standardized system regression coefficients

[kerenc]

can someone help with the command to calculate the standardized coefficients in a system regression (EVIEWS 6)??
PLEASE, I need your help.

(3 equations in each system)

[kerenc]

when I'm running a program with 3 equations to calculate the Z scores, I recieve an error.
can some one please help me correct the code to adjust it to a system estimation?
thank you in advance!

%seriesname = "op_income_mo06" "op_income_mo05" "op_income_mo04"
%groupname = "ind_mo06" "ind_mo05" "ind_mo04"

'PERFORM REGRESSION
equation eq1.ls {%seriesname} {%groupname}
!count={%groupname}.@count
vector(!count) sbetas
!stdevY = @stdev({%seriesname})
for !i=1 to !count
%name={%groupname}.@seriesname(!i)
!stdevX=@stdev({%name})
sbetas(!i)=eq1.@coefs(!i) * ( !stdevX/ !stdevY )
next

[Dataminer]

when I'm running a program with 3 equations to calculate the Z scores

My interpretation of your question and the following code is that you wish to use the programming constructs to estimate a system of OLS regressions in which a number of depvars are regressed on a common set of indepvars but to report results in a form which I used to know as 'beta coefficients' i.e. all variables expressed in standardised (zero mean unit variance) form so that the arithmetic size of estimated slope coefficients can be compared and used to indicate 'importance' of the associated indep var.. The programming constructs become effective if you have a large number of these. If this is so I would recommend transforming all the variables to that form before running a standard regression. This might have been questioned in days when computers might induce rounding errors by working to too few decimal places but that is unlikely to apply now, especially if the raw data is capable of displaying regression results that are in any way 'satisfactory'. You might also like to then consider the contribution of 'system' methods in eviews. Put briefly, my previous description implies estimation of the parameters equation by equation (including error variance) freely, whereas system methods offer the specification of a wide variety of cross-equation restrictions between these parameters. If your application offers information which suggest the validity of some restrictions then estimation would be more efficient if they are incorporated into the estimation process. For the moment this reply seeks a clearer specification of your requirements.

[Dataminer]

Kerenc
As a followup from my earlier message see the attached code. It shortens your variable names to y(1 2 3) and z(1 2 3) respectively. Note also that whilst the regressions eq1 that you specified were with un-transformed variables so that the intercept term should have been specified whereas in the following the transformed variables imply that an intercept need not be specified.
Gerald

Code: Select all

for %sector 1 2 3 series ty{%sector} = (y{%sector} - @mean(y{%sector}))/@stdevp(y{%sector}) series tz{%sector} = (z{%sector} - @mean(z{%sector}))/@stdevp(z{%sector}) next ' %groupname = "tz1 tz2 tz3" for %seriesname ty1 ty2 ty3 'PERFORM REGRESSION equation {%seriesname}eq1.ls {%seriesname} {%groupname} next

[kerenc]

thank you so much for your answers.
I'm not sure I undestand you completely. where in the code I have to specify my series and groups names? your first two rows standardize the var?

I have a system of 3 regression equations that I estimate simultanusly (LS). I have constrains for all var, excluding the intercept. how can I adjust your code to my model in order to recieve the standardized coefficients?

the dependent variables are "op_income_mo06" "op_income_mo05" "op_income_mo04"
the independent variables are in 3 groups "ind_mo06", "ind_mo05" "ind_mo04"

for each equation I have 580 observations

the system estimation is:

op_income_mo06 = c(2)*incorporation+c(3)*age_mo06+c(4)*size_med+c(5)*size_lar+c(6)*econ_group_software+c(7)*econ_group_semiconductor+c(8)*slack_mo06+c(9)*exploration_mo06+c(10)*exploration_sq_mo06+c(11)*exploitation_mo06+c(12)*exploitation_sq_mo06+c(13)*dynamism_mo06+c(14)*competition_mo06+c(15)*explr_dyn_mo06+c(16)*explr_dyn_sq_mo06+c(17)*explt_dyn_mo06+c(18)*explt_dyn_sq_mo06+c(19)*explr_comp_mo06+c(20)*explr_comp_sq_mo06+c(21)*explt_comp_mo06+c(22)*explt_comp_sq_mo06+c(1)

