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

## ----eval=FALSE---------------------------------------------------------------
#  # A) Load database data
#  LoadData("BR_2017")
#  
#  # B) GENERAL model inputs
#  ModelType <- "JPS original"
#  
#  Economies <- c("U.S.") # Names of the economies from the economic system
#  GlobalVar <- c() # Global Variables
#  DomVar <- c("GRO", "INF") # Country-specific variables
#  N <- 3 # Number of spanned factors per country
#  
#  t0_sample <- "January-1985"
#  tF_sample <- "December-2007"
#  
#  DataFreq <- "Monthly" # Frequency of the data
#  
#  StatQ <- 0 # Stationary condition
#  
#  #########################################################################################################
#  ############################### NO NEED TO MAKE CHANGES FROM HERE #######################################
#  #########################################################################################################
#  # 2) Minor preliminary work
#  FactorLabels <- LabFac(N, DomVar, GlobalVar, Economies, ModelType)
#  
#  Yields <- t(BR_jps_out$Y)
#  DomesticMacroVar <- t(BR_jps_out$M.o)
#  GlobalMacroVar <- c()
#  
#  # 3) Prepare the inputs of the likelihood function
#  ATSMInputs <- InputsForOpt(t0_sample, tF_sample, ModelType, Yields, GlobalMacroVar, DomesticMacroVar,
#                             FactorLabels, Economies, DataFreq)
#  
#  # 4) Optimization of the model
#  ModelPara <- Optimization(ATSMInputs, StatQ, DataFreq, FactorLabels, Economies, ModelType)

## ----echo= FALSE--------------------------------------------------------------
options(scipen = 100) # eliminate the scientific notation
data("BR_jps_gro_R3")
data("JPSrep")

RowsQ <- c("$r0$", "$\\lambda_1$", "$\\lambda_2$", "$\\lambda_3$" )
TableQ <- data.frame(matrix(NA, ncol = 0, nrow =length(RowsQ)))
row.names(TableQ) <- RowsQ

PackageQ<- c(ModelPara$ests$r0, diag(ModelPara$ests$K1XQ))
BRq <- c(BR_jps_out$est.llk$rho0.cP, diag(BR_jps_out$est.llk$KQ.XX))
TableQ$MultiATSM <- PackageQ
TableQ$'BR (2017)' <- BRq

TableQ <- round(TableQ, digits = 5)

suppressWarnings(library(magrittr))

kableExtra::kbl(TableQ, align = "c", caption = "$Q$-dynamics parameters") %>%
  kableExtra::kable_classic("striped", full_width = F)  %>%
  kableExtra::row_spec(0, font_size = 14) %>%
  kableExtra::footnote(general = " $\\lambda$'s are the eigenvalues from the risk-neutral feedback matrix and $r0$ is the long-run mean of the short rate under Q.")

## ----PdynTab, echo= FALSE-----------------------------------------------------
data("BR_jps_gro_R3")
data("JPSrep")

RowsP <- c("PC1", "PC2", "PC3", "GRO", "INF")
ColP <- c(" ", RowsP)

# 1) K0Z and K1Z
# Bauer and Rudebusch coefficients
TablePbr <- data.frame(matrix(NA, ncol = length(ColP), nrow =length(RowsP)))
row.names(TablePbr) <- RowsP
colnames(TablePbr) <- ColP

TablePbr[[ColP[1]]] <- BR_jps_out$est.llk$KP.0Z
for(j in 1:length(RowsP) ){TablePbr[[RowsP[j]]] <- BR_jps_out$est.llk$KP.ZZ[,j]}

TablePbr <- round(TablePbr, digits = 5)

# MultiATSM coefficients
TablePMultiATSM <- data.frame(matrix(NA, ncol = length(ColP), nrow =length(RowsP)))
row.names(TablePMultiATSM) <- RowsP
colnames(TablePMultiATSM) <- ColP

IdxVar <- c(3:5, 1:2) # indexes to flip the order of the spanned and unspanned factors
TablePMultiATSM[[ColP[1]]] <- ModelPara$ests$K0Z[IdxVar]
ModelPara$ests$K1Z <- ModelPara$ests$K1Z[IdxVar,IdxVar]
for(j in 1:length(RowsP) ){ TablePMultiATSM[[RowsP[j]]] <- ModelPara$ests$K1Z[,j]}


TablePMultiATSM <- round(TablePMultiATSM, digits = 5)
TableP <- rbind(TablePbr,TablePMultiATSM)
row.names(TableP) <- c(RowsP,paste(RowsP," ",sep="")) # trick to avoid rows to have the same name

kableExtra::kbl(TableP, align = "c", caption = "$P$-dynamics parameters") %>%
  kableExtra::kable_classic("striped", full_width = F)  %>%
  kableExtra::row_spec(0, font_size = 14) %>%
  kableExtra::add_header_above(c(" "= 1, "K0Z"=1, "K1Z" = 5), bold = T) %>%
  kableExtra::pack_rows("BR (2017)", 1, 5) %>%
  kableExtra::pack_rows("MultiATSM", 6, 10) %>%
  kableExtra::footnote(general = " $K0Z$ is the intercept and $K1Z$ is feedback matrix from the $P$-dynamics.")

