136 lines
5.3 KiB
R
136 lines
5.3 KiB
R
library(tidyverse)
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library(forecast)
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library(lmtest)
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source("Scripts/Functions.r")
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#source("Scripts/Load_Wyoming_Web_Data.r")
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DF <- FRED_GET('WYLINC3POP','LN_POP') %>% select(-YEAR)
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TS <- 1000*ts(DF,start=c(1970),end=c(2024),frequency=1)
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BC <- BoxCox.lambda(TS)
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MODEL <- auto.arima(TS, lambda = BC)
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forecast(MODEL,h=20)
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ARMA_POP <- forecast(MODEL,h=35,fan=TRUE,bootstrap = TRUE,npaths=100000,biasadj=FALSE)
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ARMA_POP
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plot(ARMA_POP,main="Lincoln County Population Forecast",xlab="Year",ylab="Population")
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help("forecast")
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####Employment to pop ratio
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EMP <- FRED_GET('LAUCN560230000000005','EMP') %>% inner_join(FRED_GET('WYLINC3POP','LN_POP')) %>% mutate(LN_POP=1000*LN_POP)
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2900/3300
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EMP <- EMP %>% mutate(RATIO=LN_POP/EMP)
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ggplot(aes(x=YEAR,y=RATIO),data=EMP)+geom_line()
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AVG_POP_RATIO <- mean(EMP$RATIO)
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SD_POP_RATIO <- sd(EMP$RATIO)
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#####City level
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#See data http://eadiv.state.wy.us/pop/
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KEM <- c(3273,3523,3688,3667,3626,3637,3611,3388,3156,3040,3020,3029,2989,2959,2976,2963,2910,2807,2729,2690,2657,2608,2575,2561,2574,2579,2603,2640,2679,2692,2642,2597,2551,2575,2578,2554,2544,2499,2457,2435,2413,2445,2445,2404,2378)
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DIAMOND <- c(1000,1070,1114,1101,1082,1078,1063,991,916,876,864,863,847,835,835,827,808,774,748,732,705,695,690,689,695,700,710,723,738,745,731,704,677,667,652,629,613,586,559,540,523,526,527,521,517)
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AREA_POP <- KEM+DIAMOND
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LN <- c(12177,13254,14031,14110,14111,14319,14384,13658,12875,12552,12625,12975,13124,13329,13759,14073,14206,14099,14114,14338,14621,14697,14858,15117,15539,15917,16429,17013,17629,18082,18083,17946,17822,18148,18346,18473,18766,18899,19042,19379,19658,20174,20690,20909,21000)
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NO_CITY <- c(10095,10392,10747,10944,11043)
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YEAR <- 1980:2024
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DATA <- cbind(YEAR,LN,AREA_POP) %>% as_tibble %>% rename("Lincoln County"=LN,"Kemmerer & Diamondvile"=AREA_POP)
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#Old daata:Period Ends in 1970
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#See in part http://eadiv.state.wy.us/demog_data/cntycity_hist.htm
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LN_OLD <- c(12487,10894,10286,9023,9018,8640)
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LN_OLD <- cbind(seq(1920,1970,by=10),LN_OLD) %>% as_tibble
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colnames(LN_OLD) <- c("YEAR","LN_POP")
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KEM_OLD <- c(843,1517,1884,2026,1667,2028,2292)
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KEM_OLD <- cbind(seq(1910,1970,by=10),KEM_OLD) %>% as_tibble
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colnames(KEM_OLD) <- c("YEAR","KEM_POP")
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DIAMOND_OLD <- c(696,726,812,586,415,398,485)
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DIAMOND_OLD <- cbind(seq(1910,1970,by=10),DIAMOND_OLD) %>% as_tibble
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colnames(DIAMOND_OLD) <- c("YEAR","DIA_POP")
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OLD_DATA <- inner_join(KEM_OLD,DIAMOND_OLD) %>% full_join(LN_OLD)
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GRAPH_DATA <- ts(DATA %>% select(-YEAR),start=c(1980),end=c(2024),frequency=1)
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png("Population.png")
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plot(GRAPH_DATA ,main="Regional Population Trends",type="b",lwd=4,col="blue")
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dev.off()
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lines(GRAPH_DATA,col="blue")
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?plot
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##############
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TS <- EMP %>% select(EMP) %>% ts(start=c(1990),end=c(2024),frequency=1)
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BC <- BoxCox.lambda(TS)
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MODEL2 <- auto.arima(TS, lambda = BC)
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MODEL2
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ARMA_EMP <- forecast(MODEL2,h=35,fan=TRUE,bootstrap = TRUE,npaths=100000,biasadj=FALSE)
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plot(ARMA_EMP,main="Lincoln County Employment",xlab="Year",ylab="Employed")
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##########
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DATA <- FRED_GET('BPPRIV056023','PRIV_HOUSING') %>% inner_join(FRED_GET('WYLINC3POP','LN_POP')) %>% inner_join(FRED_GET('ATNHPIUS56023A','HOUSE_PRICE_INDEX')) %>% left_join(FRED_GET('LAUCN560230000000005','EMP')) %>%left_join(FRED_GET('DCOILWTICO','WTI')) %>%left_join(FRED_GET('PCOALAUUSDM','COAL')) %>% mutate(LN_POP=1000*LN_POP) %>% select(-YEAR) %>% ts() %>% log() %>% diff()
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grangertest(PRIV_HOUSING~HOUSE_PRICE_INDEX,data=DATA,order=1)
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grangertest(HOUSE_PRICE_INDEX~PRIV_HOUSING,data=DATA,order=2)
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grangertest(LN_POP~PRIV_HOUSING,data=DATA,order=1)
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grangertest(HOUSE_PRICE_INDEX~COAL,data=DATA,order=2)
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grangertest(LN_POP~WTI,data=DATA,order=1)
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grangertest(COAL~WTI,data=DATA,order=2)
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grangertest(WTI~COAL,data=DATA,order=2)
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grangertest(LN_POP~COAL,data=DATA,order=1)
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grangertest(EMP~LN_POP,data=DATA,order=2)
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grangertest(PRIV_HOUSING~LN_POP,data=DATA,order=2) ## Signficant
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grangertest(EMP~LN_POP,data=DATA,order=2) ## Signficant
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grangertest(EMP~PRIV_HOUSING,data=DATA,order=2) ##Signficant
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grangertest(EMP~LN_POP,data=DATA,order=2) ##Signficant
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grangertest(PRIV_HOUSING~COAL,data=DATA,order=1) ##Signficant
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grangertest(PRIV_HOUSING~COAL,data=DATA,order=2) ##Signficant
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grangertest(PRIV_HOUSING~COAL,data=DATA,order=3) ##Signficant
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grangertest(EMP~COAL,data=DATA,order=1) ##Signficant
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grangertest(LN_POP~EMP,data=DATA,order=2)
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grangertest(PRIV_HOUSING~EMP,data=DATA,order=2)
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plot(DATA)
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library(BVAR)
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MOD <- bvar(DATA,lags=1, n_draw=40000)
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opt_irf <- bv_irf(horizon = 25, identification = TRUE)
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plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE)
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head(DATA)
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DATA
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# ATNHPIUS56023A Housing price index
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# Median income MHIWY56023A052NCEN
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# Employed Persons in Lincoln LAUCN560230000000005
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#####Plan and ideas
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#1) Review IMPLAN for industry multipliers
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#2) Review IMPLAN for employment to population multipliers (imparted)
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#3) Find a list of all planned new projects
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#4) Use the IMPLAN multipliers for each sector to estimate total change
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#5) Develop survey to estimate likelihood of new projects
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#6) Compare to the ARMA percentile
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#7) Adjust the ARMA up assuming some of these outputs are known.
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#8) Occupancy rate from IMPLAN as a housing cap when projecting
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#9) Model housing construciton rate (Maybe)
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#10) Employment rate by age in IMPLAN
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####Other ideas, develop larger plan? Maybe look at decline in other industries as a proportion of employment
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###Seperate out Kemmer and Diamondville? http://eadiv.state.wy.us/pop/wyc&sc40.htm
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