Consider two different models.
(1) DV of a duration (a length of time in minutes)
(2) DV is a frequency (a count)
Their regressors are:
(1) volume1 volume2 and countR, where countR is endogenous
(2) control variables affect both the DVs and the regressor countR
(3) firm, day of the week and week count dummies
Furthermore, the dependent variables need to somehow be considered together. In other words, what is being studied can occur for long periods of time frequently or rarely for short periods of time, etc.
DV (1) is on a daily, per product per firm basis. There are many zeros and it is also within a range (has a cut-off maximum)
DV (2) is a total sum over the entire period (8 weeks) of data
Prior research shows two-way and 3-way interaction for volume1, volume2, and countR
My data is showing me that volume1 and volume2 are highly correlated. So I may have to use them in separate models. But in that case, how do I test for the 3-way interaction?
My contribution will be the data itself (the DVs). They are actual measures of a phenomenon, instead of estimations. So I would like to keep the model as close as possible to prior theory development. Prior work used a simulation for the 3 factors (volume1, volume2, countR) and ran ANOVA tests for their count output.
A second contribution is a 0/1 dummy that tests the effects of a new technology in this field. Using the technology *should* decrease duration and frequency of phenomenon occurrence.
If there is no user-defined command, please advise on manually running this. I've looked at ivreg2 and ivpois and am not sure on incorporating interaction effects across stages on the latter command. Please let me know if I've neglected including a detail of the setup. To reiterate, I am interested in studying the same relationships between the three variables (volume1, volume2, countR) as prior literature, just with using exact measures of the phenomenon instead of estimated or simulated ones.
Thanks.
(1) DV of a duration (a length of time in minutes)
(2) DV is a frequency (a count)
Their regressors are:
(1) volume1 volume2 and countR, where countR is endogenous
(2) control variables affect both the DVs and the regressor countR
(3) firm, day of the week and week count dummies
Furthermore, the dependent variables need to somehow be considered together. In other words, what is being studied can occur for long periods of time frequently or rarely for short periods of time, etc.
DV (1) is on a daily, per product per firm basis. There are many zeros and it is also within a range (has a cut-off maximum)
DV (2) is a total sum over the entire period (8 weeks) of data
Prior research shows two-way and 3-way interaction for volume1, volume2, and countR
My data is showing me that volume1 and volume2 are highly correlated. So I may have to use them in separate models. But in that case, how do I test for the 3-way interaction?
My contribution will be the data itself (the DVs). They are actual measures of a phenomenon, instead of estimations. So I would like to keep the model as close as possible to prior theory development. Prior work used a simulation for the 3 factors (volume1, volume2, countR) and ran ANOVA tests for their count output.
A second contribution is a 0/1 dummy that tests the effects of a new technology in this field. Using the technology *should* decrease duration and frequency of phenomenon occurrence.
If there is no user-defined command, please advise on manually running this. I've looked at ivreg2 and ivpois and am not sure on incorporating interaction effects across stages on the latter command. Please let me know if I've neglected including a detail of the setup. To reiterate, I am interested in studying the same relationships between the three variables (volume1, volume2, countR) as prior literature, just with using exact measures of the phenomenon instead of estimated or simulated ones.
Thanks.