On a different note, why this interest in percent
change in coefficient as a metric?
I make three elementary comments.
1. The behaviour of ratios can be complicated
already. This measure is a ratio calculated from
ratios.
2. Specifically, is the behaviour as the denominator
goes from small positive through zero to small
negative regarded as a feature?
3. There is a lack of symmetry in the calculation.
I can imagine a practical argument that (1) and
(2) do not matter for the application, and (3)
might be irrelevant given a time order, but I wouldn't
put much weight on this measure.
Nick
[email protected]
Tim Wade
> Hi Raphael, I don't know how to do this in Mata, but here is a brute
> force solution using macros and for loops:
>
>
> . regress price headroom rep78 gear_ratio
>
> Source | SS df MS Number
> of obs = 69
> -------------+------------------------------ F( 3,
> 65) = 4.68
> Model | 102521828 3 34173942.7 Prob >
> F = 0.0051
> Residual | 474275131 65 7296540.47
> R-squared = 0.1777
> -------------+------------------------------ Adj
> R-squared = 0.1398
> Total | 576796959 68 8482308.22 Root
> MSE = 2701.2
>
> --------------------------------------------------------------
> ----------------
> price | Coef. Std. Err. t P>|t|
> [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------------
> headroom | -136.9778 414.9107 -0.33 0.742
> -965.6117 691.6561
> rep78 | 576.2363 362.8717 1.59 0.117
> -148.4686 1300.941
> gear_ratio | -2995.126 829.7523 -3.61 0.001
> -4652.256 -1337.996
> _cons | 13577.64 3025.567 4.49 0.000
> 7535.166 19620.12
> --------------------------------------------------------------
> ----------------
>
> /*only include coefficients you want to compare*/
>
> . foreach var of varlist headroom rep78 {
> 2. local `var'1=_b[`var']
> 3. }
>
> . regress price headroom rep78
>
> Source | SS df MS Number
> of obs = 69
> -------------+------------------------------ F( 2,
> 66) = 0.43
> Model | 7450346.06 2 3725173.03 Prob >
> F = 0.6511
> Residual | 569346613 66 8626463.83
> R-squared = 0.0129
> -------------+------------------------------ Adj
> R-squared = -0.0170
> Total | 576796959 68 8482308.22 Root
> MSE = 2937.1
>
> --------------------------------------------------------------
> ----------------
> price | Coef. Std. Err. t P>|t|
> [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------------
> headroom | 391.6261 422.1074 0.93 0.357
> -451.1385 1234.391
> rep78 | 69.23416 363.8024 0.19 0.850
> -657.1208 795.5892
> _cons | 4735.368 1930.863 2.45 0.017
> 880.276 8590.459
> --------------------------------------------------------------
> ----------------
>
> . foreach var of varlist headroom rep78 {
> 2. local `var'2=_b[`var']
> 3. }
>
> . foreach var of varlist headroom rep78 {
> 2. di as result "percent change for
> `var'="((``var'2'-``var'1')/``var'1')*100
> 3. }
> percent change for headroom=-385.90483
> percent change for rep78=-87.985109
Raphael Fraser
> > I would like to calculate the percentage change in the regression
> > coeffecients of model 1 and model 2. Can any one help? I tried using
> > Mata but I did not know how to divide each element in a matrix with
> > different scalars.
> >
> > sysuse auto, clear
> > stset mpg, failure(foreign)
> > stcox mpg price weight rep78, nohr nolog /*Model 1*/
> > stcox mpg weight rep78, nohr nolog /*Model 2*/
> >
> > For example % change = (rep78_m2 - rep78_m1) / rep78_m1
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