How to show Wald, LR test, SSE or σ ̂u, θ and LR test in a regression output table?

[Lucas Bordure]

Good morning to the community Stata
I would like to perform a comparison between Pooled OLS model, and estimators of random-effects model (xtreg, xtmixed and xtreg mle).
My regression is: firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility
To design correctly the comparison, I would like to design a comparison table with the regression output.

But I have difficulties to show the output for Wald, LR test, SSE or σ ̂u, θ and LR test in a regression output table. Do you know if a code exists for this?

I runned this command but Wald, LR test, SSE or σ ̂u, θ and LR test are missing.

eststab using example.rtf, se scalar (F df_m mss rss rmse r2 r2_a N)

My entire code is:

use"File4.dta", clear

global i i
global t t
global ylist firm_performance
global xlist intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility

describe $i $t $ylist $xlist
summarize  $i $t $ylist $xlist

*Pooled_OLS model
eststo: regress firm_performance intangible_assets enterprise_value market_capitalization *leverage stock_growth dividend_payout_ratio stock_volatility

*LSDV | LSDV1 without a dummy
*eststo: regress firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility g1-g8
*test g1 g2 g3 g4 g5 g6 g7 g8

*Comparison between Pooled_OLS_model and LSDV model
*esttab using example.rtf, p scalars (F df_m rss rmse r2 r2_a N)
*eststo clear

*LSDV2 (without intercept)
*eststo: regress firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility g1-g9, noconstant

*LSDV3 (with constraint)
*constraint define 1 g1 + g2 + g3 + g4 + g5 + g6 + g7 + g8 + g9 = 0
*eststo: cnsreg firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility g1-g9, constraint(1)

*Comparison between LSDV1, LSDV2, LSDV3
*esttab using example.rtf, se scalars  (F df_m rss rmse r2 r2_a N)
*eststo clear

*One-way fixed effect ("within" estimation) with xtreg
*eststo: xtreg firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility, fe i(industry_n)

*One-way fixed effect ("within" estimation) with atreg
*eststo: areg firm_performance intangible_assets enterprise_value market_capitalization *leverage stock_growth dividend_payout_ratio stock_volatility, absorb(industry_n)

*Two-way fixed ("within" estimation)

*Comparison between OLS, LSDV and fixed "within" effects model
*esttab using example.rtf, se scalars  (F df_m mss rss rmse r2 r2_a N)
*eststo clear

*"Between" estimation
*xtreg firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility, be i(industry_n)

*One-way random effect xtreg
eststo: xtreg firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility, re theta

*One-way random effect xtmixed
eststo: xtmixed  firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility ||  industry_n:,

*One-way random effect xtreg mle
eststo: xtmixed firm_performance intangible_assets enterprise_value market_capitalization leverage stock_growth dividend_payout_ratio stock_volatility || industry_n:, mle

*Comparison between OLS, xtreg xtmixed and xtreg mle
eststab using example.rtf, se scalar (F df_m mss rss rmse r2 r2_a N)

Have a good day!