FCVAR (version 0.1.4)

FCVARlikeGrid: Grid Search to Maximize Likelihood Function

Description

FCVARlikeGrid performs a grid-search optimization by calculating the likelihood function on a grid of candidate parameter values. This function evaluates the likelihood over a grid of values for c(d,b) (or phi). It can be used when parameter estimates are sensitive to starting values to give an approximation of the global maximum that can then be used as the starting value in the numerical optimization in FCVARestn. plot.FCVAR_grid plots the likelihood function from FCVARlikeGrid.

Usage

FCVARlikeGrid(x, k, r, opt)

Arguments

x

A matrix of variables to be included in the system.

k

The number of lags in the system.

r

The cointegrating rank.

opt

An S3 object of class FCVAR_opt that stores the chosen estimation options, generated from FCVARoptions().

Value

An S3 object of type FCVAR_grid containing the optimization results, including the following parameters:

params

A vector params of d and b (and mu if level parameter is selected) corresponding to a maximum over the grid of c(d,b) or phi.

dbHatStar

A vector of d and b corresponding to a maximum over the grid of c(d,b) or phi.

muHatStar

A vector of the optimal mu if level parameter is selected.

Grid2d

An indicator for whether or not the optimization is conducted over a 2-dimensional parameter space, i.e. if there is no equality restriction on d and b.

dGrid

A vector of the grid points in the parameter d, after any transformations for restrictions, if any.

bGrid

A vector of the grid points in the parameter b, after any transformations for restrictions, if any.

dGrid_orig

A vector of the grid points in the parameter d, in units of the fractional integration parameter.

bGrid_orig

A vector of the grid points in the parameter b, in units of the fractional integration parameter.

like

The maximum value of the likelihood function over the chosen grid.

k

The number of lags in the system.

r

The cointegrating rank.

opt

An S3 object of class FCVAR_opt that stores the chosen estimation options, generated from FCVARoptions().

References

Johansen, S. and M. <U+00D8>. Nielsen (2010). "Likelihood inference for a nonstationary fractional autoregressive model," Journal of Econometrics 158, 51-66.

See Also

FCVARoptions to set default estimation options. plot.FCVAR_grid plots the likelihood function from FCVARlikeGrid.

Other FCVAR auxiliary functions: FCVARforecast(), FCVARsimBS(), FCVARsim(), FracDiff(), plot.FCVAR_grid()

Examples

# NOT RUN {
# Restrict equality of fractional parameters.
# }
# NOT RUN {
opt <- FCVARoptions()
opt$dbStep1D     <- 0.2 # Coarser grid for plotting example.
opt$dbMin        <- c(0.01, 0.01) # Set lower bound for d,b.
opt$dbMax        <- c(2.00, 2.00) # Set upper bound for d,b.
opt$constrained  <- 0 # impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
opt$restrictDB   <- 1 # impose restriction d=b ? 1 <- yes, 0 <- no.
opt$progress     <- 2 # Show progress report on each value of b.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
likeGrid_params <- FCVARlikeGrid(x, k = 2, r = 1, opt)
plot(likeGrid_params)
# }
# NOT RUN {
# Linear restriction on fractional parameters.
# }
# NOT RUN {
opt <- FCVARoptions()
opt$dbStep1D     <- 0.2 # Coarser grid for plotting example.
opt$dbMin        <- c(0.01, 0.01) # Set lower bound for d,b.
opt$dbMax        <- c(2.00, 2.00) # Set upper bound for d,b.
opt$constrained  <- 0 # impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
opt$restrictDB   <- 0 # impose restriction d=b ? 1 <- yes, 0 <- no.
# Impose linear restriction on d and b:
opt$R_psi        <- matrix(c(2, -1), nrow = 1, ncol = 2)
opt$r_psi        <- 0.5
opt$progress     <- 2 # Show progress report on each value of b.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
likeGrid_params <- FCVARlikeGrid(x, k = 2, r = 1, opt)
plot(likeGrid_params)
# }
# NOT RUN {
# Constrained 2-dimensional optimization.
# Impose restriction dbMax >= d >= b >= dbMin.
# }
# NOT RUN {
opt <- FCVARoptions()
opt$dbStep1D     <- 0.2 # Coarser grid for plotting example.
opt$dbStep2D     <- 0.2 # Coarser grid for plotting example.
opt$dbMin        <- c(0.01, 0.01) # Set lower bound for d,b.
opt$dbMax        <- c(2.00, 2.00) # Set upper bound for d,b.
opt$constrained  <- 1 # impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
opt$restrictDB   <- 0 # impose restriction d=b ? 1 <- yes, 0 <- no.
opt$progress     <- 2 # Show progress report on each value of b.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
likeGrid_params <- FCVARlikeGrid(x, k = 2, r = 1, opt)
# }
# NOT RUN {
# Unconstrained 2-dimensional optimization.
# }
# NOT RUN {
opt <- FCVARoptions()
opt$dbStep1D     <- 0.1 # Coarser grid for plotting example.
opt$dbStep2D     <- 0.2 # Coarser grid for plotting example.
opt$dbMin        <- c(0.01, 0.01) # Set lower bound for d,b.
opt$dbMax        <- c(2.00, 2.00) # Set upper bound for d,b.
opt$constrained  <- 0 # impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
opt$restrictDB   <- 0 # impose restriction d=b ? 1 <- yes, 0 <- no.
opt$progress     <- 2 # Show progress report on each value of b.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
likeGrid_params <- FCVARlikeGrid(x, k = 2, r = 1, opt)
# }