diff --git a/Scripts/create_tables.R b/Scripts/create_tables.R
index 588d3926bde0ed4baf1cbc8f1f5c1f4588662f55..45600ebe9b2d13d0c9d73a73b421b557e1109bb7 100644
--- a/Scripts/create_tables.R
+++ b/Scripts/create_tables.R
@@ -26,7 +26,7 @@ texreg(l=list(ols_percentage_correct_control_A, ols_time_spent_control_A, ols_ti
natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
- label = "tab:mani",
+ label = "tab:olsA",
caption = "Results of OLS regressions for Scenario Case A.",
file="Tables/ols/ols_A.tex")
@@ -44,7 +44,7 @@ texreg(l=list(ols_percentage_correct_control_D, ols_time_spent_control_D, ols_ti
natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
- label = "tab:mani",
+ label = "tab:olsD",
caption = "Results of OLS regressions for Scenario Case B.",
file="Tables/ols/ols_D.tex")
diff --git a/Scripts/data_prep.R b/Scripts/data_prep.R
index 8f695328cc4319720b79fe9dda01c1116d91112a..5627a1cf170e9f75fbb5910c57f0da1aae6f28fe 100644
--- a/Scripts/data_prep.R
+++ b/Scripts/data_prep.R
@@ -19,7 +19,8 @@ database_full <- database_full %>% mutate(Gender_female = case_when(Gender == 2
Kids_Dummy = case_when(Number_Kids > 0 ~ 1, TRUE ~0),
Employment_full = case_when(Employment_type == 1 ~ 1, TRUE~0),
Pensioner = case_when(Employment_type == 6 ~ 1, TRUE~0),
- Age_mean = Age - mean(Age))
+ Age_mean = Age - mean(Age),
+ Income_mean = QFIncome - mean(QFIncome))
# Data cleaning
diff --git a/Scripts/mxl/mxl_wtp_space_caseD_RentINT_NR.R b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_NR.R
new file mode 100644
index 0000000000000000000000000000000000000000..d0d686c41cd9b909b7619fb4928e056dfb489444
--- /dev/null
+++ b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_NR.R
@@ -0,0 +1,187 @@
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+
+# Test treatment effect
+
+database <- database_full %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0))
+
+table(database$Dummy_Treated)
+table(database$Dummy_Vol_Treated)
+table(database$Dummy_no_info)
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Case_D_NR",
+ modelDescr = "MXL wtp space Case D NR",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(mu_natural = 15,
+ mu_walking = -1,
+ mu_rent = -2,
+ ASC_sq = 0,
+ mu_ASC_sq_treated = 0,
+ mu_ASC_sq_vol_treated = 0,
+ mu_ASC_sq_no_info = 0,
+ mu_ASC_NR = 0,
+ mu_rent_treated = 0,
+ mu_rent_vol_treated = 0,
+ mu_rent_no_info = 0,
+ mu_rent_NR = 0,
+ mu_nat_treated =0,
+ mu_nat_vol_treated = 0,
+ mu_nat_no_info = 0,
+ mu_nat_NR = 0,
+ mu_walking_treated =0,
+ mu_walking_vol_treated = 0,
+ mu_walking_no_info = 0,
+ mu_walking_NR = 0,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 2)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+### Set parameters for generating draws, use 2000 sobol draws
+apollo_draws = list(
+ interDrawsType = "sobol",
+ interNDraws = n_draws,
+ interUnifDraws = c(),
+ interNormDraws = c("draws_natural", "draws_walking", "draws_rent", "draws_asc"),
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters, define distribution of the parameters
+apollo_randCoeff = function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ randcoeff[["b_mu_natural"]] = mu_natural + sig_natural * draws_natural
+ randcoeff[["b_mu_walking"]] = mu_walking + sig_walking * draws_walking
+ randcoeff[["b_mu_rent"]] = -exp(mu_rent + sig_rent * draws_rent)
+ randcoeff[["b_ASC_sq"]] = ASC_sq + sig_ASC_sq * draws_asc
+
+ return(randcoeff)
+}
+
+
+### validate
+apollo_inputs = apollo_validateInputs()
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ #### List of utilities (later integrated in mnl_settings below) ####
+ # Define utility functions here:
+
+ V = list()
+ V[['alt1']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR)*
+ (b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ + mu_nat_treated * Naturalness_1 *Dummy_Treated + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_1 *Dummy_Treated + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated
+ + mu_nat_NR * Z_Mean_NR *Naturalness_1 +
+ + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
+ - Rent_1)
+
+ V[['alt2']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR)*
+ (b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ + mu_nat_treated * Naturalness_2 *Dummy_Treated + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_2 *Dummy_Treated + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated
