Selection of Explanatory Variables for Linear Regression Models Estimated on Regional Panel Data

Mieczysław Kowerski

doi:10.56583/br.2141

Published : 2023-06-30

Vol. 19 No. 1 (2023)

Selection of Explanatory Variables for Linear Regression Models Estimated on Regional Panel Data

Mieczysław Kowerski

https://orcid.org/0000-0002-2147-2037

DOI: https://doi.org/10.56583/br.2141

Abstract

One of the problems in estimating of a single-equation linear regression model is the selection of explanatory variables. While many methods of selecting variables for models estimated on the basis of time series or cross-sectional data have been developed, there are no such methods of selecting variables for panel data models. The lack of an appropriate method for selecting variables for linear panel data models may lead to incorrect parameter values for some variables, which makes it difficult and sometimes even impossible to interpret the results of estimated model. Methods of selecting variables for panel data models cannot be based on the Pearson linear correlation coefficient. Therefore, a three-step procedure of variable selection for linear panel data models has been proposed, providing the correct parameter signs for all selected variables. The procedure is illustrated with the selection of variables for panel data models with fixed effects of the average annual unemployment rate according to Labor Force Survey (LFS) in Polish voivodships in the years 2010-2021 (balanced panel consisting of 192 observations).

Keywords:

variable selection, fixed effects linear panel data models

Most read articles by the same author(s)

Mieczysław Kowerski, Magdalena Kowal, Marital Separation in Poland. Regional Approach , Regional Barometer. Analyses & Prognoses: Vol. 12 No. 1 (2014)
Mieczysław Kowerski, Review of Andrzej Miszczuk and Magdalena Miszczuk, Local Economic Policy in Poland. Conditions, Institutions, Instruments , Regional Barometer. Analyses & Prognoses: Vol. 18 No. 2 (2022)
Mieczysław Kowerski, Małgorzata Wolańczyk, Mariusz Poninkiewicz, A Proposition for a Methodology to Assess the Influence of European Union Funds on Living Conditions among Citizens of a Commune , Regional Barometer. Analyses & Prognoses: Vol. 13 No. 3 (2015)
Mieczysław Kowerski, Dawid Długosz, Jarosław Bielak, Zmiany nastrojów gospodarczych w województwie lubelskim w II kwartale 2008 r. , Regional Barometer. Analyses & Prognoses: No. 4 (14) (2008)
Mieczysław Kowerski, Jarosław Bielak, Fifty Issues of Regional Barometer , Regional Barometer. Analyses & Prognoses: Vol. 15 No. 4 (2017)
Mieczysław Kowerski, Jarosław Bielak, The Influence of Business Cycle on the Forecast of Household Financial Situation , Regional Barometer. Analyses & Prognoses: Vol. 11 No. 4 (2013)
Mieczysław Kowerski, Marian Wargacki, Nastroje gospodarcze w województwie lubelskim w drugim kwartale 2002 roku , Regional Barometer. Analyses & Prognoses: No. 2 (2002)
Mieczysław Kowerski, The Economic Sentiment Index as a Tool of Estimation of the Growth Rate of GDP in Regions. An Example of the Lubelskie Voivodship , Regional Barometer. Analyses & Prognoses: Vol. 11 No. 1 (2013)
Mieczysław Kowerski, The Impact of Economic Sentiments Among Entrepreneurs and Consumers on the Dividend Decisions by Limited Liability Companies , Regional Barometer. Analyses & Prognoses: No. 2 (20) (2010)
Mieczysław Kowerski, Dawid Długosz, Nastroje gospodarcze w województwie lubelskim w czwartym kwartale 2002 roku , Regional Barometer. Analyses & Prognoses: No. 3 (2003)

<< < 1 2 3 4 5 6 > >>

Details

References

Statistics

Authors

Download files

PDF

Citation rules

Kowerski, M. (2023). Selection of Explanatory Variables for Linear Regression Models Estimated on Regional Panel Data. Regional Barometer. Analyses & Prognoses, 19(1), 95–105. https://doi.org/10.56583/br.2141

Altmetric indicators

Cited by / Share

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Antczak, E., E. Gałecka-Burdziak, and R. Pater. 2018. “Unemployment and Vacancy Flows in Spatial Labour Market Matching at the Regional Level. The Case of a Transition Country.” Journal of Applied Economics 21 (1):25–43. doi: 10.1080/15140326.2018.1526874. DOI: https://doi.org/10.1080/15140326.2018.1526874

Arellano, M., and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” The Review of Economic Studies 58 (2):277–297. doi: 10.2307/2297968. DOI: https://doi.org/10.2307/2297968

Biørn, E. 2017. Econometrics of Panel Data. Methods and Applications. Oxford: Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780198753445.001.0001

Borsuk, M., and K. Kostrzewa. 2020. “Miary ryzyka systemowego dla Polski. Jak ryzyko systemowe wpływa na akcję kredytową banków?” Bank i Kredyt 51 (3):211–238.

