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gbnlpanel - Online in the Cloud

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gbnlpanel - Non-linear panel regression


gbnlpanel [options] <FUN>


Each row of a block represents a single process realization. Blocks are separated by two
white spaces and represent model variables, columns represent the time variable. Input is
read as (X1_[r,t],...,Xj_[r,t],... XN_[r,t]), where r is the row, t the column and j the
block. The model assumes that

FUN(X1_[r,t],...,XN_[t,t]) - c_r = e_{r,t}

with e i.i.d and 'c' arow specific element, which can be fixed (fixed effect) or a normal
random variable (random effect).


-M type of model (default 0)

0 fixed effects

1 random effects

-O type of output (default 0)

0 parameters

1 parameters and errors

2 <variables> and panel statistics

3 parameters and variance matrix

-V variance matrix estimation (default 0)

0 < J^{-1} >, computed via fully-reduced log-likelihood

1 < H^{-1} >, computed via fully-reduced log-likelihood

2 < H^{-1} J H^{-1} >, computed via fully-reduced log-likelihood

3 < J^{-1} >, computed via non-reduced log-likelihood

4 < H^{-1} >, computed via non-reduced log-likelihood

5 < H^{-1} J H^{-1} >, computed via non-reduced log-likelihood

-v verbosity level (default 0)

0 just results

1 comment headers

2 summary statistics

3 covariance matrix

4 minimization steps

5 model definition

-e minimization tolerance (default 1e-6)

-F input fields separators (default " \t")

-h this help

-A comma separated MLL optimization options: step,tol,iter,eps,msize,algo. Use empty
fields for default. (default 0.1,0.01,500,1e-6,1e-6,0)

step initial step size of the searching algorithm

tol line search tolerance iter: maximum number of iterations

eps gradient tolerance : stopping criteria ||gradient||<eps

algo optimization methods: 0 Fletcher-Reeves, 1 Polak-Ribiere, 2
Broyden-Fletcher-Goldfarb-Shanno, 3 Steepest descent (not recommended), 4 simplex,
5 Broyden-Fletcher-Goldfarb-Shanno-2


gbnlpanel 'x1-a*x2-c,a=0,c=0' < data.dat
linear panel regression with one independent variable

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