+ + mu_nat_NR * Z_Mean_NR *Naturalness_2 +
+ + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
+ - Rent_2)
+
+ V[['alt3']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR)*
+ (b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ + mu_nat_treated * Naturalness_3 *Dummy_Treated + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_3 *Dummy_Treated + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
+ + mu_ASC_sq_treated * Dummy_Treated + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
+ + mu_ASC_sq_no_info * Dummy_no_info
+ + mu_ASC_NR * Z_Mean_NR
+ + mu_nat_NR * Z_Mean_NR *Naturalness_3
+ + mu_walking_NR * Z_Mean_NR * WalkingDistance_3
+ - Rent_3)
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3),
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = choice,
+ V = V#, # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
+
+
+# ################################################################# #
+#### MODEL ESTIMATION ##
+# ################################################################# #
+# estimate model with bfgs algorithm
+
+mxl_wtp_case_d_rent_NR = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(mxl_wtp_case_d_rent_NR)
+
+
diff --git a/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
index 612c5147efe4a31b13964b4ad4d2bb2687a67d70..2b16bb087a277655f659d410f95011ad3355c802 100644
--- a/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
+++ b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
@@ -113,31 +113,31 @@ apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimat
V = list()
V[['alt1']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * QFIncome)*
+ + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ mu_nat_treated * Naturalness_1 *Dummy_Treated + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated
+ mu_walking_treated * WalkingDistance_1 *Dummy_Treated + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated
+ mu_nat_NR * Z_Mean_NR *Naturalness_1 + mu_nat_Age * Age_mean * Naturalness_1
- + mu_nat_Income * QFIncome * Naturalness_1 + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
- + mu_walking_Age * Age_mean * WalkingDistance_1 + mu_walking_Income * QFIncome * WalkingDistance_1
+ + mu_nat_Income * Income_mean * Naturalness_1 + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
+ + mu_walking_Age * Age_mean * WalkingDistance_1 + mu_walking_Income * Income_mean * WalkingDistance_1
- Rent_1)
V[['alt2']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * QFIncome)*
+ + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ mu_nat_treated * Naturalness_2 *Dummy_Treated + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated
+ mu_walking_treated * WalkingDistance_2 *Dummy_Treated + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated
+ mu_nat_NR * Z_Mean_NR *Naturalness_2 + mu_nat_Age * Age_mean * Naturalness_2
- + mu_nat_Income * QFIncome * Naturalness_2 + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
- + mu_walking_Age * Age_mean * WalkingDistance_2 + mu_walking_Income * QFIncome * WalkingDistance_2
+ + mu_nat_Income * Income_mean * Naturalness_2 + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
+ + mu_walking_Age * Age_mean * WalkingDistance_2 + mu_walking_Income * Income_mean * WalkingDistance_2
- Rent_2)
V[['alt3']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * QFIncome)*
+ + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ mu_nat_treated * Naturalness_3 *Dummy_Treated + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated
@@ -145,10 +145,10 @@ apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimat
+ mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
+ mu_ASC_sq_treated * Dummy_Treated + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
+ mu_ASC_sq_no_info * Dummy_no_info
- + mu_ASC_NR * Z_Mean_NR + mu_ASC_Age * Age_mean + mu_ASC_Income * QFIncome
+ + mu_ASC_NR * Z_Mean_NR + mu_ASC_Age * Age_mean + mu_ASC_Income * Income_mean
+ mu_nat_NR * Z_Mean_NR *Naturalness_3 + mu_nat_Age * Age_mean * Naturalness_3
- + mu_nat_Income * QFIncome * Naturalness_3 + mu_walking_NR * Z_Mean_NR * WalkingDistance_3
- + mu_walking_Age * Age_mean * WalkingDistance_3 + mu_walking_Income * QFIncome * WalkingDistance_3
+ + mu_nat_Income * Income_mean * Naturalness_3 + mu_walking_NR * Z_Mean_NR * WalkingDistance_3
+ + mu_walking_Age * Age_mean * WalkingDistance_3 + mu_walking_Income * Income_mean * WalkingDistance_3
- Rent_3)