I have time-series data. I want to run rolling regressions and obtain the decomposed R2 (also called Shapley values)
I can easily obtain the slope coefficients as follows.
Code:
rolling _b , window (40) saving (betas, replace) keep(fqdate):reg rgdp x1 x2
The problem is that I do not know how rego, for example, saves the decomposed R2. I did the following to investigate further
Code:
. rego gdp x1 x2, noperc
------------------------------------------------------------------------------
Gr Regressor | Coef. Std.Err. P>|t| Std.Coef. Shapley R2
--------------------+---------------------------------------------------------
1 x1 | -.1608338 * .0853014 0.061 -0.1375 0.0191
2 x2 | .056379 * .0308584 0.069 0.1333 0.0179
- Intercept | .0256372 .0061803 0.000
--------------------+---------------------------------------------------------
Observations | 184
Overall R2 | 0.03703
Root MSE | .0799205
F-stat. Model | 3.480318 ** 0.033
Log Likelihood | 205.3446
------------------------------------------------------------------------------
. ereturn list
scalars:
e(N) = 184
e(df_m) = 2
e(df_r) = 181
e(F) = 3.480318446798436
e(r2) = .0370324181096345
e(rmse) = .0799205236317307
e(mss) = .0444596071032519
e(rss) = 1.156099507660175
e(r2_a) = .0263918923428902
e(ll) = 205.3446297734195
e(ll_0) = 201.8729608831572
e(rank) = 3
e(noperc) = 1
macros:
e(cmdline) : "rego gdp x1 x2, noperc"
e(regressors) : " x1 x2"
e(cmd) : "rego"
e(title) : "Linear regression"
e(marginsok) : "XB default"
e(vce) : "ols"
e(depvar) : "gdp"
e(properties) : "b V"
e(predict) : "regres_p"
e(model) : "ols"
e(estat_cmd) : "regress_estat"
matrices:
e(b) : 1 x 3
e(V) : 3 x 3
e(shapley_perc) : 1 x 2
e(shapley) : 1 x 2
e(group_details) : 2 x 1
e(vars_per_group) : 2 x 1
e(stdb) : 1 x 3
functions:
e(sample)Then I thought to try something like:
Code:
. rolling _b decomposed=e(shapley_perc), window (40) saving (betas, replace) keep(fqdate):rego gdp x1 x2, noperc (running rego on estimation sample) type mismatch error in expression: e(shapley_perc) r(109);
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