diff --git a/R/preprocess2.R b/R/preprocess2.R
index 93832024..19c2cc7e 100644
--- a/R/preprocess2.R
+++ b/R/preprocess2.R
@@ -41,7 +41,9 @@ preprocess2 <- function(object, cluster, fe, param, bootcluster, na_omit) {
     # combine fixed effects in formula with main formula
     # note: you get a warning here if rhs = 2 is empty (no fixed effect specified via fml)
     formula_coef_fe <- suppressWarnings(formula(Formula::as.Formula(formula), lhs = 1, rhs = c(1, 2), collapse = TRUE))
+  
     
+  
     formula <- formula_coef_fe
     
     if (!is.null(eval(of$fixef))) {
@@ -82,11 +84,27 @@ preprocess2 <- function(object, cluster, fe, param, bootcluster, na_omit) {
     of[[1L]] <- quote(stats::model.frame)
     # of is a data.frame that contains all variables: depvar, X, fixed effects and clusters specified
     # in feols and via fe argument
+    
     of <- eval(of, parent.frame())
     
+    # check if one of the fixed effects or cluster variables is not of 
+    # type character or factor
+    fixedid <- object$fixef_vars
+    #to_char <- union(fixedid, cluster)
+    to_char <- fixedid
+    j <- !(sapply(data.frame(of[,c(fixedid)]), class) %in%  c("character", "factor"))
+    if(sum(j) > 0){
+      stop(paste("The fixed effects variable(s)", paste(to_char[j], collapse = " & "), "is/are not factor variables.`feols()` changes the types of fixed effects internally, but this is currently not supported in `boottest()`, but will be implemented in the future. To run the bootstrap, you will have to change the type of all fixed effects variables to factor prior to estimation with `feols()`."))
+    }
+    # # change fixed effects variables and cluster variables to character
+  
+    # of[,to_char] <- sapply(of[,to_char], as.character)
+    # # sapply(of, class)
+
     N_model <- object$nobs
     model_param_names <- c(names(coef(object)), object$fixef)
   } else if (class(object) == "felm") {
+    
     of <- object$call
     o <- match(c("formula", "data", "weights"), names(of), 0L)
     # keep only required arguments
@@ -133,6 +151,21 @@ preprocess2 <- function(object, cluster, fe, param, bootcluster, na_omit) {
     # names(of$fml) <- "formula"
     of <- eval(of, parent.frame())
     
+    # check if one of the fixed effects or cluster variables is not of 
+    # type character or factor
+    fixedid <- names(object$fe)
+    #to_char <- union(fixedid, cluster)
+    to_char <- fixedid
+    j <- !(sapply(data.frame(of[,c(fixedid)]), class) %in%  c("character", "factor"))
+    if(sum(j) > 0){
+      stop(paste("The fixed effects variable(s)", paste(to_char[j], collapse = " & "), "is/are not factor variables.`lfe()` changes the types of fixed effects internally, but this is currently not supported in `boottest()`, but will be implemented in the future. To run the bootstrap, you will have to change the type of all fixed effects variables to factor prior to estimation with `felm()`."))
+    }
+    # # change fixed effects variables and cluster variables to character
+    # fixedid <- names(object$fe)
+    # to_char <- union(fixedid, cluster)
+    # of[,to_char] <- sapply(of[,to_char], as.character)
+    # # sapply(of, class)
+    
     N_model <- object$N
     model_param_names <- rownames(coef(object))
   } else if (class(object) == "lm") {
@@ -163,6 +196,14 @@ preprocess2 <- function(object, cluster, fe, param, bootcluster, na_omit) {
     of <- eval(of, parent.frame())
     # print(of)
     
+    # # set cluster variables to character
+    # to_char <- cluster
+    # of[,to_char] <- sapply(of[,to_char], as.character)
+    # # sapply(of, class)
+    
+    
+    
+    
     N_model <- length(residuals(object))
     model_param_names <- names(coef(object))
     
@@ -226,6 +267,7 @@ preprocess2 <- function(object, cluster, fe, param, bootcluster, na_omit) {
   # Step 3: assign Y, X, weights, fixed_effects, W etc.
   
   model_frame <- of
+
   Y <- model.response(model_frame)
   
   # check if there are no factor variables in the covariates and fixed effects after deletion of fe variable
@@ -239,10 +281,12 @@ preprocess2 <- function(object, cluster, fe, param, bootcluster, na_omit) {
   #
   # X: need to delete clusters
   X <- model.matrix(formula_coef_fe, model_frame)
+  
   if (!is.null(fe)) {
     # note: simply update(..., -1) does not work - intercept is dropped, but all levels of other fe are kept
     X <- X[, -which(colnames(X) == "(Intercept)")]
   }
+  
   k <- dim(X)[2]
   weights <- as.vector(model.weights(of))
   
