## Maximum likelihood estimation by mlexp: A chi-squared example

**Overview**

In this post, I show how to use **mlexp** to estimate the degree of freedom parameter of a chi-squared distribution by maximum likelihood (ML). One example is unconditional, and another example models the parameter as a function of covariates. I also show how to generate data from chi-squared distributions and I illustrate how to use simulation methods to understand an estimation technique.

**The data**

I want to show how to draw data from a \(\chi^2\) distribution, and I want to illustrate that the ML estimator produces estimates close to the truth, so I use simulated data.

In the output below, I draw a \(2,000\) observation random sample of data from a \(\chi^2\) distribution with \(2\) degrees of freedom, denoted by \(\chi^2(2)\), and I summarize the results.

**Example 1: Generating \(\chi^2(2)\) data**

. drop _all . set obs 2000 number of observations (_N) was 0, now 2,000 . set seed 12345 . generate y = rchi2(2) . summarize y Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- y | 2,000 2.030865 1.990052 .0028283 13.88213

The mean and variance of the \(\chi^2(2)\) distribution are \(2\) and \(4\), respectively. The sample mean of \(2.03\) and the sample variance of \(3.96=1.99^2\) are close to the true values. I set the random-number seed to \(12345\) so that you can replicate my example; type **help seed** for details.

**mlexp and the log-likelihood function**

The log-likelihood function for the ML estimator for the degree of freedom parameter \(d\) of a \(\chi^2(d)\) distribution is

\[

{\mathcal L}(d) = \sum_{i=1}^N \ln[f(y_i,d)]

\]

where \(f(y_i,d)\) is the density function for the \(\chi^2(d)\) distribution. See Trivedi, 2005 and Wooldridge, 2010 for instructions to ML.

The **mlexp** command estimates parameters by maximizing the specified log-likelihood function. You specify the contribution of an observation to the log-likelihood function inside parentheses, and you enclose parameters inside the curly braces \(\{\) and \(\}\). I use **mlexp** to estimate \(d\) in example 2.

**Example 2: Using mlexp to estimate \(d\)**

. mlexp ( ln(chi2den({d},y)) ) initial: log likelihood = -(could not be evaluated) feasible: log likelihood = -5168.1594 rescale: log likelihood = -3417.1592 Iteration 0: log likelihood = -3417.1592 Iteration 1: log likelihood = -3416.7063 Iteration 2: log likelihood = -3416.7063 Maximum likelihood estimation Log likelihood = -3416.7063 Number of obs = 2,000 ------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- /d | 2.033457 .0352936 57.62 0.000 1.964283 2.102631 ------------------------------------------------------------------------------

The estimate of \(d\) is very close to the true value of \(2.0\), as expected.

**Modeling the degree of freedom as a function of a covariate**

When using ML in applied research, we almost always want to model the parameters of a distribution as a function of covariates. Below, I draw a covariate \(x\) from Uniform(0,3) distribution, specify that \(d=1+x\), and draw \(y\) from a \(\chi^2(d)\) distribution conditional on \(x\). Having drawn data from the DGP, I estimate the parameters using **mlexp**.

**Example 3: Using mlexp to estimate \(d=a+b x_i\)**

. drop _all . set obs 2000 number of observations (_N) was 0, now 2,000 . set seed 12345 . generate x = runiform(0,3) . generate d = 1 + x . generate y = rchi2(d) . mlexp ( ln(chi2den({b}*x +{a},y)) ) initial: log likelihood = -(could not be evaluated) feasible: log likelihood = -4260.0685 rescale: log likelihood = -3597.6271 rescale eq: log likelihood = -3597.6271 Iteration 0: log likelihood = -3597.6271 Iteration 1: log likelihood = -3596.5383 Iteration 2: log likelihood = -3596.538 Maximum likelihood estimation Log likelihood = -3596.538 Number of obs = 2,000 ------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- /b | 1.061621 .0430846 24.64 0.000 .9771766 1.146065 /a | .9524136 .0545551 17.46 0.000 .8454876 1.05934 ------------------------------------------------------------------------------

The estimates of \(1.06\) and \(0.95\) are close to their true values.

**mlexp** makes this process easier by forming a linear combination of variables that you specify.

**Example 4: A linear combination in mlexp**

. mlexp ( ln(chi2den({xb: x _cons},y)) ) initial: log likelihood = -(could not be evaluated) feasible: log likelihood = -5916.7648 rescale: log likelihood = -3916.6106 Iteration 0: log likelihood = -3916.6106 Iteration 1: log likelihood = -3621.2905 Iteration 2: log likelihood = -3596.5845 Iteration 3: log likelihood = -3596.538 Iteration 4: log likelihood = -3596.538 Maximum likelihood estimation Log likelihood = -3596.538 Number of obs = 2,000 ------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | 1.061621 .0430846 24.64 0.000 .9771766 1.146065 _cons | .9524138 .0545551 17.46 0.000 .8454878 1.05934 ------------------------------------------------------------------------------

The estimates are the same as in example 3, but the command was easier to write and the output is easier to read.

**Done and undone**

I have shown how to generate data from a \(\chi^2(d)\) distribution when \(d\) is a fixed number or a linear function of a covariate and how to estimate \(d\) or the parameters of the model for \(d\) by using **mlexp**.

The examples discussed above show how to use **mlexp** and illustrate an example of conditional maximum likelihood estimation.

**mlexp** can do much more than I have discussed here; see **[R] mlexp** for more details. Estimating the parameters of a conditional distribution is only the beginning of any research project. I will discuss interpreting these parameters in a future post.

**References**

Cameron, A. C., and P. K. Trivedi. 2005. *Microeconometrics: Methods and applications*. Cambridge: Cambridge University Press.

Wooldridge, J. M. 2010. *Econometric Analysis of Cross Section and Panel Data*. 2nd ed. Cambridge, Massachusetts: MIT Press.