SZ.prior.evaluation: Sims-Zha Bayesian VAR Prior Specification Search

In MSBVAR: Markov-Switching, Bayesian, Vector Autoregression Models

Description

Estimates posterior and in-sample fit measures for a reduced form vector autoregression model with different specifications of the Sims-Zha hyperparameters values.

Usage

SZ.prior.evaluation(Y, p,
                    lambda0, lambda1, lambda3, lambda4, lambda5,
                    mu5, mu6, z = NULL, nu = ncol(Y) + 1, qm,
                    prior = 0, nsteps, y.future)

Arguments

Y	T x m matrix of endogenous variables for the VAR
p	Lag length
lambda0	List of values, e,g, c(0.7, 0.8, 0.9) in [0,1], Overall tightness of the prior (discounting of prior scale).
lambda1	List of values, e,g, c(0.05, 0.1, 0.2) in [0,1], Standard deviation or tightness of the prior around the AR(1) parameters.
lambda3	List of values, e,g, c(0, 1, 2) for Lag decay (>0, with 1=harmonic)
lambda4	List of values, e,g, c(0.15, 0.2, 0.5) for Standard deviation or tightness around the intercept [>0]
lambda5	Single value for the standard deviation or tightness around the exogneous variable coefficients [>0]
mu5	Single value for sum of coefficients prior weight [>=0]
mu6	Single value for dummy Initial observations or cointegration prior [>=0]
z	Exogenous variables
nu	Prior degrees of freedom = m+1
qm	Frequency of the data for lag decay equivalence. Default is 4, and a value of 12 will match the lag decay of monthly to quarterly data. Other values have the same effect as "4"
prior	One of three values: 0 = Normal-Wishart prior, 1 = Normal-flat prior, 2 = flat-flat prior (i.e., akin to MLE)
nsteps	Number of periods in the forecast horizon
y.future	Future values of the series, nsteps x m for computing the root mean squared error and mean absolute error for the fit

Details

This function fits a series of BVAR models for the combinations of lambda0, lambda1, lambda3, and lambda4 provided. For each possible value of these parameters specified, a Sims-Zha prior BVAR model is fit, posterior fit measures are computed, and forecasts are generated over nsteps. These nstep forecasts are then compared to a new set of data in y.future and root mean sqaured error and mean absolute error measures are computed.

Value

A matrix of the results with columns corresponding to the values of "lambda0", "lambda1", "lambda3", "lambda4", "lambda5", "mu5", "mu6", "RMSE", "MAE", "MargLLF","MargPosterior".

Note

The matrix of the results can be usefully plotted using the lattice package. See the example below.

Author(s)

Patrick T. Brandt

References

Brandt, Patrick T. and John R. Freeman. 2006. "Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis" Political Analysis 14(1):1-36.

See Also

szbvar

Examples

Y <- EuStockMarkets
results <- SZ.prior.evaluation(window(Y, start=c(1998, 1),
                                       end=c(1998,149)),
                               p=3,
                               lambda0=c(1,0.9),
                               lambda1=c(0.1,0.2),
                               lambda3=c(0,1),
                               lambda4=c(0.1,0.25),
                               lambda5=0,
                               mu5=4,
                               mu6=4, z=NULL,
                               nu=ncol(Y)+1, qm=4,
                               prior=0,
                               nstep=20, 
                               y.future=window(Y, start=c(1998,150)))

# Now plot the RMSE and marginal posterior of the data for each of the
# 6 period forecasts as a function of the prior parameters.  This can
# easily be done using a lattice graphic.

library(lattice)

attach(as.data.frame(results))
dev.new()
xyplot(RMSE ~ lambda0 | lambda1 + lambda3)
dev.new()
xyplot(logMDD ~ lambda0 | lambda1 + lambda3)
dev.new()
xyplot(LLF ~ lambda0 | lambda1 + lambda3)

Example output

##
## MSBVAR Package v.0.9-2
## Build date:  Thu Nov  1 08:57:59 2018 
## Copyright (C) 2005-2018, Patrick T. Brandt
## Written by Patrick T. Brandt
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0351179, SES-0351205, SES-0540816, and SES-0921051)
##

Finished model 1 of 16 
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")

MSBVAR documentation built on May 30, 2017, 1:23 a.m.