## -----------------------------------------------------------------------------
library(MultiATSM)

## ----ModFea, message=FALSE, echo=FALSE----------------------------------------
ModelLabels <- c("JPS original", "JPS global", "JPS multi", "GVAR single", "GVAR multi", 
                 "JLL original", "JLL No DomUnit", "JLL joint Sigma")

# Rows
Tab <- data.frame(matrix(nrow = length(ModelLabels), ncol = 0)) 
rownames(Tab) <- ModelLabels

# Empty columns
EmptyCol <- c("", "", "", "", "", "", "", "") 
Tab$EmptyCol0 <- EmptyCol
# P-dynamics + 2 empty spaces
Tab$PdynIndUnco <- c("x", "", "", "", "", "", "", "")
Tab$PdynIndCo <- c("", "", "", "", "", "", "", "")
Tab$PdynJointUnco <- c("", "x", "x", "", "", "", "", "")
Tab$PdynJointJLL <- c("", "", "", "", "", "x", "x", "x")
Tab$PdynJointGVAR <- c("", "", "", "x", "x", "", "", "")
Tab$EmptyCol1 <- EmptyCol
Tab$EmptyCol2 <- EmptyCol
# Q-dynamics + 2 empty spaces
Tab$QdynInd <- c("x", "x", "", "x", "", "", "", "")   
Tab$QdynJoint <- c("", "", "x", "", "x", "x", "x", "x") 
Tab$EmptyCol3 <- EmptyCol
Tab$EmptyCol4 <- EmptyCol
# Sigma + 2 empty spaces
Tab$Ponly <-  c("", "", "", "", "", "x", "x", "")
Tab$PandQ <- c("x", "x", "x", "x", "x", "", "", "x")
Tab$EmptyCol5 <- EmptyCol
Tab$EmptyCol6 <- EmptyCol
# Dominant Unit
Tab$DomUnit <- c("", "", "", "", "", "x", "", "x")

# Adjust column names
ColNames <- c("","","","","JLL", "GVAR", "", "", "", "", "", "", "","", "", "","")
colnames(Tab) <- ColNames


# Generate the table
suppressWarnings(library(magrittr))


kableExtra::kbl(Tab, align = "c", caption = "Model Features") %>%
  kableExtra::kable_classic("striped", full_width = F)  %>%
  kableExtra::row_spec(0, font_size = 10) %>%
  kableExtra::add_header_above(c(" "=2, "Unrestricted" = 1, "Restricted" = 1, "Unrestricted" = 1, "Restricted" = 2, " " = 11)) %>%
 kableExtra::add_header_above(c(" "=2, "Individual" = 2, "Joint" = 3, " "=2, "Individual" = 1, "Joint" = 1, " "=2, "P only" = 1, "P and Q" = 1, " " = 3))  %>%
  kableExtra::add_header_above(c( " "=2, "P-dynamics"= 5, " "=2, "Q-dynamics"= 2, " "=2, "Sigma matrix estimation" = 2, " "=2, "Dominant Country"=1), bold = T) %>%
kableExtra::pack_rows("Unrestricted VAR", 1, 3 , label_row_css = "background-color: #666; color: #fff;")  %>%
kableExtra::pack_rows("Restricted VAR (GVAR)", 4, 5, label_row_css = "background-color: #666; color: #fff;") %>%
kableExtra::pack_rows("Restricted VAR (JLL)", 6, 8, label_row_css = "background-color: #666; color: #fff;")

## -----------------------------------------------------------------------------
LoadData("CM_2024")

## -----------------------------------------------------------------------------
data('CM_Yields')

## -----------------------------------------------------------------------------
data('CM_Factors')

## -----------------------------------------------------------------------------
data('CM_Trade')

## -----------------------------------------------------------------------------
data('CM_Factors_GVAR')

## -----------------------------------------------------------------------------
Initial_Date <- "2006-09-01" # Format "yyyy-mm-dd"
Final_Date <- "2019-01-01" # Format "yyyy-mm-dd"
DataFrequency <- "Monthly"
GlobalVar <- c("GBC", "VIX") # Global Variables
DomVar <- c("Eco_Act", "Inflation", "Com_Prices", "Exc_Rates") #  Domestic variables
N <-  3 # Number of spanned factors per country
Economies <- c("China", "Mexico", "Uruguay", "Brazil", "Russia")
ModelType <- "JPS original"

## -----------------------------------------------------------------------------
FactorLabels <- LabFac(N, DomVar, GlobalVar, Economies, ModelType)
RiskFac_TS <- DataForEstimation(Initial_Date, Final_Date, Economies, N, FactorLabels, ModelType,
                                DataFrequency)

