Contents
How is linear regression used in a Monte Carlo experiment?
This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design.
What are the coefficients of a Monte Carlo model?
The coefficients of the model – β 1, β 2, and β 3 – will be fixed parameters. In practice, we won’t know their true values – indeed, one purpose of the model is to estimate these values. To complete the specification of the model, we need to be really clear about the assumed properties of the random error term, ε.
What should I know about Monte Carlo simulation?
This post is the third in a series of posts that I’m writing about Monte Carlo (MC) simulation, especially as it applies to econometrics. If you’ve already seen the first two posts in the series ( here and here) then you’ll know that my intention is to provide a very elementary introduction to this topic.
How are observations chosen in a Monte Carlo experiment?
In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes.
When does linear regression have better error rates?
If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N 0 = N 1 = 25) linear regression has superior type-1 error rates compared to the other models.
How are Monte Carlo methods used in statistics?
Analyses of this type are called Monte Carlo methods: they randomly sample from a set of quantities for the purpose of generating and summarizing a distribution of some statistic related to the sampled quantities. If this concept is confusing, hopefully this chapter will clarify.
When to wrap code into functions in Monte Carlo analysis?
In Monte Carlo analyses, it is often useful to wrap code into functions. This allows for easy replication and setting adjustment (e.g., if you wanted to compare the growth trajectories of two populations with differing growth rates). As an example, turn the population model shown above into a function: