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Updated model

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Alex Gebben Work 2026-03-09 17:04:08 -06:00
parent 6187a3bffa
commit c835e3accf
1 changed files with 19 additions and 12 deletions
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31
Scale.r
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@ -30,7 +30,6 @@ DATE <- c(2017,2018,2018,2022,2015,2020,2024,2025,2025,2012,2024,2025,2024,2024,
DATA <- cbind(CAP,EMP,PEAK_CONST,DETAILED_REPORT,DATE) %>% as.matrix %>% as_tibble DATA <- cbind(CAP,EMP,PEAK_CONST,DETAILED_REPORT,DATE) %>% as.matrix %>% as_tibble
DATA$NAMES <- NAMES DATA$NAMES <- NAMES
DATA DATA
colnames(DATA ) <- c("Capacity","Emp","Peak_Const","Report_Quality","Report_Year","Plant") colnames(DATA ) <- c("Capacity","Emp","Peak_Const","Report_Quality","Report_Year","Plant")
REG_DATA <-DATA %>% select(Emp,Capacity,Report_Quality,Report_Year) REG_DATA <-DATA %>% select(Emp,Capacity,Report_Quality,Report_Year)
REG_DATA_TRIMMED <-DATA %>% filter(Plant!='Homer') %>% select(Emp,Capacity,Report_Quality,Report_Year) REG_DATA_TRIMMED <-DATA %>% filter(Plant!='Homer') %>% select(Emp,Capacity,Report_Quality,Report_Year)
@ -44,25 +43,33 @@ PRED_DATA$Plant <- 'Enyo'
PRED_DATA$Emp<- NA PRED_DATA$Emp<- NA
round(exp(predict(MOD1,newdata=PRED_DATA))) round(exp(predict(MOD1,newdata=PRED_DATA)))
#PRED_DATA$Capacity<- c(640,1280,1280,3680) #Larger number includes the single cyle turbines that look to be turned off most of the time #PRED_DATA$Capacity<- c(640,1280,1280,3680) #Larger number includes the single cyle turbines that look to be turned off most of the time
round(exp(predict(MOD1,newdata=PRED_DATA))) PRED_OPERATING <- round(exp(predict(MOD1,newdata=PRED_DATA)))
PRED_OPERATING
ggplot(REG_DATA,aes(x=Capacity,y=log(Emp)))+geom_point()+geom_smooth(method='lm') ggplot(REG_DATA,aes(x=Capacity,y=log(Emp)))+geom_point()+geom_smooth(method='lm')
#feols(log(Emp)~Capacity+Report_Quality+Report_Year,REG_DATA_TRIMMED,vcov='hetero') #feols(log(Emp)~Capacity+Report_Quality+Report_Year,REG_DATA_TRIMMED,vcov='hetero')
feols(Peak_Const~Capacity+Report_Year ,DATA,vcov='hetero')
DATA
MOD2 <- feols(Peak_Const~Capacity+Report_Year+Report_Quality,DATA,vcov='hetero')
feols(Peak_Const~Capacity+Report_Year+Report_Quality,DATA,vcov='hetero') feols(Peak_Const~Capacity+Report_Year+Report_Quality,DATA,vcov='hetero')
ggplot(DATA,aes(x=Capacity,y=Peak_Const))+geom_point()+geom_smooth(method='lm')
###Construction Data
CONST_SCHEDULE <- c(45,75,90,115,175,235,285,350,400,350,255,175,95,65)
PEAK_RATIO <- (sum(CONST_SCHEDULE )/12)/400
PEAK_RATIO
PRED_DATA_CONST <- PRED_DATA
PRED_DATA_CONST$Capacity <- c(640,640,0,2400)
CONST_EMPLOYMENT_PRED <- round(predict(MOD2,PRED_DATA_CONST)*PEAK_RATIO)
CONST_EMPLOYMENT_PRED
MOD2
CONST_SCHEDULE
95*2
MOD1 <- feols(log(Emp)~Capacity,REG_DATA,vcov='hetero')
MOD1
PRED_DATA <- DATA[1:4,] PRED_DATA <- DATA[1:4,]
PRED_DATA
b