Charemza, W., and D. Deadman. 1997. Nowa ekonometria. Translated by E.M. Syczewska. Warszawa: Polskie Wydawnictwo Ekonomiczne.

Dańska-Borsiak, B. 2011. Dynamiczne modele panelowe w badaniach ekonomicznych. Łódź: Wydawnictwo Uniwersytetu Łódzkiego. DOI: https://doi.org/10.18778/7525-549-2

Driver, C., A. Grosman, and P. Scaramozzino. 2020. “Dividend Policy and Investor Pressure.” Economic Modelling 89:559–576. doi: 10.1016/j.econmod.2019.11.016. DOI: https://doi.org/10.1016/j.econmod.2019.11.016

Grabiński, T., S. Wydymus, and A. Zeliaś. 1982. Metody doboru zmiennych w modelach ekonometrycznych. Warszawa: Państwowe Wydawnictwo Naukowe.

Hellwig, Z. 1969. “Problem optymalnego wyboru predykant.” Przegląd Statystyczny 16 (3/4):221–237.

Hellwig, Z. 1976. “Przechodniość relacji skorelowania zmiennych losowych i płynące stąd wnioski ekonometryczne.” Przegląd Statystyczny 23 (1):3–20.

Hellwig, Z. 1977. “Efekt katalizy w modelu ekonometrycznym, jego wykrywanie i usuwanie.” Przegląd Statystyczny 24 (2):179–191.

Herman, S. 2019. “Impact of Joint-Stock Companies’ Financial Condition on Real Activities Manipulation to Manage Earnings.” Wiadomości Statystyczne 64 (10):36–52. DOI: https://doi.org/10.5604/01.3001.0013.7588

Hsiao, C. 2014. Analysis of Panel Data. 3rd ed. Econometric society monographs. New York, NY: Cambridge University Press.

Karkowska, R. 2019. “Systemic Risk Affected by Country Level Development. The Case of the European Banking Sector.” Argumenta Oeconomica 2 (43):255–282. doi: 10.15611/aoe.2019.2.11. DOI: https://doi.org/10.15611/aoe.2019.2.11

Kowerski, M., and J. Bielak. 2021. “Kilka uwag na temat pomiaru zależności pomiędzy zmiennymi o panelowej strukturze danych.” Wiadomości Statystyczne 66 (5):7–25. doi: 10.5604/01.3001.0014.8781. DOI: https://doi.org/10.5604/01.3001.0014.8781

Kufel, T. 2011. Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL. 3rd ed. Warszawa: Wydawnictwo Naukowe PWN.

Maddala, G.S. 2006. Ekonometria. Translated by M. Gruszczyński, E. Tomczyk and B. Witkowski. Warszawa: Wydawnictwo Naukowe PWN.

Nowak, E. 2002. Zarys metod ekonometrii. Zbiór zadań. 3rd ed. Warszawa: Wydawnictwo Naukowe PWN.

Pluskota, A. 2020. “The Impact of Corruption on Economic Growth and Innovation in an Economy in Developed European Countries.” Annales Universitatis Mariae Curie-Skłodowska. Sectio H, Oeconomia 54 (2):77–87. doi: 10.17951/h.2020.54.2.77-87. DOI: https://doi.org/10.17951/h.2020.54.2.77-87

Tokarski, T., S. Roszkowska, and P. Gajewski. 2005. “Regionalne zróżnicowanie łącznej produktywności czynników produkcji w Polsce.” Ekonomista (2):215–244.

Witkowski, B. 2012. “Modele danych panelowych.” In Mikroekonometria. Modele i metody analizy danych indywidualnych, edited by M. Gruszczyński, 267–308. Warszawa: Wolters Kluwer Polska.