1 + x2, data=mydata)

mod0 <- nls(Y ~ b0  + b2 * (theta*x1+x2), data=mydata,
            start=list(b0=0, b2=0.5, theta=-2))  

Then with mod0 making a confidence interval using profile likelihood:

confint(mod0, parm ="theta")   
Waiting for profiling to be done...
      2.5%      97.5% 
-2.4700532 -0.8152139 

Then comparing with the delta method:

car::deltaMethod(mod_lm, "x1/x2")  
     Estimate       SE    2.5 %  97.5 %
x1/x2 -1.35072  0.34126 -2.01957 -0.6819

This intervals are quite different, and I am somewhat surprised about the magnitude of the difference. It is an exercise to do simulations to compare the quality of the intervals!

For the example I have used a value for $\beta_2$ which makes 0 a quite unlikely value. If you redo the simulations with $\beta_2$ closer to zero, this functions used will have problems. Below I will look into that problem ... (later)

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    $\begingroup$ Thank you for the answer. I don't believe you need non-linear least squares to estimate. Simply estimate the the first equation using OLS and substitute the variables. it should be the same minimum. What makes the problem difficult is that the "standard" estimate is not ideal. I will search the profile likelihood method though and see if it helps. $\endgroup$ Aug 28, 2023 at 23:05
  • $\begingroup$ And yes, that is the regression model! $\endgroup$ Aug 28, 2023 at 23:08
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    $\begingroup$ This proposal is nearly equivalent to OLS fit using Maximum Likelihood and applying the "Delta Method" to its estimates. Where it can be of advantage is in exploring the likelihood function in a neighborhood of its maximum in terms of the parameter $\theta$ instead of the original coefficients. Especially with a ratio where the denominator has an appreciable change of being $0,$ the likelihood might not be well characterized by the usual second-order approximation (aka Fisher Information matrix). $\endgroup$
    – whuber
    Aug 29, 2023 at 14:59

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