Relevant bibliographies by topics / Panel data regression performance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Panel data regression performance'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Panel data regression performance"

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Setya, Muhammad Setya Pratama, Riki Afriansyah, and Pidaryani. "Indonesian Banking Financial Performance on Profitability Using Panel Data Regression." Agregat: Jurnal Ekonomi dan Bisnis 8, no. 2 (2024): 131–45. http://dx.doi.org/10.22236/agregat_vol8.i2/16339.

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This study's goal is to determine if the return on assets (ROA) variable posted on the Indonesia Stock Exchange, together with the loan-to-deposit ratio (LDR), capital adequacy ratio (CAR), and non-performing loans (NPL), are correlated with profitability. The impact of return on assets (ROA) factors on the banking sector is investigated in this study. The research used a data sample of 245 from 49 financial institutions that have continuously fulfilled the necessary data requirements for a minimum of five years. Panel data, testing of panel data, testing of classical assumptions, and multiple
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Agus Astapa, I. Gede, Gede Suwardika, and I. Ketut Putu Suniantara. "ANALISIS DATA PANEL PADA KINERJA REKSADANA SAHAM." Jurnal VARIAN 1, no. 2 (2018): 59–69. http://dx.doi.org/10.30812/varian.v1i2.72.

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Mutual funds is another investment opportunity with a more measurable risk as well as return high enough with enough capital is affordable for the community. Mutual fund performance can be measured by several indicators.. Modeling the performance of mutual funds modeled by regression of the data panel. The regression model estimation data panel will do with the three approaches, namely the approach of common effect, fixed effects and random effects. This research purpose to know the performance of mutual funds from stock selection skill variable influences, market timing ability and level of r
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Wang, Chunling, and Xiaoyan Lin. "Bayesian Semiparametric Regression Analysis of Multivariate Panel Count Data." Stats 5, no. 2 (2022): 477–93. http://dx.doi.org/10.3390/stats5020028.

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Panel count data often occur in a long-term recurrent event study, where the exact occurrence time of the recurrent events is unknown, but only the occurrence count between any two adjacent observation time points is recorded. Most traditional methods only handle panel count data for a single type of event. In this paper, we propose a Bayesian semiparameteric approach to analyze panel count data for multiple types of events. For each type of recurrent event, the proportional mean model is adopted to model the mean count of the event, where its baseline mean function is approximated by monotone
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Castro, José Raúl, Tuesman Castillo, and Cesar Saravia. "Active Modeling of Solar Panel Performance using K-Means and Multiple Linear Regression with Environmental Variables and Historical Data." IOP Conference Series: Earth and Environmental Science 1434, no. 1 (2024): 012010. https://doi.org/10.1088/1755-1315/1434/1/012010.

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Abstract The present study focuses on the relationship between environmental variables and solar panel performance, using clustering techniques and regression analysis. The K-Means algorithm was implemented to cluster data according to environmental conditions, and multiple linear regression was applied to mathematically model the performance of the panels. This approach allows for the identification of patterns in solar energy production considering solar irradiation and temperature on the solar panels under various conditions. The photovoltaic system of the Universidad Técnica Particular de
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Purwaningsih, S. S., Anny Suryani, and Adolf Taylor. "PENGARUH INFLASI, SUKU BUNGA, DAN INDEKS HARGA SAHAM GABUNGAN (IHSG) TERHADAP KINERJA REKSADANA DENGAN PERMODELAN REGRESI DATA PANEL." Sigma-Mu 7, no. 2 (2017): 1–16. http://dx.doi.org/10.35313/sigmamu.v7i2.128.

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Correlation analysis is used to observe the effect of independent variables on the dependent variable, while the regression analysis is used to determine the shape of the relationship between the variables, and also for predicting or forecasting. The correlation meant in this study is the correlation between inflation, interest rates, the Composite Stock Price Index (CSPI) on the performance of mutual fund products; while the regression model meant in this case is the regression model between inflation, interest rates and stock index mutual fund product performance of some companies Investment
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Hui, Xinye. "Variable Selection for Panel Data Linear Regression Models with Fixed Effects." International Journal of Social Sciences and Public Administration 2, no. 2 (2024): 98–107. http://dx.doi.org/10.62051/ijsspa.v2n2.17.

