Relevant bibliographies by topics / Panel data regressions

Contents

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

Academic literature on the topic 'Panel data regressions'

Author: Grafiati
Published: 11 September 2021
Last updated: 30 July 2025

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Journal articles on the topic "Panel data regressions"

1

Hjalmarsson, Erik. "Predictive Regressions with Panel Data." International Finance Discussion Paper 2006, no. 869 (2006): 1–39. http://dx.doi.org/10.17016/ifdp.2006.869.

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Arellano, Manuel, and Stéphane Bonhomme. "Nonlinear panel data estimation via quantile regressions." Econometrics Journal 19, no. 3 (2016): C61—C94. http://dx.doi.org/10.1111/ectj.12062.

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Cheng, Jing, and Mingyao Ai. "Optimal designs for panel data linear regressions." Statistics & Probability Letters 163 (August 2020): 108769. http://dx.doi.org/10.1016/j.spl.2020.108769.

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Moon, Hyungsik Roger, and Benoit Perron. "PETER C.B. PHILLIPS’S CONTRIBUTIONS TO PANEL DATA METHODS." Econometric Theory 30, no. 4 (2014): 882–93. http://dx.doi.org/10.1017/s0266466613000509.

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This paper discusses Peter Phillips’s contributions to panel data methods. These include contributions in the areas of seemingly unrelated regressions, nonstationary panel data, dynamic panels, and the development of multiple index asymptotic theory. We also discuss his empirical contributions in the area of economic growth and convergence that use macro panel data.
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Baltagi, Badi H., and Ping X. Wu. "UNEQUALLY SPACED PANEL DATA REGRESSIONS WITH AR(1) DISTURBANCES." Econometric Theory 15, no. 6 (1999): 814–23. http://dx.doi.org/10.1017/s0266466699156020.

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This paper deals with the estimation of unequally spaced panel data regression models with AR(1) remainder disturbances. A feasible generalized least squares (GLS) procedure is proposed as a weighted least squares that can handle a wide range of unequally spaced panel data patterns. This procedure is simple to compute and provides natural estimates of the serial correlation and variance components parameters. The paper also provides a locally best invariant test for zero first-order serial correlation against positive or negative serial correlation in case of unequally spaced panel data.
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Baltagi. "Specification Tests in Panel Data Models Using Artificial Regressions." Annales d'Économie et de Statistique, no. 55/56 (1999): 277. http://dx.doi.org/10.2307/20076199.

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Belhadj, Besma. "Fuzzy multiple regressions for Cross-Section and Panel data." Socio-Economic Planning Sciences 91 (February 2024): 101761. http://dx.doi.org/10.1016/j.seps.2023.101761.

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Allison, Paul D. "Asymmetric Fixed-effects Models for Panel Data." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311982644. http://dx.doi.org/10.1177/2378023119826441.

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Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the
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Karnik, Ajit, and Mala Lalvani. "Heterogeneity in Growth Processes: Estimating Growth Regressions using Panel Data." International Economic Journal 23, no. 4 (2009): 561–90. http://dx.doi.org/10.1080/10168730903372224.

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Jochmans, Koen, and Martin Weidner. "Fixed‐Effect Regressions on Network Data." Econometrica 87, no. 5 (2019): 1543–60. http://dx.doi.org/10.3982/ecta14605.

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This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two‐way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer–employee or student–teacher panel data. We formalize how the structure of the network affects the accuracy with which the fixed effects can be estimated. This allows us to derive sufficient conditions on the network for consistent estimation and asymptotically valid inference to be possible. Estimation of moments is also considered. We
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Dissertations / Theses on the topic "Panel data regressions"

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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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M, Zein Aida, and Per Ångström. "Can macroeconomic factors explain the choice of capital structure? - A study of listed non-financial firms in Sweden." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-302858.

