Dear Folks,
I am running a multinomial logit model for my research. I am creating a categorical variables (dummies) for industries and for advisor.
First of all, how do we calculate the probability as most of the text books use some calculation or newer version of Stata will give us that probabilities straight away in order for u to interpret. If that's the case then how do we interpret? I used this command for the marginal effects.
margins, dy/dx (*) at means predict (pr outcome (2)))
My independent variable are choice of performance measure to be used either (Outcome 1) ROA exclusively, (Outcome 2) ROE exclusively, (Outcome 3) ROE & ROA jointly and Outcome 4( neither ROE nor ROA
X variables consists of the control variables market capitalization, volatility in ROA, volatility in ROE, industry dummies and some of advisor dummies
global ylist
global xlist roev roav lb lnc lev pth lmc Bain (dummy advisor) Mckinsey (dummy advisor ) BG (dummy advisor) Industries dummy)..... etc
* Multinomial logit model with base outcome the most frequent alternative
mlogit $ylist $xlist
margins, dydx(*) atmeans predict(pr outcome(1))
margins, dydx(*) atmeans predict(pr outcome(2))
margins, dydx(*) atmeans predict(pr outcome(3))
margins, dydx(*) atmeans predict(pr outcome(4))
How does it work with the interpretation of dummy Bain advisor? Is it relative to all other advisors ? Do we have to find probabilities or Stata calculates for us?
I also think we can run industry and time effects together?
Here goes the code
Thanks,
I am running a multinomial logit model for my research. I am creating a categorical variables (dummies) for industries and for advisor.
First of all, how do we calculate the probability as most of the text books use some calculation or newer version of Stata will give us that probabilities straight away in order for u to interpret. If that's the case then how do we interpret? I used this command for the marginal effects.
margins, dy/dx (*) at means predict (pr outcome (2)))
My independent variable are choice of performance measure to be used either (Outcome 1) ROA exclusively, (Outcome 2) ROE exclusively, (Outcome 3) ROE & ROA jointly and Outcome 4( neither ROE nor ROA
X variables consists of the control variables market capitalization, volatility in ROA, volatility in ROE, industry dummies and some of advisor dummies
| margins, dydx(*) atmeans predict(pr outcome (2) | dy/dx | Std. | z | P>z [95% | Conf. | Interval] |
| return on asset volatility (ROAV) | -0.04423 | 0.026128 | -1.69 | 0.09 | -0.09544 | 0.006978 |
| Return on equity volatility (ROEV) | -0.32427 | 0.19455 | -1.67 | 0.096 | -0.70558 | 0.057037 |
| board committee lb | 0.097636 | 0.071001 | 1.38 | 0.169 | -0.04152 | 0.236794 |
| nominatee committee % lnc | -0.00397 | 0.001804 | -2.2 | 0.028 | -0.00751 | -0.00043 |
| Leverage lev | -0.00052 | 0.000323 | -1.6 | 0.109 | -0.00115 | 0.000116 |
| Price to book ptb | 0.00661 | 0.003876 | 1.71 | 0.088 | -0.00099 | 0.014206 |
| Market Cpaitalization lmc | -0.04825 | 0.011329 | -4.26 | 0 | -0.07046 | -0.02605 |
| Bain (Dummy Advisor) | 0.095134 | 0.070328 | 1.35 | 0.176 | -0.04271 | 0.232973 |
| Mckinsey (Dummy Advisor) | 0.250295 | 0.058871 | 4.25 | 0 | 0.13491 | 0.36568 |
| BG (Dummy Advisor) | 0.121386 | 0.074217 | 1.64 | 0.102 | -0.02408 | 0.266848 |
| Towers (Dummy Advisor) | 0.118271 | 0.059905 | 1.97 | 0.048 | 0.000861 | 0.235682 |
| Mercer (Dummy Advisor) | 0.591537 | 0.135875 | 4.35 | 0 | 0.325226 | 0.857848 |
| Pwc (Dummy Advisor) | 0.119648 | 0.064528 | 1.85 | 0.064 | -0.00683 | 0.246121 |
| Food Service Industry (Dummy) | 0.046763 | 0.03126 | 1.5 | 0.135 | -0.01451 | 0.108032 |
| Customer Service Industry (Dummy) | 0.047415 | 0.017949 | 2.64 | 0.008 | 0.012236 | 0.082594 |
| Car's Industry (Dummy) | -0.20241 | 0.046227 | -4.38 | 0 | -0.29301 | -0.11181 |
| Genearl Retailers (Dummy) | 0.117296 | 0.02383 | 4.92 | 0 | 0.070591 | 0.164001 |
| Aerospace Industry (Dummy) | 0.084182 | 0.018928 | 4.45 | 0 | 0.047084 | 0.12128 |
| Minning Industry (Dummy) | -0.16032 | 0.027304 | -5.87 | 0 | -0.21383 | -0.1068 |
| Agriculture Industry (Dummy) | -0.14285 | 0.022954 | -6.22 | 0 | -0.18784 | -0.09786 |
| Food court Industry (Dummy) | -0.13902 | 0.021214 | -6.55 | 0 | -0.1806 | -0.09744 |
global ylist
global xlist roev roav lb lnc lev pth lmc Bain (dummy advisor) Mckinsey (dummy advisor ) BG (dummy advisor) Industries dummy)..... etc
* Multinomial logit model with base outcome the most frequent alternative
mlogit $ylist $xlist
margins, dydx(*) atmeans predict(pr outcome(1))
margins, dydx(*) atmeans predict(pr outcome(2))
margins, dydx(*) atmeans predict(pr outcome(3))
margins, dydx(*) atmeans predict(pr outcome(4))
How does it work with the interpretation of dummy Bain advisor? Is it relative to all other advisors ? Do we have to find probabilities or Stata calculates for us?
I also think we can run industry and time effects together?
Here goes the code
Thanks,