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This paper introduces a robust variable selection mechanism for fixed effect panel data models by integrating compound quantile regression with the adjusted MIXED penalty method. Initially, forward orthogonal deviation transformation is employed to eliminate the influence of fixed effects. Subsequently, the MIXED penalty is utilized to construct a penalized compound quantile regression objective function, facilitating simultaneous estimation of regression coefficients and variable selection. This method not only effectively eliminates the interference of fixed effects but also demonstrates out
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Georgescu, Irina, Ionuț Nica, Camelia Delcea, Nora Chiriță, and Ștefan Ionescu. "Assessing Regional Economic Performance in Romania Through Panel ARDL and Panel Quantile Regression Models." Sustainability 16, no. 21 (2024): 9287. http://dx.doi.org/10.3390/su16219287.

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This study aims to address the persistent regional economic disparities in Romania by evaluating economic performance through Panel Autoregressive Distributed Lag (pARDL) and panel quantile regression (PQR) models. The analysis focuses on the impact of key economic variables, including research and development expenditures (CTCRD), IT infrastructures (IT), the number of universities (FCL), and the average number of employees (NMSP), on regional gross domestic product (GDPR). Using data from the Romanian National Institute of Statistics for the period 2003–2022, this research seeks to understan
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Wahile, Ganesh S., Srikant Londhe, Shivshankar Trikal, et al. "Performance analysis of photovoltaic panel using machine learning method." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 19–30. https://doi.org/10.11591/ijeecs.v34.i1.pp19-30.

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Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel t
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Wahile, Ganesh S., Srikant Londhe, Shivshankar Trikal, et al. "Performance analysis of photovoltaic panel using machine learning method." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 19. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp19-30.

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Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures,
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Lmakri, Aziz, Abdelhadi Akharif, and Amal Mellouk. "Optimal Detection of Bilinear Dependence in Short Panels of Regression Data." Revista Colombiana de Estadística 43, no. 2 (2020): 143–71. http://dx.doi.org/10.15446/rce.v43n2.83044.

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In this paper, we propose parametric and nonparametric locally andasymptotically optimal tests for regression models with superdiagonal bilinear time series errors in short panel data (large n, small T). We establish a local asymptotic normality property– with respect to intercept μ, regression coefficient β, the scale parameter σ of the error, and the parameter b of panel superdiagonal bilinear model (which is the parameter of interest)– for a given density f1 of the error terms. Rank-based versions of optimal parametric tests are provided. This result, which allows, by Hájek’s representation
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Dissertations / Theses on the topic "Panel data regression performance"

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Ishmael, Shu Aghanifor. "Performance determinants For Swedish Financial Monetary Institutions: Panel Data evidence between 2010-2014." Thesis, Högskolan Dalarna, Företagsekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:du-23506.

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Due to the rapid changes that governs the Swedish financial sector such as financial deregulations and technological innovations, it is imperative to examine the extent to which the Swedish Financial institutions had performed amid these changes. For this to be accomplish, the work investigates what are the determinants of performance for Swedish Financial Monetary Institutions? Assumptions were derived from theoretical and empirical literatures to investigate the authenticity of this research question using seven explanatory variables. Two models were specified using Returns on Asset (ROA) an
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Spinetti, Rodrigo Noel Saldías. "Análise da relação conduta-desempenho das grandes empresas agrícolas na expansão da produção de grãos no Uruguai." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/29546.

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A área de cultivos de sequeiro no Uruguai quadriplicou-se na ultima década como consequência dos aumentos nos preços das commodities, do crescimento da soja e da rápida consolidação da lógica empresarial na produção de grãos. A conduta das principais empresas agrícolas mudou a realidade do setor, aumentando a concentração da terra e deslocando outras atividades. Novas regiões começaram a ser utilizadas para a agricultura e o incremento da demanda pelos arrendamentos da terra elevou os preços e dificultou o acesso a este recurso. Utilizando como referencial teórico o paradigma Estrutura-Conduta
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Kamangar, Daniel, and Richard Sundin. "Management and CEO Stock Ownership and its Effect on Company Performance." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229670.

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This is a study on the effect of management and CEO stock ownership on company performance. A regression analysis is performed on panel data consisting of a sample of 30 companies listed on OMX Stockholm Mid Cap. A total of 210 and 2520 observations is considered on a yearly and monthly basis, respectively, for seven years (2010-2016). The Hausman test is applied for determining between the fixed effects and random effects regression models. Results show that management relative stock ownership has a significant positive effect on company net income growth and return on assets. The effect is n
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Calife, Flávio Estévez. "Determinantes do desempenho financeiro dos municípios paulistas." reponame:Repositório Institucional do FGV, 2006. http://hdl.handle.net/10438/2487.

