133 lines
8.0 KiB
R
133 lines
8.0 KiB
R
#####Packages
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library(tidyverse) #Cleaning data
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library(fixest) #Estimating a model of birth rates, to provide variance in the birth rate Monte Carlo using a fixed effect model.
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library(forecast) #Fore ARIMA migration simulations
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library(parallel)
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library(uuid) #To add a index to each batch
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####If the prelimnary data needs to be reloaded run the supplied bash script to download, process, and generate all needed data sets for the Monte Carlo population Simulation. Otherwise skip this step to save time
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RELOAD_DATA <- FALSE
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if(RELOAD_DATA){system("bash Prelim_Process.sh")}
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#Load custom functions needed for the simulation
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source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Increment_Data_Year.r")
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source("Scripts/Load_Custom_Functions/Single_Age_Mortality_Trend_Simulation.r")
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#######Preliminary Model Inputs
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YEARS_AHEAD <- 43
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ST_YEAR <- 2023
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################################Load Data
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DEMO <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2023_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
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BIRTH_MOD <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Birth_Regression.Rds")
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#Must add region as a factor with multiple levels for predict to work. Seems to check for multiple levels although that is not needed econometrics.
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BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Data/Birth_Simulation_Key_Starting_Points.Rds") %>% mutate(Region=factor(Region)) %>% filter(KEM==1,Year==2023)
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MIGRATION_ARIMA <- readRDS("Data/Intermediate_Inputs/Migration_ARIMA_Models/Kemmerer_Diamondville_Net_Migration_ARIMA.Rds")
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MIGRATION_ODDS <- readRDS("Data/Intermediate_Inputs/Migration_Trends/Migration_Age_Probability_Zero_to_85.Rds")
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##############
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#Data for death rate trends
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SINGLE_AGE_MODS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Time_Series_Regression.Rds")
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BOUNDS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Bounds_for_Predictions.Rds")
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MAX_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_RATE)
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MIN_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_RATE)
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MAX_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MAX_RATE)
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MIN_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MIN_RATE)
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MIN_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_MALE_FEMALE_GAP)
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MAX_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_MALE_FEMALE_GAP)
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BASELINE_AGE_ADJUST_MEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Male') %>% pull(Percent_of_Population)
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BASELINE_AGE_ADJUST_WOMEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Female') %>% pull(Percent_of_Population)
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#Adjust to just women popualtion (Not all population percent
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BASELINE_AGE_ADJUST_WOMEN <- BASELINE_AGE_ADJUST_WOMEN/sum(BASELINE_AGE_ADJUST_WOMEN )
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BASELINE_AGE_ADJUST_MEN <- BASELINE_AGE_ADJUST_MEN/ sum(BASELINE_AGE_ADJUST_MEN )
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MOD_MEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Men_Mortality_by_Age.Rds")
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MOD_WOMEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Women_Mortality_by_Age.Rds")
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MOD_LIN_MEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Men_Mortality_by_Age.Rds")
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MOD_LIN_WOMEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Women_Mortality_by_Age.Rds")
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XREG <- cbind(rep(0.0001,YEARS_AHEAD+1),rep(0.0001,YEARS_AHEAD+1)) #Empty data set to simulate in the future
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XREG <- ts(XREG,start=ST_YEAR,frequency=1)
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SIMULATE_MORTALITY_RATE_TRENDS <- function(){
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SIMULATED_MORTALITY_DATA_SET <- MAKE_EMPTY(ST_YEAR,ST_YEAR+YEARS_AHEAD,MOD_LIN_MEN,MOD_LIN_WOMEN,MOD_MEN_ALL,MOD_WOMEN_ALL,XREG)
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MORTALITY_SIMULATION <- AGE_DIST(SINGLE_AGE_MODS,SIMULATED_MORTALITY_DATA_SET ,MAX_MALE,MAX_FEMALE,MIN_MALE,MIN_FEMALE,MAX_GAP,MIN_GAP,BASELINE_AGE_ADJUST_MEN,BASELINE_AGE_ADJUST_WOMEN)
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return(MORTALITY_SIMULATION )
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}
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#####################START YEAR BY SIMULATIONS
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#CURRENT_YEARS_AHEAD=1;CURRENT_SIM_NUM <- 1;MORTALITY_SIMULATION <- SIMULATE_MORTALITY_RATE_TRENDS()
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SINGLE_YEAR_SIM <- function(DEMO,BIRTH_DATA,CURRENT_YEARS_AHEAD,MORTALITY_SIMULATION,NET_MIGRATION){
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ORIG_DEMO <- DEMO
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DEMO <- DEMOGRAPHICS_AFTER_MIGRATION(DEMO, NET_MIGRATION,MIGRATION_ODDS )
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TOTAL_MIGRATION <- sum(DEMO-ORIG_DEMO)
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BIRTH_DATA$Year <- BIRTH_DATA$Year+1
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BIRTH_DATA$Lag_Two_Births <- BIRTH_DATA$Lag_Births
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BIRTH_DATA$Lag_Births <- BIRTH_DATA$Births
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BIRTH_DATA$Births <- NA
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##We grab one year earlier than the window because they are one year older this year. Because the ages are from 0-85, row 18 is year 17, but one year is added making it 18 years in the current year. The birth windows are 18-28 for women and 18-30 for men.
