diff --git a/ARMA_Pop.r b/ARMA_Pop.r
new file mode 100644
index 0000000..b60a2c6
--- /dev/null
+++ b/ARMA_Pop.r
@@ -0,0 +1,22 @@
+library(tidyverse)
+library(forecast)
+
+source("Scripts/Functions.r")
+#source("Scripts/Load_Wyoming_Web_Data.r")
+
+DF <- FRED_GET('WYLINC3POP','LN_POP') %>% select(-YEAR)
+TS <- 1000*ts(DF,start=c(1970),end=c(2024),frequency=1)
+BC <- BoxCox.lambda(TS)
+MODEL <- auto.arima(TS, lambda = BC)
+
+plot(forecast(MODEL,h=35),main="Lincoln County Population Forecast")
+
+#####Plan and ideas
+#1) Review IMPLAN for industry multipliers
+#2) Review IMPLAN for employment to population multipliers (imparted)
+#3) Find a list of all planned new projects
+#4) Use the IMPLAN multipliers for each sector to estimate total change
+#5) Develop survey to estimate likelihood of new projects
+#6) Compare to the ARMA percentile
+#7) Adjust the ARMA up assuming some of these outputs are known.
+####Other ideas, develop larger plan? Maybe look at decline in other industries as a proportion of employment
diff --git a/BVAR_Pop.r b/BVAR_Pop.r
index 5e0b04b..07603a6 100644
--- a/BVAR_Pop.r
+++ b/BVAR_Pop.r
@@ -33,6 +33,7 @@ DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDCARI9POP','CARIBOU_POP',FALS
 DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDBONN0POP','BONNEVILLE_POP',FALSE)
 ###US Population
 DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('POPTOTUSA647NWDB','US_POP',FALSE)
+DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('CE16OV','US_EMP',FALSE)
 
 
 
@@ -44,21 +45,48 @@ for(x in 1:length(DATA_TO_GATHER)){
   if(x==1){RES <- C_DATA}else{RES <- RES %>% full_join(C_DATA)}
   rm(CURRENT,C_DATA)
 }
+
 DATA <- RES %>% mutate(US_POP=US_POP-WY_POP,WY_POP=WY_POP-LN_POP-UINTA_POP-SUBLETTE_POP-SWEETWATER_POP-TETON_POP)
 colnames(DATA)
-TS_DATA_ORIG <- DATA %>% select(YEAR,LN_POP,US_POP,WY_POP,UINTA_POP,SUBLETTE_POP,SWEETWATER_POP,TETON_POP,BEAR_LAKE_POP,CARIBOU_POP,BONNEVILLE_POP) %>% filter(!is.na(LN_POP)) %>% arrange(YEAR) %>% select(-YEAR)
-
+TS_DATA_ORIG <- DATA %>% select(YEAR,LN_POP,LN_EMPLOYMENT,US_EMP,US_POP,WY_POP,UINTA_POP,SUBLETTE_POP,SWEETWATER_POP,TETON_POP,BEAR_LAKE_POP,CARIBOU_POP,BONNEVILLE_POP) %>%
+  filter(!is.na(LN_POP),!is.na(LN_EMPLOYMENT)) %>%
+  arrange(YEAR) %>% select(-YEAR)
+TEST <- RES %>% filter(!is.na(LN_EMPLOYMENT)) %>% mutate(LAG_LN_EMP=lag(LN_EMPLOYMENT))
+TEST %>% select(LN_EMPLOYMENT,LAG_LN_EMP)
+feols(log(LN_POP)~log(LAG_LN_EMP)+lag(LN_POP)+YEAR,data=TEST )
+colnames(DATA)
 TS_DATA <- log(ts(TS_DATA_ORIG,start=c(1970),end=c(2024),frequency=1))
-plot(TS_DATA)
-?bv_minnesota
-MOD <- bvar(TS_DATA,lags=2, n_draw=15000)
+tsplot(TS_DATA)
+library(fixest)
+TEMP <- feols(log(LN_POP) ~ lag(LN_POP)+log(US_EMP)+lag(log(US_EMP))+log(US_POP),data=DATA)
+TEM
+
+plot(TEMP$residuals)
+MOD <- bvar(TS_DATA,exogen="sdfs",sdflkj=5,lags=2, n_draw=15000)
+plot(predict(MOD,horizon=25,value="LN_POP"),area=TRUE)
+forecast(MOD,variables=c("LN_POP"),horizon=25)
+?predict.bvar
+?bv_mh
 summary(MOD)
 plot(MOD)
 plot(fitted(MOD,type="mean"))
 plot(residuals(MOD,type="mean"),vars=c("LN_POP","UINTA_POP","SWEETWATER_POP"))
 plot(MOD, type = "dens", vars_response = "LN_POP", vars_impulse = "LN_POP-lag1")
 opt_irf <- bv_irf(horizon = 25, identification = TRUE)
-plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_response = c("LN_POP"),vars_impulse = c("UINTA_POP","SWEETWATER_POP","WY_POP"))
-plot(predict(MOD,horizon=25,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars=c("LN_POP","UINTA_POP"))
+
+plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_EMP"),vars_response = c("WY_POP","LN_POP","UINTA_POP","SUBLETTE_POP","SWEETWATER_POP"))
+plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_EMP"),vars_response = c("TETON_POP","BEAR_LAKE_POP","CARIBOU_POP","BONNEVILLE_POP"))
+
+plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_POP"),vars_response = c("WY_POP","LN_POP","UINTA_POP","SUBLETTE_POP","SWEETWATER_POP"))
+plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_POP"),vars_response = c("TETON_POP","BEAR_LAKE_POP","CARIBOU_POP","BONNEVILLE_POP"))
+
+DATA2 <- RES %>% mutate(US_POP=US_POP-WY_POP,WY_POP=WY_POP-LN_POP-UINTA_POP-SUBLETTE_POP-SWEETWATER_POP-TETON_POP)
+TS_DATA2 <- DATA2 %>% select(YEAR,LN_POP,US_POP,WY_POP,UINTA_POP,SUBLETTE_POP,SWEETWATER_POP,TETON_POP,BEAR_LAKE_POP,CARIBOU_POP,BONNEVILLE_POP) %>%
+  dplyr::filter(!is.na(LN_POP)) %>%
+  arrange(YEAR) %>% select(-YEAR) %>% ts %>% log
+MOD2 <- bvar(TS_DATA2,lags=5, n_draw=15000)
+plot(irf(MOD2,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_response = c("LN_POP"))
+?plot.bvar_irf
+plot(predict(MOD,horizon=25,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars=c("LN_POP"))
 exp(3.5)-exp(3)
 acf(resid(MOD))
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