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Made available in DSpace on 2010-04-20T20:49:50Z (GMT). No. of bitstreams: 3 68458.pdf.jpg: 16519 bytes, checksum: f33c6e63a4f0f3cd3e59e7c3c9dce9a1 (MD5) 68458.pdf: 425785 bytes, checksum: e1e76e055a35adf8373e6fc33bad90fa (MD5) 68458.pdf.txt: 201916 bytes, checksum: 01083a5de32d1dcb5b80b759eb5484b6 (MD5) Previous issue date: 2006-08-14T00:00:00Z<br>The aim of this research is to evaluate the deteminants of municipal fiscal performance in the state of São Paulo and its influence in the municipal creditworthiness during the years of 1997 and 2003. For this work we used panel data methods a
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He, Xin. "Semiparametric analysis of panel count data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4774.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 27, 2007) Vita. Includes bibliographical references.
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Cancado, Luciana Pacheco. "Economic growth panel data evidence from Latin America /." Ohio : Ohio University, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1127143858.

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Adnan, Arisman. "Analysis of taste-panel data using ANOVA and ordinal logistic regression." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402150.

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Fernández-Val, Iván. "Three essays on nonlinear panel data models and quantile regression analysis." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32408.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2005.<br>Includes bibliographical references.<br>This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in
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Meneghel, Danilevicz Ian. "Robust linear mixed models, alternative methods to quantile regression for panel data, and adaptive LASSO quantile regression with fixed effects." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST176.

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La thèse est constituée de trois chapitres. Le premier s'intéresse au lien entre l’exposition à la pollution de l’air et les affections respiratoires chez les enfants et les adolescents. La cohorte comprend 82 individus observés mensuellement pendant 6 mois. Nous proposons un modèle linéaire mixte robuste combiné à une analyse en composantes principales afin de gérer la multicolinéarité entre les covariables et l’impact des observations extrêmes sur les estimations. Le deuxième chapitre analyse des données de panel au moyen de modèles à effets fixes et utilisant différentes fonction de perte.
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Osipenko, Denys. "Investigation into methods of predicting income from credit card holders using panel data." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33178.

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A credit card as a banking product has a dual nature both as a convenient loan and a payment tool. Credit card profitability prediction is a complex problem because of the variety of the card holders' behaviour patterns, a fluctuating balance, and different sources of interest and transactional income. The state of a credit card account depends on the type of card usage and payments delinquency, and can be defined as inactive, transactor, revolver, delinquent, and default. The proposed credit cards profit prediction model consists of four stages: i) utilisation rate and interest rate income pr
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Books on the topic "Panel data regression performance"

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Singla, Dr. How to Do Panel Data Regression Analysis. SAGE Publications Inc., 2023. http://dx.doi.org/10.4135/9781529669268.

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Stock, James H. Heteroskedasticity-robust standard errors for fixed effects panel data regression. National Bureau of Economic Research, 2006.

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Narver, John C. Market orientation and business performance: An analysis of panel data. Marketing Science Institute, 1993.

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Narver, John C. Market orientation and business performance: An analysis of panel data. Marketing Science Institute, 1993.

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Munemo, Jonathan. Foreign aid and export performance: A panel data analysis of developing countries. Federal Reserve Bank of St. Louis, 2007.

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Urga, Giovanni. Panel data vs. time series regression analysis: An aggregation issue : a comparison using labour demand functions. London University, Queen Mary and Westfield College, Department of Economics, 1993.

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Amess, Kevin. Corporate restructuring, management buyouts and firm-level performance: Evidence from UK panel data. Loughborough University of Technology, Department of Economics, 1997.

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Jia, Jing, Michael Bradbury, and Zhongtian Li. Using Seemingly Unrelated Panel Regression to Analyze the Impact of Risk Management Committees on Firm Performance. SAGE Publications Inc., 2023. http://dx.doi.org/10.4135/9781529668384.

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Bhatia, Madhur. Learn about a Static Panel Regression Model in STATA Using Data on the Profitability of Indian Banks (2005-2023). SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036209384.

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Reid, Abigail-Kate, and Nick Allum. Learn About Simple Regression in Stata With Data From the Consolidated State Performance Report (2012–2013). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526470577.