## -----------------------------------------------------------------------------
# 1) Model type
ModelType <- "JPS original"

# 2) Risk factor set
Economies <- c("China", "Brazil", "Mexico", "Uruguay")
GlobalVar <- c("GBC", "CPI_OECD") # Global variables
DomVar <- c("Eco_Act", "Inflation") # Domestic variables 
N <- 3 # number of spanned factors per country

# 3) Sample span
Initial_Date <- "01-05-2005" # Format: "dd-mm-yyyy"
Final_Date <- "01-12-2019" # Format: "dd-mm-yyyy"

# 4) Frequency of the data
DataFrequency <- "Monthly"

# 5) Risk-neutral stationary constraint
StationarityUnderQ <- 0 # 1 = set stationary condition under the Q; 0 = no stationary condition

# 6) Output label
OutputLabel <- "Model_demo"

## -----------------------------------------------------------------------------
VARXtype <- "unconstrained"

## -----------------------------------------------------------------------------
W_type <- 'Sample Mean' # Method to compute the transition matrix
t_First_Wgvar <- "2000" # First year of the sample
t_Last_Wgvar <-  "2015" # Last year of the sample

## -----------------------------------------------------------------------------
GVARlist <- list( VARXtype = "unconstrained", W_type = "Sample Mean",
                  t_First_Wgvar = "2000", t_Last_Wgvar = "2015") 

## -----------------------------------------------------------------------------
JLLlist <- list(DomUnit =  "China")

## -----------------------------------------------------------------------------
BRWlist <- within(list(flag_mean = TRUE, gamma = 0.2, N_iter = 500, B = 50, 
                       checkBRW = TRUE, B_check = 1000), N_burn <- round(N_iter * 0.15))

## ----eval=FALSE---------------------------------------------------------------
#  data(CM_GlobalMacroFactors)
#  data(CM_DomMacroFactors)
#  data(CM_Yields)
#  
#  ModelType <- "JPS original"
#  Economies <- "Mexico"
#  t0 <- "01-05-2007" # Initial Sample Date (Format: "dd-mm-yyyy")
#  tF <- "01-12-2018" # Final Sample Date (Format: "dd-mm-yyyy")
#  N <- 3
#  GlobalVar <- c("Gl_Eco_Act") # Global Variables
#  DomVar <- c("Eco_Act") # Domestic Variables
#  FactorLabels <- LabFac(N, DomVar, GlobalVar, Economies, ModelType)
#  
#  DataFreq <- "Monthly"
#  
#  ATSMInputs <- InputsForOpt(t0, tF, ModelType, Yields, GlobalMacroVar, DomesticMacroVar,
#                           FactorLabels, Economies, DataFreq,CheckInputs = FALSE)

## -----------------------------------------------------------------------------
Horiz <- 100

## -----------------------------------------------------------------------------
DesiredGraphs <- c("Fit", "GIRF", "GFEVD") # Available options are: "Fit", "IRF", "FEVD", "GIRF", 
                                          # "GFEVD", "TermPremia".

## -----------------------------------------------------------------------------
WishGraphRiskFac <- 0 #   YES: 1; No = 0.
WishGraphYields <- 1 #    YES: 1; No = 0.
WishOrthoJLLgraphs <- 0 # YES: 1; No = 0.

## -----------------------------------------------------------------------------
    WishFPremia <- 1 # Wish to estimate the forward premia: YES: 1, NO:0 
    FPmatLim <- c(60,120)

## -----------------------------------------------------------------------------
Bootlist <- list(methodBS = 'block', BlockLength = 4, ndraws =  50, pctg   =  95)

## -----------------------------------------------------------------------------
ForecastList <- list(ForHoriz = 12, t0Sample = 1, t0Forecast = 70, ForType = "Rolling")

## -----------------------------------------------------------------------------
w <- pca_weights_one_country(Yields, Economy = "Uruguay") 

## ----fig.cap = "Yield loadings on the spanned factors", echo=FALSE------------
LabSpaFac <- c("Level", "Slope", "Curvature")
N <- length(LabSpaFac)
 
w_pca <- data.frame(t(w[1:N,]))
colnames(w_pca) <- LabSpaFac
w_pca$mat <- c(0.25, 0.5, 1, 3, 5, 10) # vector of maturitie