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This study investigates the impact macroeconomic factors have on corporate capital structure in Sweden. We use a panel data analysis of unbalanced data for the sample period, 2005-2014. While previous research has shown that key factors internal to the firm are highly correlated with leverage, such as profitability, asset tangibility and firm size, we add external factors and test for economic growth, inflation, interest rates, corporate tax rates, and exchange rates. Our models do not present any substantial explanatory power for the relationship between the macroeconomic environment and diff
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Huang, Shuang, and Shuang Huang. "Regularized Markov Model for Modeling Disease Transitioning." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625633.

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In longitudinal studies of chronic diseases, the disease states of individuals are often collected at several pre-scheduled clinical visits, but the exact states and the times of transitioning from one state to another between observations are not observed. This is commonly referred to as "panel data". Statistical challenges arise in panel data in regard to identifying predictors governing the transitions between different disease states with only the partially observed disease history. Continuous-time Markov models (CTMMs) are commonly used to analyze panel data, and allow maximum likelihood
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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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Johansson, Henrik, and Mathias Kardell. "Demografisk sammansättning samt beteende hos medlemmar i panel." Thesis, Linköping University, Linköping University, Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57244.

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<p>The use of marketing research panels are a more and more frequently used source of information for studies within many different branches. The purpose of this report is to investigate the demographic composition of panels and compare it with the population of Sweden, a possible change in behaviour of respondents, and if the source of recruitment is the cause of possible differences in study results. The study was commissioned by Norstats Linkoping office. Sources for the data material include Norstat’s recruitment process and their two main panels with different recruitment sources. To enab
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Nordin, Henrik, and Gustav Klockby. "Bestämningsfaktorer till regionala bostadspriser : En analys av de svenska länen för perioden 1993-2012." Thesis, Linköpings universitet, Nationalekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111160.

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Bostadsmarknaden är en av de största tillgångsmarknaderna i ett land varpå förändringar i bostadspriserna får långt gångna konsekvenser för det enskilda hushållet, det finansiella systemet och samhällsekonomin i stort. Flertalet tidigare studier har analyserat den svenska bostadsmarknaden utifrån ett storstadsperspektiv alternativt jämfört Sveriges bostadsmarknad mot andra länder. Vi har identifierat att studiet kring vad som bestämmer prisnivån på regionala bostadsmarknader i Sverige är tämligen oexploaterat varför avsikten med den här studien är att analysera bestämn
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Books on the topic "Panel data regressions"

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Hjalmarsson, Erik. Predictive regressions with panel data. Federal Reserve Board, 2006.

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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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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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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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Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluati
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Fund, International Monetary. Oil Rents, Corruption, and State Stability: Evidence from Panel Data Regressions. International Monetary Fund, 2009.

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Staff, International Monetary Fund. Oil Rents, Corruption, and State Stability: Evidence from Panel Data Regressions. International Monetary Fund, 2009.

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Staff, International Monetary Fund. Oil Rents, Corruption, and State Stability: Evidence from Panel Data Regressions. International Monetary Fund, 2009.

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Pevehouse, Jon, and Jason D. Brozek. Time‐Series Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0019.

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This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the articl
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Book chapters on the topic "Panel data regressions"

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Genoni, Andreas, Jean Philippe Décieux, Andreas Ette, and Nils Witte. "Setting up Probability-Based Online Panels of Migrants with a Push-to-Web Approach: Lessons Learned from the German Emigration and Remigration Panel Study (GERPS)." In IMISCOE Research Series. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67498-4_16.

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AbstractWe address two major challenges in setting up probability-based online panels of migrants, using the German Emigration and Remigration Panel Study (GERPS) as an example. The first challenge is potential spatial and social selectivity in unit response when using push-to-web recruitment. To address the first challenge, we draw on a split ballot experiment with return migrants in wave 1 of GERPS. The related analysis uses population register data and geo data. We use logistic regressions to compare unit nonresponse between a push-to-web-only control group (n = 5999) and two sub-samples (e
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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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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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Zhu, Xinyuan, Zheng Zhou, and Jinzhang Tian. "Robustness Study and Deformation Pattern Analysis of Multiple Prediction Regression Models for a Panel Rockfill Dam." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6238-5_41.

