For some reason, I cannot log in and continue answering the post I wrote yesterday.
For the question on how to use weights in MLE when each observation has an assigned weight, the following is a part of the code I am using.
_max_CovPar= 1; /* Computation method for variance-covariance matrix */
_max_Algorithm= 4; /* Maximization method */
{ x, f0, g, h, retcode } = MAXLIK( newy, 0, &lpsn, x0 );
call MAXPrt(x,f0,g,h,retcode); @ x - parameter estiamtes @I cannot figure out how to apply these individual weights. Any help/comments or example would be greatly appreciated.
1 Answer
0
maxlik has a global control variable __weight which you can use to set observations weights. It assumes that the sum of the weights is the sum of the number of observations.
// Weight each observation equally
__weight = ones(rows(newy), 1);
_max_CovPar= 1; /* Computation method for variance-covariance matrix */
_max_Algorithm= 4; /* Maximization method */
{ x, f0, g, h, retcode } = MAXLIK( newy, 0, &lpsn, x0 );
call MAXPrt(x,f0,g,h,retcode); @ x - parameter estiamtes @Here is a quick example showing how to adjust the weights so that the first 10 observations have half the weight of the second 10.
// Create a 20x1 vector containing the ratios you want for weights
// here obervations 11-20 will be weighted at 2 times
// observations 1-10
__weight = reshape(1, 10, 1) | reshape(2, 10, 1)
// Adjust the weights so they keep the same
// ratio and sum to the rows(__weight)
__weight = .* (rows(__weight) ./ sumc(__weight));
Your Answer
1 Answer
maxlik has a global control variable __weight which you can use to set observations weights. It assumes that the sum of the weights is the sum of the number of observations.
// Weight each observation equally
__weight = ones(rows(newy), 1);
_max_CovPar= 1; /* Computation method for variance-covariance matrix */
_max_Algorithm= 4; /* Maximization method */
{ x, f0, g, h, retcode } = MAXLIK( newy, 0, &lpsn, x0 );
call MAXPrt(x,f0,g,h,retcode); @ x - parameter estiamtes @Here is a quick example showing how to adjust the weights so that the first 10 observations have half the weight of the second 10.
// Create a 20x1 vector containing the ratios you want for weights
// here obervations 11-20 will be weighted at 2 times
// observations 1-10
__weight = reshape(1, 10, 1) | reshape(2, 10, 1)
// Adjust the weights so they keep the same
// ratio and sum to the rows(__weight)
__weight = .* (rows(__weight) ./ sumc(__weight));