PARMFILE HISTORY (optional)
#
# This parmfile was written by SNP Version 9.1 using the following line from
# control.dat, which was read as char*, char*, float, float, int, int
# -----------------------------------------------------------------------------
#      input_file          output_file         fnew      fold   nstart   jseed
# -------------------- -------------------- --------- --------- ------ --------
# 00f0f0f0f1200000.in0 00f0f0f0f1200000.f08 -1.00e-04  1.00e-04     25   454589
# -----------------------------------------------------------------------------
# If fnew is negative, only the polynomial part of the model is perturbed.
# Similarly for fold.
#
OPTIMIZATION DESCRIPTION (required)
    pedist     Project name, pname, char*
       9.1     SNP version, defines format of this file, snpver, float
        10     Maximum number of primary iterations, itmax0, int
       290     Maximum number of secondary iterations, itmax1, int
  1.00e-08     Convergence tolerance, toler, float
         1     Write detailed output if print=1, int
         0     task, 0 fit, 1 res, 2 mu, 3 sig, 4 plt, 5 sim, 6 usr, int
         0     Increase simulation length by extra, int
  3.00e+00     Scale factor for plots, sfac, float
       457     Seed for simulations, iseed, int
        50     Number of plot grid points, ngrid, int
         0     Statistics not computed if kilse=1, int
DATA DESCRIPTION (required)
         3     Dimension of the time series, M, int
      2000     Number of observations, n, int
        14     Provision for initial lags, must have 3<drop<n, int
         0     Condition set for plt is mean if cond=0, it-th obs if it, int
         1     Reread, do not use data from prior fit, if reread=1, int
Z_0050.dat     File name, any length, no embedded blanks, dsn, string
1 2 3          Read these white space separated fields, fields, intvec
         0     Number extra data sets of n obs each in file dsn, panels, int
TRANSFORM DESCRIPTION (required)
         0     Normalize using start values if useold=1 else compute, int
         0     Make variance matrix diagonal if diag=1, int
         1     Spline transform x if squash=1, logistic if squash=2, int
  2.00e+00     Inflection point of transform in normalized x, inflec, float
POLYNOMIAL START VALUE DESCRIPTION (required)
         0     Degree of the polynomial in z, Kz, int
         0     Degree of interactions in z, Iz, int
  1.00e-08     Zero or positive to get positive SNP for EMM, eps0, float
         1     Lags in polynomial part, Lp, int
         0     Max degree of z polynomial that depends on x, maxKz, int
         0     Max interaction of z polynomial that depends on x, maxIz, int
         0     Degree of the polynomial in x, Kx, int
         0     Degree of the interactions x, Ix, int
MEAN FUNCTION START VALUE DESCRIPTION (required)
         0     Lags in VAR part, Lu, int
         1     Intercept if icept=1, int
VARIANCE FUNCTION START VALUE DESCRIPTION (required)
         0     Lags in GARCH (autoregressive) part, may be zero, Lg, int
         f     Coded 's','d','f' for scalar, diagonal, full, Qtype, char
         0     Lags in ARCH (moving average) part, may be zero, Lr, int
         f     Coded 's','d','f' for scalar, diagonal, full, Ptype, char
         0     Lags in leverage effect of GARCH, may be zero, Lv, int
         f     Coded 's','d','f' for scalar, diagonal, full, Vtype, char
         0     Lags in additive level effect, may be zero, Lw, int
         f     Coded 's','d','f' for scalar, diagonal, full, Wtype, char
POLYNOMIAL DESCRIPTION (optional)
         0     Increment or decrement to Kz, int
         0     Increment or decrement to Iz, int
  0.00e+00     Increment or decrement to eps0, float
         0     Increment or decrement to Lp, int
         0     Increment or decrement to maxKz, int
         0     Increment or decrement to maxIz, int
         0     Increment or decrement to Kx, int
         0     Increment or decrement to Ix, int
MEAN FUNCTION DESCRIPTION (optional)
         0     Increment or decrement to Lu, int
         0     Increment or decrement to icept, int
VARIANCE FUNCTION DESCRIPTION (optional)
         0     Increment or decrement to GARCH lag Lg, int
         f     Coded 's','d','f' for scalar, diagonal, full, Qtype, char
         0     Increment or decrement to ARCH lag Lr, int
         f     Coded 's','d','f' for scalar, diagonal, full, Ptype, char
         0     Increment or decrement to leverage effect lag Lv, int
         f     Coded 's','d','f' for scalar, diagonal, full, Vtype, char
         0     Increment or decrement to level effect lag Lw, int
         f     Coded 's','d','f' for scalar, diagonal, full, Wtype, char
POLYNOMIAL START VALUES FOR A (optional)
   1.00000000000000000e+00    0
MEAN FUNCTION START VALUES FOR b0 (optional)
   4.20439282110133638e-04    1
  -2.75985840217676803e-03    1
   5.90065420989997606e-04    1
VARIANCE FUNCTION START VALUES FOR Rparms (optional)
   1.00255126652531334e+00    1
   3.11829310540854112e-04    1
   9.99334383442942786e-01    1
  -1.73186540229263564e-04    1
  -1.07091263665552629e-03    1
   9.98927635625781907e-01    1
TRANSFORM START VALUES FOR mean (optional)
   1.14796039187999788e+00
  -8.16360852650000191e-02
   1.44246009499999946e-02
TRANSFORM START VALUES FOR variance (optional)
   4.61239280796379258e+00
  -1.72795257730212204e-01
   1.04008205160994432e-01
  -1.72795257730212204e-01
   3.95632376978288702e+00
   6.17087070166268386e-03
   1.04008205160994432e-01
   6.17087070166268386e-03
   4.47424696397155497e+00
SUMMARY STATISTICS (optional)
Fit criteria:
  Length rho =            9
  Length theta =         10
  n - drop =           1986
  -2 ln likelihood =       16911.28079535    1.69112807953506162e+04
  sn =            4.22782020    4.22782019883765425e+00
  aic =           4.23232020    4.23232019883765442e+00
  hq =            4.23694740    4.23694740026979133e+00
  bic =           4.24492223    4.24492222937162378e+00
Index           theta       std error     t-statistic    descriptor
    1         1.00000         0.00000         0.00000    A(1,1)  000 000
    2         0.00042         0.03081         0.01365    b0[1] 
    3        -0.00276         0.02412        -0.11443    b0[2] 
    4         0.00059         0.02363         0.02498    b0[3] 
    5         1.00255         0.01459        68.70950    R0[1] 
    6         0.00031         0.01117         0.02791    R0[2] 
    7         0.99933         0.01570        63.64246    R0[3] 
    8        -0.00017         0.01089        -0.01591    R0[4] 
    9        -0.00107         0.01070        -0.10011    R0[5] 
   10         0.99893         0.01429        69.92815    R0[6] 

For estimation of this SNP specification by MCMC, restrict the following
elements of theta to be positive:  5 7 10

Largest eigen value of mean function companion matrix = 0
Largest eigen value of variance function P & Q companion matrix = 0

