OPEC Games_ENVS_Fall 2023

library(psych)  
library(sjPlot)

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library(lmSupport)
library(car)

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library(tidyverse)

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library(psych)
library(car)
library(rmarkdown)
library(ggplot2)
library(tibble)
library(ggplot2)
library(Hmisc)

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library(apaTables)
library(nlme)

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library(lmtest)

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library(psych)
library(lme4)

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library(dplyr)
library(ggplot2)
library(latexpdf)

Dataset

#Data File
O <- read.csv("OPECdata.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))

#Sample Size: Number of participants (rows)
nrow(O)

## [1] 20

#Print names of columns
names(O)

## [1] "Price"                      "World_Production"          
## [3] "ROW_Production"             "Total_Reserves_Kuwait"     
## [5] "Production_Capacity_Kuwait" "Marginal_Cost_Kuwait"

Predictions

Oct 23 2023 Kuwait

# Create a data frame with the provided country data
country_data <- data.frame(
  Country = c("Saudi Arabia", "Iran", "Iraq", "Kuwait", "UAE", "Venezuela", "Nigeria"),
  Total_Reserves = c(130000, 45000, 50000, 35000, 35000, 45000, 27500),
  Production_Capacity = c(12000, 4600, 3700, 3300, 3000, 4400, 2700),
  Marginal_Cost = c(6, 7, 8, 6, 5, 8, 8)
)

# Calculate the maximum possible production based on available reserves
country_data$Maximum_Production <- pmin(country_data$Production_Capacity, country_data$Total_Reserves)

# Calculate the total maximum production for OPEC countries
total_maximum_OPEC_production <- sum(country_data$Maximum_Production)

# Create a data frame with World Production (constrained by maximum possible production) and Price
data_with_reserves <- data.frame(
  World_Production = total_maximum_OPEC_production,  # Total maximum production is the sum of OPEC countries
  Price = c(85.05, 85.52, 81.52, 78.84, 78.86, 74.96, 72.55, 71.40, 70.58, 64.42, 66.66, 62.79, 60.68, 54.04, 57.26, 57.84, 48.88, 54.29, 50.18, 45.66)
)

# Fit a linear regression model to predict production constrained by reserves
model_with_reserves <- lm(World_Production ~ Price, data = data_with_reserves)

# Kuwait's specific data
kuwait_price <- 66.09  # The average price is used for Kuwait

# Calculate the maximum possible production for Kuwait based on available reserves
kuwait_maximum_production <- pmin(3300, 35000)  # Production Capacity for Kuwait is 3300, Total Reserves are 35000

# Create a data frame with Kuwait's data (constrained by reserves)
kuwait_data <- data.frame(
  Price = kuwait_price,
  Maximum_Production = kuwait_maximum_production
)

# Predict the quantity for Kuwait considering available reserves and capacity
predicted_quantity_kuwait_with_reserves <- predict(model_with_reserves, newdata = kuwait_data)

# Print the predicted quantity for Kuwait (constrained by reserves and capacity)
print(predicted_quantity_kuwait_with_reserves)

##     1 
## 33700

# Interpretation

## The predicted oil production for Kuwait, considering both production capacity and available reserves, is estimated to be 33,700, or 33.7 thousand barrels per period.