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AbstractPanel rockfill dams, due to their cost-effectiveness, short construction periods, high safety standards, and strong adaptability to foundations, have gained rapid development and considerable attention in recent years. However, the inherent material properties and structural behavior of panel rockfill dams change continuously during operation. Particularly, long-term operation results in coordination issues between the panel concrete and the rockfill material, leading to deformations. Regular monitoring and prediction of horizontal displacements in panel rockfill dams are crucial to en
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Conference papers on the topic "Panel data regressions"

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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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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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"THE ROLE OF DIGITAL SKILLS AND DIGITALISATION IN ENHANCING LABOUR PRODUCTIVITY IN THE EU." In XIII TRADITIONAL SCIENTIFIC CONFERENCE NEW ECONOMY 2025. Oikos Institute – Research Center, Bijeljina, Bosnia and Herzegovina, 2025. https://doi.org/10.61432/cpne0301033h.

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Digitalisation and digital skills are often considered key factors shaping labour productivity and international competitiveness. The paper aims to examine the potential relationship between the level of digital skills and labour productivity. It explores the interdependence between the level of digital skills and labour productivity while considering the possible effects of business digitalisation. Using the Granger causality tests and panel data regression, we get several novel results. The results indicate a bidirectional causal relationship between productivity and digital skills, albeit w
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Gerni, Cevat, Selahattin Sarı, Ömer Selçuk Emsen, and Burhan Kabadayı. "Foreign Direct Investment in Transition Economies: Is It Related to Export Expansion or Import Substitution?" In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.00968.

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It is propounded that there are two motivations behind foreign direct investments (FDI). One of them is to invest in foreign countries because of trade barrier to export. In this case foreign investors operate in import substitution industries (ISI). The second fact to invest in another country away from homeland is to get benefit from cost advantages such as cheap labor and inputs, positioning closed to developed countries. With this aspect foreign investors operate in export oriented sectors (EOS). The economic consequences were discussed lighting on study’s aim examining the FDI to Transiti
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Xu, Jiahui, Song Chen, and Taofang Yu. "Development of industrial lands and influencing factors towards new economy strategies in China." In Post-Oil City Planning for Urban Green Deals Virtual Congress. ISOCARP, 2020. http://dx.doi.org/10.47472/ytdo4390.

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Industrial lands in China deserve new investigation as China is transferring from the high- speed development mode into new normal economy stage. We measured the development of industrial lands in China in the past two decades. Using urban industrial-construction proportion and location quotient for industrial lands (LQ) as indicators, we found that: (1) Industrial lands were generally growing slower than construction lands, and the national proportion decreased to about 20%; (2) Hot spot provinces, with a LQ above 1, and hot spot capital cities, with a proportion above 20%, were concentrating
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Cole, Kevin D. "Thermal Properties of Honeycomb Panels: Analysis of Transient Experiments With One-Dimensional Models." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0725.

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Abstract This work is motivated by the need for thermal properties in order to perform thermal stress analysis of aircraft structural members. For the particular titanium-core honeycomb panels of interest, the effective conductivity was previously underpredicted by established theory compared to a steady experiment. In the present study the thermal properties panel were measured by transient heating experiments. The transient data is analyzed with a parameter estimation method, in which a non-linear regression is carried out between the experimental data and a transient finite-difference model
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Lin, Zhipeng. "ML Estimation of Spatial Panel Data Geographically Weighted Regression Model." In 2011 International Conference on Management and Service Science (MASS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmss.2011.5999234.

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Vegirawati, Titin, Didik Susetyo, Inten Meutia, and Lukluk Fuadah. "Panel Data Regression Analysis of Partnership Contract in Indonesian Sharia Banks." In 4th Sriwijaya Economics, Accounting, and Business Conference. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0008444206990707.

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Kusrini, Dwi Endah, Zakiatul Wildani, and Nur Azizah. "ASEAN foreign trade analysis using dynamic panel data and simultaneous regression model." In THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCES (THE 3RD ICMSc): A Brighter Future with Tropical Innovation in the Application of Industry 4.0. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0117377.

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Amelia, Mia, Kusman Sadik, and Bagus Sartono. "The Study of Robust Estimators on Panel Data Regression Model for Data Contaminated with Outliers." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290517.

