Journal Impact Factor and Federal Reserve Monetary Policy: An Econometric Analysis Based on 1975-2026
期刊影响因子与美联储货币政策:基于1975—2026年的计量分析AI 解读 ↓
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期刊影响因子与美联储货币政策:基于1975—2026年的计量分析AI 解读 ↓
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The Journal Impact Factor (IF), as a core indicator of academic evaluation, has not been systematically studied in relation to its historical evolution and global macroeconomic environment. This paper employs a period-based regression analysis using long-term time series data from 1975-2026 to examine the statistical relationship between IF and Federal Reserve monetary policy (using real interest rate as a proxy variable). The study estimates three nested models using Ordinary Least Squares (OLS): (1) a baseline linear model, (2) a linear model controlling for time trends, and (3) a log-transformed model. Empirical results show that: (i) in the early period (1975-2000), there is no significant statistical relationship between IF and real interest rate ($p>0.1$); (ii) during the quantitative easing period (2001-2020), they exhibit a significant negative correlation ($β=-0.069$, $p<0.01$), meaning that for every 1 percentage point decrease in real interest rate, IF increases by approximately 6.9\%; (iii) the adjusted $R^2$ of the full-sample model reaches 0.893, indicating that real interest rate and time trends can explain 89.3\% of IF variation. This finding reveals the indirect impact of monetary policy on the academic publishing system through multiple channels such as research funding and journal pricing power, providing econometric evidence for understanding the phenomenon of "financialization of academic capital." This study not only enriches the literature on monetary policy transmission mechanisms but also provides a new perspective for valuation analysis of the academic publishing industry.
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短程与长程相依下在线期望分位数估计的极限理论
Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theory for their online estimation from dependent data remains limited. We study a one-pass, stochastic-gradient algorithm with constant learning rate $γ$ for estimating the marginal expectile of a stationary causal process. The recursion admits a unique stationary causal solution and geometrically forgets its initialization. Under…