<p>I am trying to compute local projections IR functions with the package LPIRF in R. I have un unbalanced panel dataset of 59 panel units. Data frequency is quarterly and my sample includes 10 years of data. Missing data are left missing in order not to bias results. My main regression model includes 5 bank-specific control variables (of which 3 are of main interest), 1 crisis dummy variable, 3 interaction terms for the 3 independent variables of interest, 3 macroeconomic control variables, time-fixed effects and bank fixed effects. Troughout the regressions, I use clustered standard errors (at the bank level). My question would be how to corretcly implement LPIRF in R considering my main model (should I modify something from the main model to run panel local projections) and if I should conduct any test before doing that. Thank you for your time.</p>

CC BY-SA 4.0
2024-11-10T17:21:18.800Z
Dropping missing data will bias results. The bias may be different to other methods of handling missing values - such as imputation - but omission is still a design choice which affects analysis.
williams-ji
CC BY-SA 4.0
2024-11-11T09:10:21.380Z
Yes, you are indeed correct. However, imputation, especially with respect to publication, may be questionable. With respect, instead, to theoretical implications, would you suggest to keep my model as it is in the main regressions or would it be preferable to apply any modification (e.g. removing interaction terms)? Thank you
Maurizio Marinaro
CC BY-SA 4.0
2024-11-11T14:27:34.150Z
It's impossible to say without knowing what your theory is. Whether or not you should include interaction terms depends upon the extent you believe the independant variables are independant of one another.
williams-ji
CC BY-SA 4.0
2024-11-13T15:23:11.020Z
Maybe I can provide some better context making explicit that interaction terms in my cases are financial ratios interacted with a crisis dummy variable which takes the value of 1 in crisis periods and 0 otherwise.
Maurizio Marinaro
<econometrics><paneldata><r>
Local projections Unbalanced Panel Data in R
Maurizio Marinaro
# Local projections Unbalanced Panel Data in R

(Asked by: username_0 on 2024-11-08)

I am trying to compute local projections IR functions with the package LPIRF in R. I have un unbalanced panel dataset of 59 panel units. Data frequency is quarterly and my sample includes 10 years of data. Missing data are left missing in order not to bias results. My main regression model includes 5 bank-specific control variables (of which 3 are of main interest), 1 crisis dummy variable, 3 interaction terms for the 3 independent variables of interest, 3 macroeconomic control variables, time-fixed effects and bank fixed effects. Troughout the regressions, I use clustered standard errors (at the bank level). My question would be how to corretcly implement LPIRF in R considering my main model (should I modify something from the main model to run panel local projections) and if I should conduct any test before doing that. Thank you for your time.

**Tags:** econometrics, paneldata, r
> **Comments:**
> **username_1**: Dropping missing data will bias results. The bias may be different to other methods of handling missing values - such as imputation - but omission is still a design choice which affects analysis.  
> **username_0**: Yes, you are indeed correct. However, imputation, especially with respect to publication, may be questionable. With respect, instead, to theoretical implications, would you suggest to keep my model as it is in the main regressions or would it be preferable to apply any modification (e.g. removing interaction terms)? Thank you  
> **username_1**: It's impossible to say without knowing what your theory is. Whether or not you should include interaction terms depends upon the extent you believe the independant variables are independant of one another.  
> **username_0**: Maybe I can provide some better context making explicit that interaction terms in my cases are financial ratios interacted with a crisis dummy variable which takes the value of 1 in crisis periods and 0 otherwise.  

---

## 0 answers