# Prepare plots
colors <- c("Level" = "blue", "Slope" = "green", "Curvature" = "red")

g <-  ggplot2::ggplot(data = w_pca, ggplot2::aes(x=  mat)) +  
    ggplot2::geom_line(ggplot2::aes(y = Level, color = "Level"), linewidth = 0.7) + 
    ggplot2::geom_line(ggplot2::aes(y = Slope, color = "Slope"), linewidth = 0.7) +
    ggplot2::geom_line(ggplot2::aes(y = Curvature, color = "Curvature"),  linewidth = 0.7) +
      ggplot2::labs(color = "Legend") + ggplot2::scale_color_manual(values = colors) + ggplot2::theme_classic() +
    ggplot2::theme(axis.title.y= ggplot2::element_blank(), legend.position="top", legend.title=ggplot2::element_blank(), legend.text= ggplot2::element_text(size=8) ) + 
   ggplot2::xlab("Maturity (Years)") + ggplot2::geom_hline(yintercept=0)

print(g)

## -----------------------------------------------------------------------------
data('CM_Yields')
Economies <- c("China", "Brazil", "Mexico", "Uruguay")
N <- 3
SpaFact <- Spanned_Factors(Yields, Economies, N)

## -----------------------------------------------------------------------------
data("CM_Factors")
PdynPara <- VAR(RiskFactors, VARtype= "unconstrained")

## -----------------------------------------------------------------------------
FactorsChina <- RiskFactors[1:7,]
PdynPara <- VAR(FactorsChina, VARtype= "unconstrained")

## -----------------------------------------------------------------------------
GVARinputs <- list(Economies = Economies, GVARFactors = FactorsGVAR, VARXtype ="constrained: Inflation")

## -----------------------------------------------------------------------------
data("CM_Trade")
t_First <- "2006"
t_Last <-  "2019"
Economies <- c("China", "Brazil", "Mexico", "Uruguay")
type <- "Sample Mean"
W_gvar <- Transition_Matrix(t_First, t_Last, Economies, type, DataPath = NULL, TradeFlows)
print(W_gvar)

## -----------------------------------------------------------------------------
data("CM_Factors_GVAR")

GVARinputs <- list(Economies = Economies, GVARFactors = FactorsGVAR, VARXtype = "unconstrained", 
                   Wgvar = W_gvar)
N <- 3

GVARpara <- GVAR(GVARinputs, N, CheckInputs = TRUE)

## -----------------------------------------------------------------------------
data('CM_Factors')
StaFac <- StarFactors(RiskFactors, Economies, W_gvar)

## -----------------------------------------------------------------------------
ModelType <- "JLL original"   
JLLinputs <- list(Economies = Economies, DomUnit = "China", WishSigmas = 1,  SigmaNonOrtho = NULL,
                  JLLModelType = ModelType)