op_income_mo05 = c(2)*incorporation+c(3)*age_mo05+c(4)*size_med+c(5)*size_lar+c(6)*econ_group_software+c(7)*econ_group_semiconductor+c(8)*slack_mo05+c(9)*exploration_mo05+c(10)*exploration_sq_mo05+c(11)*exploitation_mo05+c(12)*exploitation_sq_mo05+c(13)*dynamism_mo05+c(14)*competition_mo05+c(15)*explr_dyn_mo05+c(16)*explr_dyn_sq_mo05+c(17)*explt_dyn_mo05+c(18)*explt_dyn_sq_mo05+c(19)*explr_comp_mo05+c(20)*explr_comp_sq_mo05+c(21)*explt_comp_mo05+c(22)*explt_comp_sq_mo05+c(23)

op_income_mo04 = c(2)*incorporation+c(3)*age_mo04+c(4)*size_med+c(5)*size_lar+c(6)*econ_group_software+c(7)*econ_group_semiconductor+c(8)*slack_mo04+c(9)*exploration_mo04+c(10)*exploration_sq_mo04+c(11)*exploitation_mo04+c(12)*exploitation_sq_mo04+c(13)*dynamism_mo04+c(14)*competition_mo04+c(15)*explr_dyn_mo04+c(16)*explr_dyn_sq_mo04+c(17)*explt_dyn_mo04+c(18)*explt_dyn_sq_mo04+c(19)*explr_comp_mo04+c(20)*explr_comp_sq_mo04+c(21)*explt_comp_mo04+c(22)*explt_comp_sq_mo04+c(45)

[Dataminer]

kerenc,
The equation system you define is effectively that of a panel data generation process (DGP) in which the unobservable fixed effects are represented by 'sector' specific intercepts and with common observable 'environment' independent variables. This model is implemented in Eviews using the POOL object and estimation can proceed with ls (least squares) using cross section fixed effects (cx=f). As stated, your DGP makes no reference to the error specification so a conventional assumption might be that having allowed for these fixed effects all residuals have zero mean and common variance. In this situation LS on the 'stacked' data is optimal and the cited Eviews procedure incorporates this stacking. Were the error variances to be sector specific then using the random effects (cx=r) specification would be appropriate.
However by proposing to standardise all dependent variable datasets you are effectively defining a 2-stage estimation process, the first stage of which effectively knocks out any fixed and random effects heterogeneity in such a panel dataset. Indeed this standardisation is one of the initial steps that might be undertaken in estimation of both balanced and unbalanced panels. Standardising the observations on each of the sectoral dependent variables effectively transforms them each to (estimates of) zero mean and unit variance random variables and thereby renders them amenable to pooled estimation in which a larger quantity of now homogeneous data renders estimators of parameters in the DGP that are more efficient.
As far as standardisation of the independent variables are concerned, this contributes the conventional interpretation of parameter estimates know as beta coefficients, namely the marginal effect of a standardised unit of each independent variable on the dependent variable (whether the latter is standardised or not). As such each of these parameter values may be compared one to another and interpreted as relative 'importance' of their effects on the depvar (in whichever form). Since all the independent variables in this formulation are common across sectors, this interpretation is one that will be sector-unspecific. These comparisons of course may be tempered by conventional interpretations of the t-stats or P-values given in the Eviews pooled output tableau. However beta coefficients do not seem to appear in the literature very frequently these days and have largely disappeared from the text book literature. This is probably due to the obscurity of their interpretation brought about by the concept of 'standardised units of measurement'. I would recommend that unless you are working in a context that renders them particularly relevant, you don't bother with them or at least present them (for a specific purpose) in addition to the conventional estimates using independent variables measured in natural units.
I have in preparation an eviews program which generates artificial data in the style of the above DGP and applies the relevant POOL object. I will transmit this later by PM.
Gerald