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Book chapters on the topic "Panel data regression performance"

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Sotiropoulos, Marios, Michalis Skordoulis, Petros Kalantonis, and Aristidis Papagrigoriou. "The Impact of Board Diversity on Firms’ Performance: The Case of Retail Industry in Europe." In Strategic Innovative Marketing and Tourism. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51038-0_85.

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AbstractThe aim of this paper is to examine the relationship between board diversity and firms’ performance. The board of directors is the highest decision-making body responsible for governing an organization on behalf of its shareholders. It performs key functions like strategy formulation, policy development, monitoring, supervising, providing leadership, and accountability. An optimal board composition is crucial for effective corporate governance and is found to have a significant influence on firms’ performance. At the same time, board diversity is reported to have a significant impact o
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Kahane, Leo H. "Time Series and Panel Data." In Regression Basics, 3rd ed. Routledge, 2024. http://dx.doi.org/10.4324/9781003349174-7.

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Gujarati, Damodar. "Panel Data Regression Models." In Econometrics. Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-37502-5_17.

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Wang, Yu. "A Moderated Mediation Model of Slack Resources and Innovation Performance: Based on the Hierarchical Multiple Regression of Panal data." In Atlantis Highlights in Engineering. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-262-0_43.

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Liu, Timina, Shuangzhe Liu, and Lei Shi. "Regression Analysis of Panel Data." In Time Series Analysis Using SAS Enterprise Guide. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0321-4_6.

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Benfratello, Luigi. "Random Effects Regression for Panel Data." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2402.

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Benfratello, Luigi. "Random Effects Regression for Panel Data." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_2402.

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Sun, Jianguo, and Xingqiu Zhao. "Regression Analysis of Panel Count Data I." In Statistics for Biology and Health. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8715-9_5.

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Sun, Jianguo, and Xingqiu Zhao. "Regression Analysis of Panel Count Data II." In Statistics for Biology and Health. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8715-9_6.

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Pulcini, Antonio, and Brunero Liseo. "Approximate Bayesian Quantile Regression for Panel Data." In Contributions to Statistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11149-0_12.

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Conference papers on the topic "Panel data regression performance"

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Buchheit, R. G., M. Cunningham, H. Jensen, M. W. Kendig, and M. A. Martinez. "Rapid Electrochemical Corrosion Testing of Chemically Passivated Aluminum Alloys." In CORROSION 1998. NACE International, 1998. https://doi.org/10.5006/c1998-98740.

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Abstract The corrosion resistance of 33 different conversion coatings applied to five different aluminum alloys was tested by salt spray exposure and electrochemical impedance spectroscopy (EIS). Results derived from the two tests were evaluated to determine if a relationship existed. Individual salt spray test panels, with an area of 30 in.2, were visually inspected at regular intervals up to 168 hours of exposure. At each inspection interval, panels were assigned a pass rank if less than 5 pits were observed, or fail rank if more than 5 pits were observed. EIS data were analyzed using a simp
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Winarso, Raihan Adam Handoyo, Riyanarto Sarno, Agus Tri Haryono, Abdullah Faqih Septiyanto, Shoffi Izza Sabilla, and Yusril Falih Izzaddien. "Stock Price Index Prediction of Several ASEAN Countries Using Panel Data Regression and Machine Learning Algorithm." In 2024 Beyond Technology Summit on Informatics International Conference (BTS-I2C). IEEE, 2024. https://doi.org/10.1109/bts-i2c63534.2024.10942143.

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Kane, Alissa J., and Yuchou Chang. "Improving Regression in Spiking Neural Networks for Oceanographic Data Analysis." In 2024 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2024. https://doi.org/10.1109/hpec62836.2024.10938484.

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Yi, Xiaoquan, Haozhao Wang, Zhenlong Zhu, Wei Liu, Wenchao Xu, and Ruixuan Li. "AWSSS: Adaptive Weighted Statistical Space Smoothing for Regression with Imbalance Data." In 2024 IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2024. https://doi.org/10.1109/hpcc64274.2024.00050.

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Li, Yanfang. "Predicting Student English Academic Performance by using Data-Driven Multiple Regression Analysis." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070381.

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Liu, Mengqi, and Xueqiao Li. "Data Balancing in SAT-NET: Effects on Logistic Regression and Random Forest Performance." In 2024 International Conference on Satellite Internet (SAT-NET). IEEE, 2024. https://doi.org/10.1109/sat-net62854.2024.00011.