## ----echo= FALSE--------------------------------------------------------------
data("BR_jps_gro_R3")
data("JPSrep")

se <- data.frame(BR_jps_out$est.llk$sigma.e, ModelPara$ests$se )
rownames(se) <- "se"
colnames(se) <- c("MultiATSM","BR (2017)")
se <- round(se, digits = 7)

kableExtra::kbl(se, align = "c", caption ="Portfolio of yields with errors") %>%
  kableExtra::kable_classic("striped", full_width = F)  %>%
  kableExtra::row_spec(0, font_size = 14) %>%
  kableExtra::footnote(general = " $se$ is the standard deviation of the portfolio of yields observed with errors.")

## ----eval=FALSE---------------------------------------------------------------
#  # A) Load database data
#  LoadData("CM_2024")
#  
#  # B) GENERAL model inputs
#  ModelType <- "GVAR multi" # Options: "GVAR multi" or "JLL original".
#  
#  Economies <- c("China", "Brazil", "Mexico", "Uruguay")
#  GlobalVar <- c("Gl_Eco_Act", "Gl_Inflation")
#  DomVar <- c("Eco_Act", "Inflation")
#  N <- 3
#  
#  t0_sample <- "01-06-2004"
#  tF_sample <- "01-01-2020"
#  
#  OutputLabel <- "CM_jfec"
#  DataFreq <-"Monthly"
#  
#  StatQ <- 0
#  
#  # B.1) SPECIFIC model inputs
#  #################################### GVAR-based models ##################################################
#  GVARlist <- list( VARXtype = "unconstrained", W_type = "Sample Mean", t_First_Wgvar = "2004",
#                    t_Last_Wgvar = "2019")
#  #################################### JLL-based models ###################################################
#  JLLlist <- list(DomUnit =  "China")
#  ###################################### BRW inputs  ######################################################
#  WishBC <- 1
#  BRWlist <- within(list(flag_mean = TRUE, gamma = 0.001, N_iter = 200, B = 50, checkBRW = TRUE,
#                         B_check = 1000),  N_burn <- round(N_iter * 0.15))
#  
#  # C) Decide on Settings for numerical outputs
#  WishFPremia <- 1
#  FPmatLim <- c(24,36)
#  
#  Horiz <- 25
#  DesiredGraphs <- c("GIRF", "GFEVD", "TermPremia")
#  WishGraphRiskFac <- 0
#  WishGraphYields <- 1
#  WishOrthoJLLgraphs <- 1
#  
#  # D) Bootstrap settings
#  WishBootstrap <- 0 #  Set it to 1, if bootstrap is desired
#  BootList <- list(methodBS = 'bs', BlockLength = 4, ndraws = 3, pctg =  95)
#  
#  # E) Out-of-sample forecast
#  WishForecast <- 1
#  ForecastList <- list(ForHoriz = 12,  t0Sample = 1, t0Forecast = 100, ForType = "Rolling")
#  
#  #########################################################################################################
#  ############################### 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)

## ----eval=FALSE---------------------------------------------------------------
#  # A) Load database data
#  LoadData("CM_2023")
#  
#  # B) GENERAL model inputs
#  ModelType <- "GVAR multi"
#  
#  Economies <- c("Brazil", "India", "Russia", "Mexico")
#  GlobalVar <- c("US_Output_growth", "China_Output_growth", "SP500")
#  DomVar <- c("Inflation","Output_growth", "CDS", "COVID")
#  N <- 2
#  
#  t0_sample <- "22-03-2020"
#  tF_sample <- "26-09-2021"
#  
#  OutputLabel <- "CM_EM"
#  DataFreq <-"Weekly"
#  
#  StatQ <- 0
#  
#  # B.1) SPECIFIC model inputs
#  #################################### GVAR-based models ##################################################
#  GVARlist <- list( VARXtype = "constrained: COVID", W_type = "Sample Mean", t_First_Wgvar = "2015",
#                    t_Last_Wgvar = "2020")
#  ###################################### BRW inputs  ######################################################
#  WishBC <- 0
#  
#  # C) Decide on Settings for numerical outputs
#  WishFPremia <- 1
#  FPmatLim <- c(47,48)
#  
#  Horiz <- 12
#  DesiredGraphs <- c("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 = 100, pctg =  95)
#  
#  #########################################################################################################
#  ############################### 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)
#  GVARlist$DataConnectedness <- Trade_Flows
#  
#  # 3) Prepare the inputs of the likelihood function
#  ATSMInputs <- InputsForOpt(t0_sample, tF_sample, ModelType, Yields, GlobalMacro, DomMacro, FactorLabels,
#                             Economies, DataFreq, GVARlist)
#  
#  # 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)
#  
#  # 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 = NULL, GVARlist)
#