Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background
2.1 Rollup
2.2 EIP-4844
2.3 VAR(Vector Autoregression)


Data
3.1 Consensus security data
3.2 Ethereum usage data
3.3 Rollup Transactions Data
3.4 Blob gas fee data


Empirical Results
4.1 Consensus security
4.2 Ethereum usage
4.3 Rollup transactions
4.4 Blob gas fee market


Conclusion and References Background
2.1 Rollup
2.2 EIP-4844
2.3 VAR(Vector Autoregression) Background 2.1 Rollup 2.1 Rollup 2.2 EIP-4844 2.2 EIP-4844 2.3 VAR(Vector Autoregression) 2.3 VAR(Vector Autoregression) Data
3.1 Consensus security data
3.2 Ethereum usage data
3.3 Rollup Transactions Data
3.4 Blob gas fee data Data 3.1 Consensus security data 3.1 Consensus security data 3.2 Ethereum usage data 3.2 Ethereum usage data 3.3 Rollup Transactions Data 3.3 Rollup Transactions Data 3.4 Blob gas fee data 3.4 Blob gas fee data Empirical Results
4.1 Consensus security
4.2 Ethereum usage
4.3 Rollup transactions
4.4 Blob gas fee market Empirical Results 4.1 Consensus security 4.1 Consensus security 4.2 Ethereum usage 4.2 Ethereum usage 4.3 Rollup transactions 4.3 Rollup transactions 4.4 Blob gas fee market 4.4 Blob gas fee market Conclusion and References Conclusion and References Conclusion and References A. Consensus Security Data A. Consensus Security Data B. Rollup Data Collection B. Rollup Data Collection C. Detailed Var Model Results for Blob Gas Base Fee and Gas Fee C. Detailed Var Model Results for Blob Gas Base Fee and Gas Fee D. Detailed Var Model Results for Blob Gas Base Fee and Blob Gas Priority Fee D. Detailed Var Model Results for Blob Gas Base Fee and Blob Gas Priority Fee E. Rollup Transaction Dynamics E. Rollup Transaction Dynamics C DETAILED VAR MODEL RESULTS FOR BLOB GAS BASE FEE AND GAS BASE FEE This section presents the detailed results from the VAR model analysis conducted on the blob gas base fee and the gas base fee. The analysis includes various statistical tests and model estimations to assess the dynamics and relation between these two key metrics within the network’s pricing mechanism. C.1 ADF test results Table 11 displays the results of the ADF test, used to check the stationarity of the time series data for both the gas base fee and the blob gas base fee. Result confirmed that the Base Fee and Blob Gas Base Fee time series are stationary. The test statistics of -6.3719 and -10.5237 respectively, along with very low p-values. This indicates that the data are suitable for further econometric modeling, as they do not depend on time. C.2 VAR model estimation output Detailed results for the VAR model estimation are provided below, showing the full regression output for both the gas base fee and blob gas base fee equations. Detailed results for the VAR model estimation are provided below, showing the full regression output for both the gas base fee and blob gas base fee equations. Table 12 summarizes the overall model diagnostics including the number of equations, observations, log likelihood, and several information criteria that help in assessing the model fit. Table 16 presents the estimated coefficients and associated statistics for each variable within the equations, detailing the individual impacts in the model. Table 12 summarizes key metrics from the VAR model regression results. The table captures essential information such as the number of equations modeled, total observations considered, and various statistical measures including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), and Final Prediction Error (FPE). These metrics provide insights into the model’s performance and its predictive accuracy. C.3 Correlation matrix of residuals Table 13 presents the correlation matrix of residuals for the gas base fee and blob gas base fee. The near-zero correlation coefficients between the residuals of different equations suggest that the residuals are uncorrelated. Table 16 provides detailed VAR model estimates for the gas base fee and blob gas base fee. For the gas base fee, all lagged values are significant predictors, with particularly strong influence from L1, evidenced by a high t-statistic of 155.252 and a p-value less than 0.001. Authors:
(1) Seongwan Park, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (sucre87@snu.ac.kr);
(2) Bosul Mun, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (bsbs8645@snu.ac.kr);
(3) Seungyun Lee, Seoul National University, Seoul, Repulic of Korea;
(4) Woojin Jeong, Seoul National University, Seoul, Repulic of Korea;
(5) Jaewook Lee, Seoul National University, Seoul, Repulic of Korea;
(6) Hyeonsang Eom, Seoul National University, Seoul, Repulic of Korea;
(7) Huisu Jang (Corresponding author), Soongsil University, Seoul, Republic of Korea. Authors: Authors: (1) Seongwan Park, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (sucre87@snu.ac.kr); (2) Bosul Mun, this author contributed equally to the paper from Seoul National University, Seoul, Republic of Korea (bsbs8645@snu.ac.kr); (3) Seungyun Lee, Seoul National University, Seoul, Repulic of Korea; (4) Woojin Jeong, Seoul National University, Seoul, Repulic of Korea; (5) Jaewook Lee, Seoul National University, Seoul, Repulic of Korea; (6) Hyeonsang Eom, Seoul National University, Seoul, Repulic of Korea; (7) Huisu Jang (Corresponding author), Soongsil University, Seoul, Republic of Korea. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. available on arxiv available on arxiv

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Stationarity and Correlation Insights from VAR Modeling of Gas Base Fees

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