# UPDATED: Very unstable model and mostly insignificant variables in fixed effects regression model

I have been trying to create a regression model for my master thesis for the last week and I am stuck with the following issue. Currently seeking any help I can get, so greatful for any input you might have. :)

My goal is to find the effect of the EU emissions allowances price on the free cashflow to firm with several firm and macro economic control variables. The data set I am using for this covers around 500 firms and data from 2005 till 2022.

I have set up in R a one-way fixed effects model (within) and have tried to estimate the model with that. It leads to the following results, which are ok, but in my opinion do not make sense, as I am sure that some variables should have a significant effect such as GDP growth.

Is there anything that I could do/look into, to see if there is an error in the way I estimate the model? Do I need to transform the variables in a certain way (have tried some things like log, standardizing, differencing)?

Thanks a lot for your help! Greatly appreciated.

R Output:

Oneway (individual) effect Within Model

Call: plm(formula = FCFF ~ GDP_Growth + INTANGIBLE_ASSETS + REVENUE + DEPRECIATION + TOTAL_ASSETS + Patents_Filed + Exchange_Rate_EUR.CNY + Inflation + Oil_Price + Lead_Spot + EU_ETS_Future + EU_ETS_Spot, data = data, model = "within")

Balanced Panel: n = 511, T = 18, N = 9198

Residuals: Min. 1st Qu. Median 3rd Qu. Max. -5609351.5 -5784.3 -676.8 3676.6 7938700.2

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Total Sum of Squares: 3.5016e+14

Residual Sum of Squares: 2.8466e+14

R-Squared: 0.18705

F-statistic: 166.339 on 12 and 8675 DF

p-value: < 2.22e-16

EDIT/UPDATE:

Not sure if I need to make a new question or just edit the post, so just trying to edit first.

I have made some changes to the model, including using a new, better dataset and standardizing the variables. I have also changed the variables within the boundaries not creating colinearity among slightly. This has lead to the following updated results. However, still the R squared is surprisingly low. Also the p values seem a bit too good to be true.

Not sure if I can use such results now.

Updated Data Output:

And thank you all for helping me on this one. :)

• Greetings and welcome to CV! Please format your code so that people can read it :) Commented May 27 at 12:39
• Have you considered collinearity? Variance Inflation Factors and other regression diagnostics? Commented May 27 at 12:52
• Yes, I have excluded due to collinearity several variables, leading to the current selection. Commented May 28 at 12:27
• Do variable clustering on the predictors e.g. Hmisc::varclus(…) and plot the dendrogram. Commented May 29 at 11:29
• Sure, I have done that: ibb.co/BjRTV6X However, it basically just splits that into micro and macro variables. Will try to choose a cut-off point for it and then see if it helpts the model. Thanks. Related question: Is standardized data a problem for clustering the predictors? Commented May 29 at 13:29