*
* Common setup code for Mountford and Uhlig (2009), "What are the
* Effects of Fiscal Policy Shocks?", Journal of Applied Econometrics.
*
open data mudata.xls
calendar(q) 1955:1
data(format=xls,org=columns) 1955:1 2000:4 rgdp rbpexp rbprev ffrt ares $
  ppic gdpdef rcon rnresinv wage
*
set rgdp     = rgdp*100
set rbpexp   = rbpexp*100
set rbprev   = rbprev*100
set ffrt     = ffrt
set ares     = ares*100
set ppic     = ppic*100
set gdpdef   = gdpdef*100
set rcon     = rcon*100
set rnresinv = rnresinv*100
set wage     = wage*100
*
system(model=varmodel)
variables rgdp rbpexp rbprev ffrt ares ppic gdpdef rcon rnresinv wage
lags 1 to 6
end(system)
estimate(noprint)
*
dec vect[strings] vl(10)
compute vl=||"GDP","Expenditure","Revenue","Federal Rate","Reserves","PPI",$
   "GDP deflator","Consumption","Investment","Wages"||
*
* nvar is the number of variables
* nstep is the number of IRF steps to compute
*
compute nvar  =10
compute nstep =25
**************************************************************
*
* Above this line is the setup code that is specific to the data set.
* From here down is common to any such analysis.
*
* Compute scale factors for impulse responses
*
compute [vector] scales=%sqrt(%xdiag(%sigma))
*
* This is the standard setup for MC integration of an OLS VAR
*
compute sigmad =%sigma
compute sxx    =%decomp(%xx)
compute svt    =%decomp(inv(%nobs*%sigma))
compute betaols=%modelgetcoeffs(varmodel)
compute ncoef  =%rows(sxx)
compute wishdof=%nobs-ncoef
*
dec vect[rect] %%responses
dim %%responses(nkeep)
*
dec rect[series] impulses
declare series[rect] irfsquared
*
source uhligfuncs.src

