Introduction

We provide several functions for Monte Carlo simulations to assess the performance of the outlier detection algorithm outlier_detection() and the various statistical tools such as outliers_prop(). The simulations can be executed in parallel using various backends.

Monte Carlo simulations involve the following steps:

1. create or choose a true 2SLS model (including parameters)
2. specify the outlier detection algorithm to be analysed
3. optionally choose which simulation parameters to vary, such as the sample size
4. choose whether to execute the simulations sequentially or in parallel and run the simulations

See the vignette Introduction to the robust2sls Package for more details on the model setup (step 1) and the different algorithms (step 2) that are implemented.

utils::vignette("overview", package = "robust2sls")

Step 1: True model

We conceptualise data as being generated by some true model, the so-called data-generating process (DGP). Specifying a DGP ourselves in simulations, allows us to check whether the theory works in practice. For example, we could use Monte Carlo simulations to check whether the 2SLS estimator recovers the true parameters; whether the proportion of detected outliers corresponds to the expected proportion; or whether a statistical test has expected size even in finite samples.

First of all, we need to specify a valid 2SLS model and its parameters. The function generate_param() can be used to generate random parameters of a 2SLS model that fulfill the 2SLS conditions. For instance, the parameters are created such that the structural error is uncorrelated to the instruments. Instead of random parameters, they can also - partly or fully - be specified by the user.

library(robust2sls)
p <- generate_param(dx1 = 3, dx2 = 2, dz2 = 3, intercept = TRUE, seed = 10)

Here, we create parameters for a model with 3 exogenous and 2 endogenous regressors, and 3 outside instruments. The model includes an intercept, so one of the exogenous instruments is simply a constant. The parameters are stored in a list.

Structural equation: \(y_{i} = \beta_{1} x_{1,i} + \beta_{2} x_{2,i} + \beta_{3} x_{3,i} + \beta_{4} x_{4,i} + \beta_{5} x_{5,i} + u_{i}\)

First stage: \(x_{i} = \Pi^{\prime} z_{i} + r_{i}\),

where the vector \(x_{i}\) contains all the regressors and the vector of instruments \(z_{i}\) contains the 3 exogenous regressors and the two excluded instruments. \(\Pi\) is the matrix of first stage coefficients.

Step 2: Outlier detection algorithm

The workhorse command for different types of trimmed 2SLS algorithms in the robust2sls package is outlier_detection(). The main decisions are

• which initial estimator to use
• how the sample is trimmed, which is governed by
  • the reference distribution against which the errors are judged to be outliers or not
  • the cut-off value \(c\) that determines the size of the standardised errors beyond which observations are labelled as outliers and subsequently removed
• how often the algorithm is iterated, which is represented by the parameter \(m\).

To keep things simple and the run-time of the simulations low, we do not iterate the algorithm in this example. We use the Robustified 2SLS algorithm, which uses the full sample for the initial estimates. As is commonly done, we use the normal distribution as the reference distribution. To target a false outlier detection rate of approximately 5%, we choose a cut-off value of approximately 1.96, meaning that observations with an absolute standardised residual larger than 1.96 are classified as outliers. This is set using the sign_level argument of the function, which together with the reference distribution, ref_dist, automatically determines the cut-off value.

The simulation function mc_grid() also takes these arguments and internally uses them to call the outlier_detection() function repeatedly across replications.

Step 3: Parameter settings

Again to keep the run-time low, we only vary the sample size. We choose small sample sizes of 50 and 100, respectively.

Step 4: Backend for execution

The functions mc() and mc_grid() are designed to be used either sequentially or in parallel. They are implemented using the foreach package. To ensure that the results are reproducible across different ways of executing the simulations (sequentially or parallel; within the latter as multisession, multicore, cluster etc.), the package doRNG is used to execute the loops.

The Monte Carlo functions leave the registration of the foreach adaptor to the end-user. For example, both the packages doParallel and doFuture can be used.

library(parallel)
ncores <- 2
cl <- makeCluster(ncores)
# export libraries to all workers in the cluster
invisible(clusterCall(cl = cl, function(x) .libPaths(x), .libPaths()))

library(doParallel)
#> Loading required package: foreach
#> Loading required package: iterators
registerDoParallel(cl)
sim1 <- mc_grid(M = 100, n = c(100, 1000), seed = 42, parameters = p, 
               formula = p$setting$formula, ref_dist = "normal", 
               sign_level = 0.05, initial_est = "robustified", iterations = 0,
               shuffle = FALSE, shuffle_seed = 42, split = 0.5)

library(doFuture)
#> Loading required package: future
registerDoFuture()
plan(cluster, workers = cl)
sim2 <- mc_grid(M = 100, n = c(100, 1000), seed = 42, parameters = p, 
               formula = p$setting$formula, ref_dist = "normal", 
               sign_level = 0.05, initial_est = "robustified", iterations = 0,
               shuffle = FALSE, shuffle_seed = 42, split = 0.5)
stopCluster(cl)

# check identical results
identical(sim1, sim2)
#> [1] TRUE

library(doFuture)
registerDoFuture()
plan(sequential)
sim3 <- mc_grid(M = 100, n = c(100, 1000), seed = 42, parameters = p, 
               formula = p$setting$formula, ref_dist = "normal", 
               sign_level = 0.05, initial_est = "robustified", iterations = 0,
               shuffle = FALSE, shuffle_seed = 42, split = 0.5)
#> Loading required package: rngtools

# check identical results
identical(sim1, sim3)
#> [1] TRUE