# U.S. Commercial Banks Data `banks00_07` data frame contains selected variables for 500 (randomly sampled from around 5000) U.S. commercial banks from data of Koetter et al. (2012) for years 2000-2007. This data are used for illustrution purposes and are not suitable for research purposes. data ```r data(banks00_07) ``` ## Format This data frame contains the following variables (columns): - **`year`**: Year. - **`id`**: Entity ID. - **`TA`**: Gross total assets. - **`LLP`**: Loan loss provisions. - **`Y1`**: Total securities (in thousands of US dollars). - **`Y2`**: Total loans and leases (in thousands of US dollars). - **`W1`**: Cost of fixed assets divided by the cost of borrowed funds. - **`W2`**: Cost of labor (in thousands of US dollars) divided by the cost of borrowed funds. - **`ER`**: Gross total equity to gross total assets ratio. - **`TC`**: Total operating cost. - **`LA`**: Total loans and leases to gross total assets ratio. - **`Ti`**: Times bank is observed. - **`TA_ave`**: Mean value of TA. - **`TA_initial`**: Value of TA in the first observed year. - **`LLP_ave`**: Mean value of LLP. - **`LLP_initial`**: Value of LLP in the first observed year. - **`ER_ave`**: Mean value of ER. - **`ER_initial`**: Value of ER in the first observed year. - **`LA_ave`**: Mean value of LA. - **`LA_initial`**: Value of LA in the first observed year. ## Details The data were sampled and generated as shown in section "Examples". ## Examples ```r ## Not run: # Download data from the link in "Source" banks00_07 <- read.delim("2b_QLH.txt") # rename 'entity' to 'id' colnames(banks00_07) [colnames(banks00_07) == "entity"] <- "id" table(banks00_07$year) # keep if 2000 -- 2007 banks00_07 <- banks00_07[(banks00_07$year >= 2000 & banks00_07$year <= 2007),] dim(banks00_07) q1q3 <- quantile(banks00_07$TA, probs = c(.25,.75)) banks00_07 <- banks00_07[(banks00_07$TA >= q1q3[1] & banks00_07$TA <= q1q3[2]),] dim(banks00_07) # generate required variables banks00_07$TC <-banks00_07$TOC banks00_07$ER <- banks00_07$Z / banks00_07$TA banks00_07$LA <- banks00_07$Y2 / banks00_07$TA banks00_07 <- banks00_07[, colnames(banks00_07) c("id", "year", "Ti", "TC", "Y1", "Y2", "W1","W2", "ER", "LA", "TA", "LLP")] dim(banks00_07) t0 <- as.vector( by(data = banks00_07$id, INDICES = banks00_07$id, FUN = function(qq) length(qq)) ) banks00_07$Ti <- rep(t0, times = t0) banks00_07 <- banks00_07[banks00_07$Ti > 4,] # complete observations banks00_07 <- banks00_07[complete.cases(banks00_07),] dim(banks00_07) id_names <- unique(banks00_07$id) N_total <- length(id_names) set.seed(816376586) ids_n2choose <- sample(1:N_total, 500) ids2choose <- id_names[ids_n2choose] banks00_07 <- banks00_07[banks00_07$id dim(banks00_07) t0 <- as.vector( by(data = banks00_07$id, INDICES = banks00_07$id, FUN = function(qq) length(qq)) ) length(rep(t0, times = t0)) banks00_07$Ti <- rep(t0, times = t0) banks00_07[1:50,c("id","year","Ti")] # keep if Ti > 4 banks00_07 <- banks00_07[banks00_07$Ti > 4,] dim(banks00_07) # sort banks00_07 <- banks00_07[order(banks00_07$id, banks00_07$year),] # TC = TOC # # ER = Z / TA # Gross total equity to gross total assets ratio. # # LA = Y2 / TA # Total loans and leases to gross total assets ratio. banks00_07$TA_ave <- rep(as.vector( by(data = banks00_07$TA, INDICES = banks00_07$id, FUN = function(qq) mean(qq))), times = t0) banks00_07$TA_initial <- rep(as.vector( by(data = banks00_07$TA, INDICES = banks00_07$id, FUN = function(qq) qq[1])), times = t0) banks00_07$LLP_ave <- rep(as.vector( by(data = banks00_07$LLP, INDICES = banks00_07$id, FUN = function(qq) mean(qq))), times = t0) banks00_07$LLP_initial <- rep(as.vector( by(data = banks00_07$LLP, INDICES = banks00_07$id, FUN = function(qq) qq[1])), times = t0) banks00_07$ER_ave <- rep(as.vector( by(data = banks00_07$ER, INDICES = banks00_07$id, FUN = function(qq) mean(qq))), times = t0) banks00_07$ER_initial <- rep(as.vector( by(data = banks00_07$ER, INDICES = banks00_07$id, FUN = function(qq) qq[1])), times = t0) banks00_07$LA_ave <- rep(as.vector( by(data = banks00_07$LA, INDICES = banks00_07$id, FUN = function(qq) mean(qq))), times = t0) banks00_07$LA_initial <- rep(as.vector( by(data = banks00_07$LA, INDICES = banks00_07$id, FUN = function(qq) qq[1])), times = t0) cols2export <- c("id","year","Ti","TA","TA_ave", "TA_initial","LLP","LLP_ave", "LLP_initial","ER_ave","ER_initial","LA_ave","LA_initial") write.table(x = banks00_07, file = "banks00_07.txt", row.names = FALSE) ## End(Not run) ``` ## Source [http://qed.econ.queensu.ca/jae/2014-v29.2/restrepo-tobon-kumbhakar/](http://qed.econ.queensu.ca/jae/2014-v29.2/restrepo-tobon-kumbhakar/). ## References Koetter, M., Kolari, J., and Spierdijk, L. (2012), Enjoying the quiet life under deregulation? Evidence from adjusted Lerner indices for U.S. banks. **Review of Economics and Statistics**, 94 , 2, 462--480. Restrepo-Tobon, D. and Kumbhakar, S. (2014), Enjoying the quiet life under deregulation? Not Quite. **Journal of Applied Econometrics**, 29 , 2, 333--343.

banks00_07 function