Introductory Stata 52: Data Generating Process and Simulation

We can generate random numbers in Stata. We can control the data-generating process (DGP) and create data with particular properties. The generated variables can be used in the simulation. It is a powerful tool to evaluate different estimators and estimation methods.
Let me show you generating random numbers from the two commonly used distributions, the normal distribution and the uniform distribution. We know that the random number generation functions, such as "rnormal()" and "runiform()," do not produce truly random numbers but pseudorandom numbers. We use the "seed" to determine the sequence of numbers. We can set the "seed" to the same value each time to get the same sequence of pseudorandom numbers.

#RandomNumber #DataGeneratingProcess #Simulation

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*52. Data Generating Process and Simulation *
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clear
capture log close
log using simulation.log, text replace

*Random numbers
set seed 8964
set obs 5000
generate e1=rnormal()
generate e2=runiform()
summarize
histogram e1
histogram e2

generate m=rnormal()

*generate x and y
generate x=m+e1
generate y=1+1*x+(e1+e2)

*OLS
regress y x

*OLS with control
regress y x e1

*IV estimation
generate z=rnormal()
replace x=m+e1+z
replace y=1+1*x+(e1+e2)

regress y x

ivregress 2sls y (x=z), first

eregress y, endogenous(x=z)

log close

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