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BIRTH_DATA$Min_Birth_Group <- min(sum(DEMO[18:30,1]),sum(DEMO[18:28,2]))
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NEW_BORNS <- BIRTH_SIM(BIRTH_MOD,BIRTH_DATA)
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TOTAL_BIRTHS <- sum(NEW_BORNS)
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BIRTH_DATA[,"Births"] <- TOTAL_BIRTHS
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DEMO <- INCREMENT_AGES(DEMO,NEW_BORNS)
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MORTALITY_SIMULATION
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MALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,1],MORTALITY_SIMULATION[[1]][x,CURRENT_YEARS_AHEAD])})
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FEMALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,2],MORTALITY_SIMULATION[[2]][x,CURRENT_YEARS_AHEAD])})
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MALE_DEATHS <- ifelse(MALE_DEATHS>=DEMO[,1],DEMO[,1],MALE_DEATHS)
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FEMALE_DEATHS <- ifelse(FEMALE_DEATHS>=DEMO[,1],DEMO[,1],FEMALE_DEATHS)
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TOTAL_DEATHS <- sum(MALE_DEATHS+FEMALE_DEATHS)
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DEMO[,"Num_Male"] <- DEMO[,"Num_Male"] -MALE_DEATHS
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DEMO[,"Num_Female"] <- DEMO[,"Num_Female"] -FEMALE_DEATHS
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#List of values needed for the next run or for reporting a result
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TOTAL_POP <- sum(DEMO)
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return(list(DEMO,BIRTH_DATA,c(TOTAL_POP,TOTAL_BIRTHS,TOTAL_DEATHS,TOTAL_MIGRATION)))
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}
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MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
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SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL){
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TERRA_POWER_EFFECT <- rep(0,YEARS_AHEAD)
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POP_WORK_RATIO <-3716/1920.54 #Total population of Kemmerer in 2024 divided total employment both are found in IMPLAN region details for zip code 83101
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TERRA_POWER_EFFECT[3:7] <- POP_WORK_RATIO*310.75/5 #Total IMPLAN job estimate times adjusted for families and spread over five years
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MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL)+runif(1,-55,0))+TERRA_POWER_EFFECT)
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#The runif applies a downshift ranging from the historic decline rate all the way to the Lincoln rate applied in the model
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FINAL_REPORT_VALUES <- matrix(NA,ncol=6,nrow=YEARS_AHEAD)
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colnames(FINAL_REPORT_VALUES ) <- c("Sim_UUID","Year","Population","Births","Deaths","Net_Migration")
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FINAL_REPORT_VALUES[,1] <- UUIDgenerate()
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for(i in 1:YEARS_AHEAD){
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C_YEAR <- ST_YEAR+i-1
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C_RES <-SINGLE_YEAR_SIM(DEMO,BIRTH_DATA,i,SIMULATE_MORTALITY_RATE_TRENDS(),MIGRATION_SIM_VALUES[i])
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DEMO <- C_RES[[1]]
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BIRTH_DATA <- C_RES[[2]]
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FINAL_REPORT_VALUES[i,-1] <- c(C_YEAR,C_RES[[3]])
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}
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return(FINAL_REPORT_VALUES)
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}
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if(!exists("RES_DIR")){RES_DIR<- "./Results/"}
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if(!exists("RES_SIM_DIR")){RES_SIM_DIR <- paste0(RES_DIR,"Simulations/")}
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dir.create(RES_SIM_DIR, recursive = TRUE, showWarnings = FALSE)
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NCORES <- detectCores()-1
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BATCH_SIZE <- NCORES*10
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TOTAL_SIMULATIONS <- 10^6
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N_RUNS <-ceiling(TOTAL_SIMULATIONS/BATCH_SIZE )
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SIM_RES_FILE <- paste0(RES_SIM_DIR,"Kemmerer_2023_Simulation.csv")
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NEW_RES_FILE <- !file.exists(SIM_RES_FILE)
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for(i in 1:N_RUNS){
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# MIGRATION_MATRIX <- simulate(nsim=BATCH_SIZE,MIGRATION_ARIMA,n=YEARS_AHEAD)
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MIGRATION_MATRIX <- do.call(cbind, mclapply(1:BATCH_SIZE,function(x)(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA) )),mc.cores = detectCores()-1))
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# rownames(MIGRATION_MATRIX) <- ST_YEAR:(ST_YEAR+YEARS_AHEAD-1)
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# colnames(MIGRATION_MATRIX) <- 1:BATCH_SIZE
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try(RES <- do.call(rbind,mclapply(1:BATCH_SIZE,function(x){SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA)},mc.cores=NCORES)))
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if(exists("RES")){
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RES <- as.data.frame(RES)
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RES[,-1] <- as.numeric(as.matrix(RES[,-1]))
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if(NEW_RES_FILE & i==1){write_csv(RES,SIM_RES_FILE)}else {write_csv(RES,SIM_RES_FILE,col_names=FALSE,append=TRUE)}
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rm(RES)
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}
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}
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