Estimation of MRR (1997) model with GMM
Re: Estimation of MRR (1997) model with GMM
Tom,
I understand that when a model is just identified, for a given set of instruments, the point estimates don't depend upon the weight matrices.
Keeping the model just identified, when I choose different set of instruments, I get different results. This leads to subjectivity.
I am trying to avoid this subjectivity. If I use moment conditions only and no instrumental variables, then I do not have to worry about the sensitivity of the results to the choice of instruments. I will still keep the model just identified by having the number of moment conditions equal to the number of parameters. I wanted to know if there is a problem related to this.
I understand that when a model is just identified, for a given set of instruments, the point estimates don't depend upon the weight matrices.
Keeping the model just identified, when I choose different set of instruments, I get different results. This leads to subjectivity.
I am trying to avoid this subjectivity. If I use moment conditions only and no instrumental variables, then I do not have to worry about the sensitivity of the results to the choice of instruments. I will still keep the model just identified by having the number of moment conditions equal to the number of parameters. I wanted to know if there is a problem related to this.
Re: Estimation of MRR (1997) model with GMM
But you are using instrumental variables---just a particular choice of them (1 and R(t) I assume). The terminology dates back to Hansen and Singleton(1982). Any function of R(t) is also available, and since the functions are non-linear it's not clear that the linear instrument is necessarily the best.
Re: Estimation of MRR (1997) model with GMM
Hi Tom,
It would be great if I can get your feedback about my program. I just want to make sure that it does not have errors before I begin estimations and writing a paper. Since the number of orthogonality conditions ( corr, cons, ols1, ols2 in nlsystem) is equal to the number of coefficients (4) to be estimated, model is just identified and J-statistic is not produced in the output. These 4 orthogonality conditions are specified in the MRR (1997) paper that I mentioned earlier. Thank you.
It would be great if I can get your feedback about my program. I just want to make sure that it does not have errors before I begin estimations and writing a paper. Since the number of orthogonality conditions ( corr, cons, ols1, ols2 in nlsystem) is equal to the number of coefficients (4) to be estimated, model is just identified and J-statistic is not produced in the output. These 4 orthogonality conditions are specified in the MRR (1997) paper that I mentioned earlier. Thank you.
Code: Select all
*A=1 if transaction is buyer initiated and A=-1 if trade is seller initiated.
*L1A is last A.
*TrPrice is transaction price and L1TrPrice is last transaction price.
*
set Ret = 100*(TrPrice-L1TrPrice)
*
nonlin(parmset=baseparms) KV TV RV alpha
*
frml eps = Ret-(KV+TV)*A+(KV+RV*TV)*L1A
frml corr = A*L1A-A*A*RV
frml cons = eps-alpha
frml ols1 = eps*A-alpha*A
frml ols2 = eps*L1A-alpha*L1A
*
linreg Ret
# Constant A L1A
*
compute KV=%beta(2)*0.5
compute TV=%beta(2)*0.5
compute RV=0.5
compute alpha = %beta(1)
*
instruments L1A{1}
nlsystem(inst,parmset=baseparms,trace,zudep) / corr cons ols1 ols2
*
........
Non-Linear Optimization, Iteration 0. Function Calls 1.
Cosine of Angle between Direction and Gradient 0.1535572. Alpha used was 0.000000
Adjusted squared norm of gradient 12.7565
Diagnostic measure (0=perfect) 0.0000
Subiterations 1. Distance scale 1.000000000
Old Function = 9.136313 New Function = 2.254782
New Coefficients:
0.112662 0.107324 0.296196 -0.001811
Non-Linear Optimization, Iteration 1. Function Calls 2.
Cosine of Angle between Direction and Gradient 0.9264844. Alpha used was 0.000000
Adjusted squared norm of gradient 1213.861
Diagnostic measure (0=perfect) 0.7000
Subiterations 1. Distance scale 1.000000000
Old Function = 18.281797 New Function = 0.000000
New Coefficients:
0.121835 0.098151 0.296196 -0.001811
Non-Linear Optimization, Iteration 2. Function Calls 3.
Cosine of Angle between Direction and Gradient 0.8024985. Alpha used was 0.000000
Adjusted squared norm of gradient 2.718637e-020
Diagnostic measure (0=perfect) 0.4200
Subiterations 1. Distance scale 1.000000000
Old Function = 0.000000 New Function = 0.000000
New Coefficients:
0.121835 0.098151 0.296196 -0.001811
GMM-Continuously Updated Weight Matrix
Convergence in 2 Iterations. Final criterion was 0.0000000 <= 0.0000100
Usable Observations 1778678
Skipped/Missing (from 1778680) 2
Function Value 4.58938198e-022
Variable Coeff Std Error T-Stat Signif
***************************************************************************************
1. KV 0.121835011 0.013608726 8.95271 0.00000000
2. TV 0.098150551 0.026015506 3.77277 0.00016144
3. RV 0.296196264 0.074459016 3.97798 0.00006950
4. ALPHA -0.001810637 0.001289142 -1.40453 0.16016167
Re: Estimation of MRR (1997) model with GMM
CONSTANT isn't in your instrument set? That's usually there at a minimum.
Re: Estimation of MRR (1997) model with GMM
Thank you Tom.