R/borrow.const.R

In mirtaher/TwoPeriodSim: Two period model risk sharing using the collective model

#' Borrowing Constraints
#'
#' This function finds the max and min amount of savings given a specific income shocks
#' parameters and individual family (i) and specific repetition (r). If income sgocks pars are not specified
#' by defualt the values in the param function would be adopted.
#'  First, it checks whether the incomes are NAs, whuch in this case just returns two NAs.
#'  Otherwise, the minimum savings (max borrowing) in the first period is the minimum realized
#'  second period income in simulation in the state of marriage and divorce for each of spouses.
#'  The minimum of these three anounts, i.e. min second period income in the state of divorce for husband, wife, and
#'  family if tehy decide to stay married, would constitute as the min savings. In addition, an starvation consumption in the second
#'  period (which specifies by tol variable) sets aside.
#'  The maximum saving is the whole total first period income after setting aside the starvation consumption in the first period.
#'
#' @param i The marriage index
#' @param r First period repetition. It is not necessary to be greater than one for the first period. It is needed for taking expectaions, which is required in the second period
#' @param sigma_eta_h The husband's variance of transitory shock. If not specified the default is the baseline value specified in the param()
#' @param rho The contemporaneous correlation coefficient of the husband and wife income shocks. If not specified the default is the baseline value specified in the param()
#' @param phi The ratio of the wife's standard deviation of the transitory shock to that of the husband. If not specified the default is the baseline value specified in the param()
#' @export



borrow.const <- function(i, r, sigma_eta_h = param()$sigma_eta_h, rho = param()$rho, phi = param()$phi){


  # Distributing parameters
  par <- param()
  N <- par$N
  reps1 <- par$reps1
  reps2 <- par$reps2
  seed <- par$seed

  theta <- par$theta
  mu <- par$mu
  beta <- par$beta
  ybar <- par$ybar

  delta <- par$delta
  tau.d <- par$tau.d

  wt.h <- par$wt.h
  wt.w <- par$wt.w
  u <- par$u
  U <- par$U
  u.grad <- par$u.grad

  income <- TwoPeriodSim::incomeProcess(sigma_eta_h = sigma_eta_h, Rho = rho, Phi = phi)
  y1 <- income$y1
  y2 <- income$y2

  miss <- is.na(y1[i,1,r]) | is.na(y1[i,2,r])
  if (miss) {
    s.min <- NA
    s.max <- NA
  } else {
    equal <- (y1[i,1,r] + y1[i,2,r])/2
    y2.h.min <- min(y2[i,1,r,])
    y2.w.min <- min(y2[i,2,r,])
    y2.min <- min(y2[i,1,r,] + y2[i,2,r,])
    tol.cons <- 0.01

    b.mar <- (-beta) * y2.min  * (1 - tol.cons)
    b.h <- (-beta)/delta * y2.h.min * (1 - tol.cons)
    b.w <- (-beta)/(1 - delta) * y2.w.min * (1 - tol.cons)

    s.min <- max(b.mar, b.h, b.w)

    if (equal > 0){
      s.max <- (y1[i,1,r] + y1[i,2,r]) * (1 - tol.cons)
    } else {
      s.max <- (y1[i,1,r] + y1[i,2,r]) * (1 + tol.cons)
      if (s.max < s.min){
        s.min <- NA
        s.max <- NA
      }
    }
  }

  return(list("lower" = s.min, "upper" = s.max))

}