## ----eval=FALSE---------------------------------------------------------------
#  data("CM_Factors")
#  N <- 3
#  JLLpara <- JLL(RiskFactors, N, JLLinputs, CheckInputs = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  ########################################################################################################
#  #################################### USER INPUTS #######################################################
#  ########################################################################################################
#  # A) Load database data
#  LoadData("CM_2024")
#  
#  # B) GENERAL model inputs
#  ModelType <- "JPS multi" # available options: "JPS original", "JPS global", "GVAR single", "JPS multi",
#                            #"GVAR multi", "JLL original", "JLL No DomUnit", "JLL joint Sigma".
#  
#  Economies <- c("China", "Brazil", "Mexico") # Names of the economies from the economic system
#  GlobalVar <- c("Gl_Eco_Act")# Global Variables
#  DomVar <- c("Eco_Act", "Inflation") # Country-specific variables
#  N <- 2  # Number of spanned factors per country
#  
#  t0_sample <- "01-05-2005" # Format: "dd-mm-yyyy"
#  tF_sample <- "01-12-2019" # Format: "dd-mm-yyyy"
#  
#  OutputLabel <- "Test" # label of the model for saving the file
#  DataFreq <-"Monthly" # Frequency of the data
#  
#  StatQ <- 0 # Wish to impose stationary condition for the eigenvalues of each country: YES: 1,NO:0
#  
#  # B.1) SPECIFIC model inputs
#  #################################### GVAR-based models ##################################################
#  GVARlist <- list( VARXtype = "unconstrained", W_type = "Sample Mean", t_First_Wgvar = "2005",
#                    t_Last_Wgvar = "2019")
#  # VARXtype: Available options "unconstrained" or "constrained" (VARX)
#  # W_type: Method to compute the transition matrix. Options:"Time-varying" or "Sample Mean"
#  # t_First_Wgvar: First year of the sample (transition matrix)
#  # t_Last_Wgvar:  Last year of the sample (transition matrix)
#  #################################### JLL-based models ###################################################
#  JLLlist <- list(DomUnit =  "China")
#  # DomUnit: name of the economy of the economic system, or "None" for the model "JLL No DomUnit"
#  ###################################### BRW inputs  ######################################################
#  WishBC <- 0 # Wish to estimate the model with the bias correction method of BRW (2012): #YES: 1, NO:0
#  BRWlist <- within(list(flag_mean = TRUE, gamma = 0.05, N_iter = 250, B = 50, checkBRW = TRUE,
#                         B_check = 1000),  N_burn <- round(N_iter * 0.15))
#  # flag_mean: TRUE = compute the mean; FALSE = compute the median
#  # gamma: Adjustment parameter
#  # N_iter:  Number of iteration to be conserved
#  # N_burn:  Number of iteration to be discarded
#  # B: Number of bootstrap samples
#  # checkBRW: wishes to perform closeness check: TRUE or FALSE
#  # B_check: If checkBRW is chosen as TRUE, then choose number of bootstrap samples used in the check
#  
#  # C) Decide on Settings for numerical outputs
#  WishFPremia <- 1 # Wish to estimate the forward premia: YES: 1, NO:0
#  FPmatLim <- c(60,120) #  If the forward premia is desired, then choose the Min and max maturities of the
#                        # forward premia. Otherwise set NA
#  Horiz <- 30
#  DesiredGraphs <- c("Fit", "IRF", "TermPremia") # "Fit", "IRF", "FEVD", "GIRF", "GFEVD", "TermPremia"
#  WishGraphRiskFac <- 0
#  WishGraphYields <- 1
#  WishOrthoJLLgraphs <- 0
#  
#  # D) Bootstrap settings
#  WishBootstrap <- 1 #  YES: 1; No = 0.
#  BootList <- list(methodBS = 'bs', BlockLength = 4, ndraws = 5, pctg =  95)
#  # methodBS: bootstrap method. Available options: (i) 'bs' ; (ii) 'wild'; (iii) 'block'
#  # BlockLength: Block length, necessary input for the block bootstrap method
#  # ndraws: number of bootstrap draws
#  # pctg: confidence level
#  
#  # E) Out-of-sample forecast
#  WishForecast <- 1 #  YES: 1; No = 0.
#  ForecastList <- list(ForHoriz = 12,  t0Sample = 1, t0Forecast = 162, ForType = "Rolling")
#  # ForHoriz: forecast horizon
#  # t0Sample:   initial sample date
#  # t0Forecast:  last sample date for the first forecast
#  # ForType: Available options are "Rolling" or "Expanding"
#  
#  #########################################################################################################
#  ############################### NO NEED TO MAKE CHANGES FROM HERE #######################################
#  #########################################################################################################
#  
#  # 2) Minor preliminary work: get the sets of factor labels and  a vector of common maturities
#  FactorLabels <- LabFac(N, DomVar, GlobalVar, Economies, ModelType)
#  if(any(ModelType == c("GVAR single", "GVAR multi"))){ GVARlist$DataConnectedness <- TradeFlows}
#  
#  # 3) Prepare the inputs of the likelihood function
#  ATSMInputs <- InputsForOpt(t0_sample, tF_sample, ModelType, Yields, GlobalMacroVar, DomesticMacroVar,
#                             FactorLabels, Economies, DataFreq, GVARlist, JLLlist, WishBC, BRWlist)
#  
#  # 4) Optimization of the ATSM (Point Estimates)
#  ModelParaList <- Optimization(ATSMInputs, StatQ, DataFreq, FactorLabels, Economies, ModelType)
#  
#  # 5) Numerical and graphical outputs
#  # a) Prepare list of inputs for graphs and numerical outputs
#  InputsForOutputs <- InputsForOutputs(ModelType, Horiz, DesiredGraphs, OutputLabel, StatQ, DataFreq,
#                                      WishGraphYields, WishGraphRiskFac, WishOrthoJLLgraphs, WishFPremia,
#                                      FPmatLim, WishBootstrap, BootList, WishForecast, ForecastList)
#  
#  # b) Fit, IRF, FEVD, GIRF, GFEVD, and Term Premia
#  NumericalOutputs <- NumOutputs(ModelType, ModelParaList, InputsForOutputs, FactorLabels, Economies)
#  
#  # c) Confidence intervals (bootstrap analysis)
#  BootstrapAnalysis <- Bootstrap(ModelType, ModelParaList, NumericalOutputs, Economies, InputsForOutputs,
#                                 FactorLabels, JLLlist, GVARlist, WishBC, BRWlist)
#  
#  # 6) Out-of-sample forecasting
#  Forecasts <- ForecastYields(ModelType, ModelParaList, InputsForOutputs, FactorLabels, Economies,
#                              JLLlist, GVARlist, WishBC, BRWlist)