Hello all,
I am trying to navigate my way through nonlinear models using panel data. I am familiar with nonlinear models without panel data, but using these models with panel data is a new space for me. I am also familiar with xtreg and curvefit. My main goal right now is to find the model that best fits my data (e.g., quadratic, power, growth, etc.); i.e., to understand the relationship between my variables of interest. Here is my design and what I have done:
My data: annual data over 10 years, 500 companies
Dependent variable: Company financial performance
Main independent (predictor) variable of interest: Company innovation
Theoretically, I suspect an inverted U-shape relationship between performance and innovation; i.e., very little or very high innovation is associated with lower levels of company financial performance than moderate levels of innovation. Now I want to test this (or see if there is a different nonlinear relationship between financial performance and innovation).
I have plotted the data on a twoway plot to observe the structure. It looks like an inverted U or an inverted growth (exponential) relationship, where increasing innovation at very low levels of innovation drastically increases financial performance but then this relationship tapers off (flattens out). So a few issues/question:
1) I have used curvefit but I don't think it works with panel data. Maybe I am wrong? I also don't think the twoway scatter accounts for the panel structure of the data.
2) I don't know if I can use nonlinear models with fixed effects. Does this make sense? So my code for a quadratic relationship (exploring an inverted U) looks like this:
xtreg financial_performance c.innovation##c.innovation control_variables i.yr, fe
I found the output is significant and plotted the output using marginsplot and all points towards an inverted U, but I want to make sure I'm not missing something in what I am doing.
c.innovation = +ve and significant @ 1%
c.innovation##c.innovation = -ve and significant @ 5%
3) Should I be concerned with first-order autoregressive error terms to account for the repeated measurement of financial performance (my dependent variable) from the same firms. If so, how would I incorporate this into my models? I am wondering about GLS models (e.g., xtgls) but am not familiar with these.
Thank you for any guidance and help you can provide!
Sincerely,
Roger
I am trying to navigate my way through nonlinear models using panel data. I am familiar with nonlinear models without panel data, but using these models with panel data is a new space for me. I am also familiar with xtreg and curvefit. My main goal right now is to find the model that best fits my data (e.g., quadratic, power, growth, etc.); i.e., to understand the relationship between my variables of interest. Here is my design and what I have done:
My data: annual data over 10 years, 500 companies
Dependent variable: Company financial performance
Main independent (predictor) variable of interest: Company innovation
Theoretically, I suspect an inverted U-shape relationship between performance and innovation; i.e., very little or very high innovation is associated with lower levels of company financial performance than moderate levels of innovation. Now I want to test this (or see if there is a different nonlinear relationship between financial performance and innovation).
I have plotted the data on a twoway plot to observe the structure. It looks like an inverted U or an inverted growth (exponential) relationship, where increasing innovation at very low levels of innovation drastically increases financial performance but then this relationship tapers off (flattens out). So a few issues/question:
1) I have used curvefit but I don't think it works with panel data. Maybe I am wrong? I also don't think the twoway scatter accounts for the panel structure of the data.
2) I don't know if I can use nonlinear models with fixed effects. Does this make sense? So my code for a quadratic relationship (exploring an inverted U) looks like this:
xtreg financial_performance c.innovation##c.innovation control_variables i.yr, fe
I found the output is significant and plotted the output using marginsplot and all points towards an inverted U, but I want to make sure I'm not missing something in what I am doing.
c.innovation = +ve and significant @ 1%
c.innovation##c.innovation = -ve and significant @ 5%
3) Should I be concerned with first-order autoregressive error terms to account for the repeated measurement of financial performance (my dependent variable) from the same firms. If so, how would I incorporate this into my models? I am wondering about GLS models (e.g., xtgls) but am not familiar with these.
Thank you for any guidance and help you can provide!
Sincerely,
Roger
Comment