library(tidyverse)
library(fixest)
source("Scripts/Functions.r")
#source("Scripts/Load_Wyoming_Web_Data.r")
DATA_TO_GATHER <- list()
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c("WYPOP","WY_POP",TRUE)

DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c("WYNQGSP","WY_GDP",TRUE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c("MEHOINUSWYA646N","WY_MED_INCOME",TRUE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c("BUSAPPWNSAWY","WY_BUISNESS_APPLICATIONS",FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('ACTLISCOUWY','WY_HOUSES_FOR_SALE',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('WYRVAC','WY_RENTAL_VACANCY_RATE',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('WYBPPRIVSA','WY_PRIVATE_HOUSING',FALSE)
#New Private Housing Units Authorized by Building Permits for Wyoming
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('B03002006E056023','LN_FIVE_YEAR_POP',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('GDPALL56023','LN_GDP',TRUE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYLINC3POP','LN_POP',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('LAUCN560230000000005','LN_EMPLOYMENT',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('BPPRIV056023','LN_PRIVE_HOUSING',FALSE)

DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('ENU5602320510','LN_NUM_ESTABLISHMENTS',FALSE)

DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('GDPALL56041','UINTA_GDP',TRUE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYUINT1POP','UINTA_POP',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYSUBL5POP','SUBLETTE_POP',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYSWEE7POP','SWEETWATER_POP',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYTETO9POP','TETON_POP',FALSE)
##Idaho Counties
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDBEAR7POP','BEAR_LAKE_POP',FALSE)
DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDCARI9POP','CARIBOU_POP',FALSE)

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)




for(x in 1:length(DATA_TO_GATHER)){
	CURRENT <- DATA_TO_GATHER[[x]]

	if(CURRENT[3]){C_DATA <- CPI_ADJUST(FRED_GET(CURRENT[1],CURRENT[2]))}else{C_DATA <- FRED_GET(CURRENT[1],CURRENT[2])}
	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)
feols(log(LN_POP) ~log(US_POP)+log(UINTA_POP)+log(WY_POP)+log(SUBLETTE_POP)+log(SWEETWATER_POP)+log(TETON_POP)+log(BEAR_LAKE_POP)+log(CARIBOU_POP)+log(BONNEVILLE_POP)+YEAR,data=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 <- diff(log(ts(TS_DATA_ORIG,start=c(1970),end=c(2024),frequency=1)))
library("forecast")
library("vars")
VARselect(TS_DATA,lag.max=4,type="const")
VAR1 <- VAR(TS_DATA,p=3,type="const",season=NULL,exog=NULL)
plot(irf(VAR1,response="LN_POP"))
plot(forecast(VAR1,))
RES <- (predict(VAR1, n.ahead = 20, ci = 0.95))
names(RES )
names(RES$fcst)
RES$fcst$LN_POP %>% as_tibble
CURRENT_POP <- max(DATA$LN_POP,na.rm=TRUE)
0.0157*CURRENT_POP*1000
0.083*CURRENT_POP*1000
-0.0489*CURRENT_POP*1000




# View the forecasted values and confidence intervals
print(forecast_results)

# You can also plot the forecasts
plot(forecast_results)


install.packages("sparsegl")
plot(VAR1)



#Check a VAR it looks like lags on changes to Private industry could affect other variables
		#Idea check a SVAR placing limits on which shocks are first
feols((LINC_POP)~(WY_POP)+log(LINC_PRIV_IND)+Year,data=DF)
feols(log(1000*LINC_POP)~log(LINC_GDP)+log(LINC_PRIV_IND)+log(LINC_LABOR_FORCE)+log(LINC_PRIV_IND)+Year,DF)
RES
ggplot(data=RES) +geom_point(aes(x=YEAR,y=WY_POP),color="red")+geom_point(aes(x=YEAR,y=30*LN_POP),color="blue")+geom_point(aes(x=YEAR,y=LN_GDP/1700),color="black")+geom_point(aes(x=YEAR,y=LN_LABOR_FORCE/20),color="orange")
itial information and data that will be required from Kemmerer-Diamondville Water &
Wastewater Joint Powers Board includes names of key stakeholders that can be interviewed
regarding future developments, new businesses, and business closures.
version
install.packages("pbkrtest")
install.packages("bayesPop")
library("bayesMig")
help("bayesPop")
library(bayesMig)
?mig.predict
example(mig.predict)
mig.predict(51)
library("bayesPop")
example(bayesMig)
?bayesMig