@@ -261,12 +305,13 @@ preprocess2 <- function(object, cluster, fe, param, bootcluster, na_omit) {
     
     fixed_effect_W <- fixed_effect[, 1]
     if (is.null(weights)) {
-      levels(fixed_effect_W) <- 1 / table(fixed_effect)
+      levels(fixed_effect_W) <- (1 / table(fixed_effect)) # because duplicate levels are forbidden
     } else if (!is.null(weights)) {
       stop("Currently, boottest() does not jointly support regression weights / WLS and fixed effects. If you want to use
            boottest() for inference based on WLS, please set fe = NULL.")
       # levels(fixed_effect_W) <- 1 / table(fixed_effect)
     }
+    
     W <- Matrix::Diagonal(N, as.numeric(as.character(fixed_effect_W)))
     n_fe <- length(unique(fixed_effect[, 1]))
   }
diff --git a/inst/tinytest/test_bootcluster.R b/inst/tinytest/test_bootcluster.R
index 54e15bf4..37fe8442 100644
--- a/inst/tinytest/test_bootcluster.R
+++ b/inst/tinytest/test_bootcluster.R
@@ -13,6 +13,9 @@
 # ---------------------------------------------------------------------------------------------- # 
 # Part A1: no fixed effect in model
 
+library(lfe)
+library(fixest)
+
 lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + as.factor(Q1_immigration) , 
             data = fwildclusterboot:::create_data(N = 10000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
 feols_fit <- feols(proposition_vote ~ treatment + ideology1 + log_income + as.factor(Q1_immigration), 
diff --git a/inst/tinytest/test_numeric_fe_clusters.R b/inst/tinytest/test_numeric_fe_clusters.R
new file mode 100644
index 00000000..606137d0
--- /dev/null
+++ b/inst/tinytest/test_numeric_fe_clusters.R
@@ -0,0 +1,85 @@
+# test issue https://github.com/s3alfisc/fwildclusterboot/issues/14
+# raised by Timoth?e
+# Test 1: one cluster variable is numeric
+# Test 2: one fixed effect is numeric
+# Test 3: both fixed effect and cluster variables are numeric
+
+data(voters)
+
+to_char <- c("Q1_immigration", "Q2_defense", "group_id1")
+sapply(voters[, to_char], class)
+
+
+# Test 1: one cluster variable is numeric
+voters_1 <- voters
+voters_1$Q2_defense <- as.numeric(voters_1$Q2_defense)
+sapply(voters_1[, to_char], class)
+
+feols_fit <- feols(proposition_vote ~ treatment  + log_income | Q2_defense , data = voters)
+feols_fit_2 <- feols(proposition_vote ~ treatment  + log_income | Q2_defense, data = voters_1)
+lfe_fit <- lfe::felm(proposition_vote ~ treatment  + log_income | Q2_defense, data = voters_1)
+
+# bootstrap inference via boottest()
+# expect_no_error
+boottest(feols_fit,
+                       clustid = c("Q1_immigration","Q2_defense"),
+                       B = 9999,
+                       param = "treatment",
+                       bootcluster='min')
+
+expect_error(boottest(feols_fit_2,
+                         clustid = c("Q1_immigration","Q2_defense"),
+                         B = 9999,
+                         param = "treatment",
+                         bootcluster='min'))
+
+expect_error(boottest(lfe_fit,
+                         clustid = c("Q1_immigration","Q2_defense"),
+                         B = 9999,
+                         param = "treatment",
+                         bootcluster='min'))
+
+
+
+# are results the same no matter the type of the clustering variable?
+
+run <- FALSE
+if(run){
+  voters_2 <- voters
+  voters_2$group_id1 <- as.numeric(voters_2$group_id1)
+  
+  feols_fit <- feols(proposition_vote ~ treatment  + log_income  , data = voters)
+  feols_fit_2 <- feols(proposition_vote ~ treatment  + log_income , data = voters_2)
+  lfe_fit <- lfe::felm(proposition_vote ~ treatment  + log_income , data = voters)
+  lfe_fit_2 <- lfe::felm(proposition_vote ~ treatment  + log_income , data = voters_2)
+  
+  feols_boot <- 
+    boottest(feols_fit,
+             clustid = c("Q1_immigration","Q2_defense"),
+             B = 9999,
+             param = "treatment",
+             bootcluster='min')
+  feols_boot_2 <- 
+    boottest(feols_fit_2,
+             clustid = c("Q1_immigration","Q2_defense"),
+             B = 9999,
+             param = "treatment",
+             bootcluster='min')
+  lfe_boot <- 
+    boottest(lfe_fit,
+             clustid = c("Q1_immigration","Q2_defense"),
+             B = 9999,
+             param = "treatment",
+             bootcluster='min')
+  lfe_boot_2 <- 
+    boottest(lfe_fit_2,
+             clustid = c("Q1_immigration","Q2_defense"),
+             B = 9999,
+             param = "treatment",
+             bootcluster='min')
+  expect_equal(feols_boot$p_val, feols_boot_2$p_val)
+  expect_equal(feols_boot$p_val, lfe_boot$p_val)
+  expect_equal(lfe_boot$p_val, lfe_boot_2$p_val)
+  
+  
+}