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Bai, Yifan. "The Impact of Data Distribution and Data Noise on the Fitting and Prediction Performance of Regression Models." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00164.

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Simbolon, Jeremy Syaloom Okey Nathanael, and Windy Gambetta. "On Performance Comparison between Strong Machine Unlearning Algorithms for Logistic Regression Credit Assessment Models." In 2024 IEEE International Conference on Data and Software Engineering (ICoDSE). IEEE, 2024. https://doi.org/10.1109/icodse63307.2024.10829916.

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Jafari, Fereshteh, Kamran Moradi, Qobad Shafiee, and Fariba Moghaddam. "Data-Driven Performance Modeling of Solar Panels Using Polynomial Regression." In 2024 International Conference on Control, Automation and Diagnosis (ICCAD). IEEE, 2024. http://dx.doi.org/10.1109/iccad60883.2024.10554048.

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Saxena, Priti, Abhineet Saxena, and Garima Sharma. "Is CSR important to improve financial performance of listed companies: Comparative analysis of industries using panel data regression model." In ADVANCES IN INTELLIGENT APPLICATIONS AND INNOVATIVE APPROACH. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0148992.

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Reports on the topic "Panel data regression performance"

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Jimenez Mori, Raul Alberto, and Jorge Enrique Mercado Díaz. Energy Intensity: A Decomposition and Counterfactual Exercise for Latin American Countries. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0011485.

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This paper investigates trends in energy intensity in Latin American countries over the last 40 years. It applies the Fisher Ideal Index to decompose the energy intensity into the relative contributions of energy efficiency and the activity mix, and then analyzes the determinants of these energy indexes through panel data regression techniques. Finally, the paper compares the performance of Latin American countries to that of a similar set of countries chosen through the synthetic control method. The authors find that the energy intensity in Latin American countries has decreased about 20 perc
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Graham, Bryan, Jinyong Hahn, Alexandre Poirier, and James Powell. Quantile Regression with Panel Data. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21034.

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Powell, James L., Alexandre Poirier, Bryan S. Graham, and Jinyong Hahn. Quantile regression with panel data. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.1215.

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Ferson, Wayne, and Junbo Wang. A Panel Regression Approach to Holdings-based Fund Performance Measures. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w28238.

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Stock, James, and Mark Watson. Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/t0323.

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Mukhamadeeva, O. R., S. A. Gorbatkov, S. A. Farkhieva, and N. H. Sharafutdinova. Algorithm for constructing a nonlinear dynamic predictive regression model with panel data. OFERNIO, 2020. http://dx.doi.org/10.12731/ofernio.2020.24646.

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Wooldridge, Jeffrey. Difference-in-Differences (DiD) Methods with Panel Data. Instats Inc., 2022. http://dx.doi.org/10.61700/shzpb9gqg10ye469.

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Abstract:
This workshop covers recent developments in difference-in-differences (DiD) estimation for causal inference with longitudinal panel data, covering the foundational theory for DiD approaches along with flexible regression-based methods and staggered interventions. More recent methods based on rolling controls, long differencing, and more will also will be covered. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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Wooldridge, Jeffrey. Difference-in-Differences (DiD) Methods with Panel Data. Instats Inc., 2023. http://dx.doi.org/10.61700/ysyoi6wkdcgbz469.

Full text
Abstract:
This workshop covers recent developments in difference-in-differences (DiD) estimation for causal inference with longitudinal panel data, covering the foundational theory for DiD approaches along with flexible regression-based methods and staggered interventions. More recent methods based on rolling controls, long differencing, and more will also will be covered. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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9

Rosen, Richard A. Can Panel Data Methodologies Determine the Impact of Climate Change on Economic Growth? Institute for New Economic Thinking Working Paper Series, 2021. http://dx.doi.org/10.36687/inetwp171.

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Several major papers have been published over the last ten years claiming to have detected the impact of either annual variations in weather or climate change on the GDPs of most countries in the world using panel data-based statistical methodologies. These papers rely on various multivariate regression equations which include the annual average temperatures for most countries in the world as one or more of the independent variables, where the usual dependent variable is the change in annual GDP for each country from one year to the next year over 30-50 year time periods. Unfortunately, the qu
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Bandyopadhyay, Subhayu, Arabinda Basistha, and Jonathan Munemo. Foreign Aid and Export Performance: A Panel Data Analysis of Developing Countries. Federal Reserve Bank of St. Louis, 2007. http://dx.doi.org/10.20955/wp.2007.023.

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