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Nonparametric Estimation of Smooth Coefficients in Fixed-Effect Panel Data Models
No full textWe propose a kernel-based nonparametric estimator for a smooth coefficient panel data model with fixed effects. Without requiring a zero sum of fixed effects, we propose an estimator that is easy to construct and computationally efficient. Eliminating the fixed effects through a local within transformation, we perform a local linear estimation for the coefficient functions associated with time varying variables and associated derivatives. We further estimate the intercept coefficient function, if present, through a difference of kernel weighted averages. We characterize the estimator’s asymptotic properties under a large-n and large-T framework. We demonstrate that the estimator is not asymptotically equivalent to the standard kernel estimator that ignores fixed effects. Through extensive simulation studies, we highlight the estimator’s encouraging numerical performance and computational advantages over existing kernel estimators in the literature. We showcase the empirical applicability by estimating a smooth coefficient model for the Environmental Kuznets Curve through a panel of OECD countries
When prices spike: Identifying excessive volatility in fertilizer markets
No full textSharp and volatile fertilizer price movements can hinder adoption and reduce agricultural productivity, especially among vulnerable smallholders. Using a nonparametric location-scale approach to model price returns, we quantify the conditional value-at-risk (CVaR) - the high return threshold exceeded with low probability - to identify excessive price spikes in potash, urea, and di-ammonium phosphate (DAP) markets. We use the bias-corrected estimator from Martins-Filho et al. (2018) and propose a simpler estimator based on Hill (1975). Backtesting results indicate superior performance of the Hill-based estimator, supporting its value as a convenient method for detecting unusual fertilizer price surges amid recurring global volatility
Estimating Corporate Investment Efficiency with Bias Correction: A Semiparametric Panel Model Approach
No full textEmpirical studies often use the residuals from ordinary least squares regression models to represent certain discretionary or unexpected components and then regress these residuals on potential determinants. However, this two-step approach has been criticized for leading to biased estimates, invalid inferences, and unreliable empirical results. This paper shows that the shortcomings of the two-step approach and alternative existing methodologies are retained and even more pronounced when analyzing inefficient corporate investment. To address these shortcomings, we propose a novel semiparametric model tailored for investment efficiency analysis. Our model effectively mitigates estimation bias caused by inappropriate model design or misspecified model structure, and accurately discerns over-investment, under-investment, and efficient investment along with their respective probabilities. Applying our model to a sample of Chinese listed firms reveals significant, previously obscured nonlinear impacts of Tobin’s q and sales on investment. Our results reveal pronounced tendencies towards over-investment, contradictory to existing models which reveal opposite tendencies towards under-investment. Our model is applicable to various types of efficiency analysis, where each firm may exhibit different performance outcomes with associated probabilities
Rebuttal to Winn et al. (2024) ’s “Redefining searching in non-medical sciences systematic reviews”
No full textLetter to the Editor and Rebuttal to Winn et al. (2024)
To the Editor and Readership of the Journal of Librarianship and Information Science:
We are writing to express concerns regarding the article, Redefining searching in non-medical sciences systematic reviews: The ascendance of Google Scholar as the primary database (https://doi.org/10.1177/09610006241256393). Our concerns are numerous, and include issues involving the research questions, data availability and validity, representations of evidence synthesis methods, and the disregard for established scholarship which refutes the reliability, transparency and reproducibility of Google Scholar searches for evidence synthesis reviews
