# Load necessary libraries
library(stats)
library(dplyr) #create lagged data

# Generate some example data (replace this with your actual data)
# Let's assume 'data' is a data frame with columns: past_inflation, unemployment_gap, output_gap, and target_inflation
# You should replace this with your actual data

# Create a data frame with example data

output_gap <- read.csv("C:/Work/DMU Teaching/ECON 5001/Block teaching/Block 6/UK_annual_output_gap.csv", sep = ",", header = TRUE)
CPI_data <- read.csv("C:/Work/DMU Teaching/ECON 5001/Block teaching/Block 6/UK_annual_inflation.csv", sep = ",", header = TRUE)

CPI <- CPI_data$CPI.INDEX.00..ALL.ITEMS.2015.100
CPI_L1 <- dplyr::lag(CPI, n=1)

inflation <- (CPI-CPI_L1)/CPI_L1*100
inflation_L1 <- dplyr::lag(inflation, n=1)


#remove the first two years as there are no lags
inflation <- inflation[3:27]
inflation_L1 <- inflation_L1[3:27]
output_gap <- output_gap[3:27,4]

data <- data.frame(y=inflation, x1=inflation_L1, x2=output_gap)

                         
# Fit a linear regression model
model <- lm(y ~ x1 + x2, data = data)

# Summary of the regression model
summary(model)

# Predict inflation in 2023 using the model
new_data <- data.frame(
  x1 = c(inflation[25]),
  x2 = c(-2.4) #you can put any current inflation number in this  
)

inflation_2023 <- predict(model, newdata = new_data)
print(inflation_2023)

# Predict inflation in 2024 using the model
new_data <- data.frame(
  x1 = c(inflation_2023),  
  x2 = c(-2.6)  #you can put any current inflation number in this  
)

inflation_2024 <- predict(model, newdata = new_data)

# Print the predicted inflation
print(inflation_2024)