This folder contains three groups of self-contained files: First Group: Nested Fixed Point and Nested Pseudo Likelihood------------------ 1) "Rust Data Generating Process.R” illustrates a simple DGP for the Rust bus dataset. It generates the data and estimates the parameters using a nested fixed point algorithm The output file “bus_df_in.csv” is used in the Nested Pseudo Likelihood algorithm 2) "Import Data and Estimate" implements Aguirregabiria and Mira’s (2002) NPL algorithm It imports “bus_df_in.csv” generated above and calls "npl_sing.R" and "clogit.R" Second Group: Arcidiacono and Miller (2011)------------------------------------ 1) “AM2011Table1cols2356.R” recreates columns 2,3,5 and 6 from Arcidiacono and Miller (2011) Data is simulated within the program It calls the following support functions: -xgrid.R,wlogitd.R,wlogit.R,likebusML4.R,genbus4.cpp,fvdataBOTH.cpp,intcond.R,intcondP.R Third Group: Bayesian DDC (2009)------------------------------------------------ *) "BayesianDDCEstimateDataRustvEmax.R" uses the output from "Rust Data Generating Process.R” to replicate the Imai, Jain, and Ching (2009) method, but does not allow for random effects. Use this file to get a base understanding of the model before proceeding to the hierarchical version. 1) "RustDGPwithHierarchicalRE.R" generates Rust data with a hierarchical mixing 2) "EstimateBayesianHierarchicalDDC.R" follows a method similar to Imai, Jain, and Ching (2009) for estimation. See the code for options on variations of the estimation procedure. 3) "EstimateBayesianHierarchicalDDC cpp.R" is similar to #2 but uses c++ for speed improvements. The main c++ program is in "bddcMCMCloop.cpp" IMPORTANT: this is a stylized example meant to highlight the mechanics of the process. Care must be taken when selecting the priors and scaling parameters. Here convergence is achieved, but only because of how I set up the problem.