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Reports on the topic "Panel data regressions"

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Bonhomme, Stéphane, and Manuel Arellano. Nonlinear panel data estimation via quantile regressions. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.4015.

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Crump, Richard K., Nikolay Gospodinov, and Ignacio Lopez Gaffney. A Simple Diagnostic for Time-Series and Panel-Data Regressions. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1132.

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Wilhelm, Daniel. Identification and estimation of nonparametric panel data regressions with measurement error. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.3415.

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Ziesemer, Thomas. Endogenous R&D elasticities of productivity functions and BERD bias: Explorations of functional coefficient regressions. UNU-MERIT, 2025. https://doi.org/10.53330/hgya6809.

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Macroeconomic productivity is modelled as a Cobb-Douglas function of private and public R&amp;D stocks in recent literature. The slope parameters of a growth rate version may change over time and with circumstances. Using the method of functional-coefficient regression, we show that human capital, GDP (per worker), services and defence R&amp;D (both % GDP), lags of domestic and foreign private and public R&amp;D, and lagged labour-augmenting technical change, all in growth rates, change the elasticities of productivity. The result is a panel data set of regression coefficients representing ela
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Raitzer, David, and Joeffrey Drouard. Empirically Estimated Impacts of Climate Change on Global Crop Production via Increasing Precipitation–Evapotranspiration Extremes. Asian Development Bank, 2024. https://doi.org/10.22617/wps240589-2.

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To assess climate change effects on crop yields, remote sensing-derived yield and agrometeorological reanalysis data are used to construct a panel at 0.1-degree resolution for 2003–2015. Regressions controlling for grid cell-specific intercepts and time trends, temperature, rainfall, and cloudiness estimate the subregional relationships between yields and precipitation-evapotranspiration extremes for rice, wheat, and maize. Results imply that climate change will cause global yield reductions for all crops, with losses highest for wheat and maize, especially in South Asia and Southern Africa.
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Valencia, Oscar, Juliana Gamboa-Arbeláez, and Gustavo Sánchez. Debt Erosion: Asymmetric Response to Demand and Supply Shocks. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005027.

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This paper explores the effect of inflation supply and demand shocks on government debt. It identifies the shocks using a sign-restricted Structural Vector Autoregression (SVAR) model with quarterly data. Estimations of dynamic panel regressions and local projections suggest that supply shocks lead to persistent increases in government debt, while demand shocks result in long-lasting declines. Furthermore, high debt levels increase economic vulnerability, amplifying the impacts of both supply and demand shocks by more than three times. Specifically, supply shocks increase debt through higher b
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Chong, Alberto E., Mark Gradstein, and María Cecilia Calderón. Foreign Aid, Income Inequality, and Poverty. Inter-American Development Bank, 2006. http://dx.doi.org/10.18235/0010965.

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The aim of this paper is to provide empirical evidence on the link between foreign aid and income inequality for the period 1973-2002. Since simple cross-country regressions cannot be taken as true time series findings, we also focus on dynamic panel data techniques, which allow accounting for potential simultaneity and heterogeneity problems. We do not find very robust evidence that foreign aid is conducive to the improvement of the distribution of income, even when the quality of institutions is taken into account. This finding is consistent with recent empirical research on aid and growth t
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Ziesemer, Thomas. A data set for domestic and foreign private and public R&D stocks and labour-augmenting technical change for 44 rich or emerging economies with explorations on panel unit roots, cointegration, growth rate slowdown, and con- or divergence. UNU-MERIT, 2025. https://doi.org/10.53330/irzp8539.

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We extend existing data sets for domestic and foreign private and public R&amp;D stocks as well as labour-augmenting technical change data based on CES production functions. We cover slightly more periods and many more countries, now 44 up from 17. We consider panel unit root issues for a large sample of 41 countries and two smaller samples with 21 rich and 20 emerging economies. Autoregressive regressions show negative time trends in the growth rates of labour-augmenting technical change, domestic private and domestic and foreign public R&amp;D stocks indicating the growth slowdown in the dat
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9

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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