128
Views
0
CrossRef citations to date
0
Altmetric
Development Economics

‘Prism’ for the meso-level: do the cyclical patterns at the national and regional levels coincide?

, , &
Article: 2350196 | Received 25 Aug 2023, Accepted 26 Apr 2024, Published online: 14 May 2024

References

  • Adarov, A. (2021). Dynamic interactions between financial cycles, business cycles and macroeconomic imbalances: A panel VAR analysis. International Review of Economics & Finance, 74, 434–451. https://doi.org/10.1016/j.iref.2021.03.021
  • Adhikari, B., & Dhital, S. (2021). Decentralization and regional convergence: Evidence from night-time lights data. Economic Inquiry, 59(3), 1066–1088. https://doi.org/10.1111/ecin.12967
  • Ahmad, M. (2021). Non-linear dynamics of innovation activities over the business cycles: Empirical evidence from OECD economies. Technology in Society, 67, 101721. https://doi.org/10.1016/j.techsoc.2021.101721
  • Almeida, A., Galiano, A., Golpe, A. A., & Martín, J. M. (2020). Regional unemployment and cyclical sensitivity in Spain. Letters in Spatial and Resource Sciences, 13(2), 187–199. https://doi.org/10.1007/s12076-020-00252-3
  • Altman, S. A., Ghemawat, P., & Bastian, P. (2019). DHL Global Connectedness Index 2018. The state of globalization in a fragile world. Dhl.com. https://www.dhl.com/content/dam/dhl/global/core/documents/pdf/glo-core-gci-2018-full-study.pdf
  • Amazonaws. (2023). Spectral analysis of time series. https://rstudio-pubs-static.s3.amazonaws.com/9428_1197bd003ebd43c49b429f22ea4f36e5.html
  • Angeletos, G. M., Iovino, L., & La’O, J. (2020). Learning over the business cycle: Policy implications. Journal of Economic Theory, 190, 105115. https://doi.org/10.1016/j.jet.2020.105115
  • Angerer, P., Kluyver, T., & Schulz, J. (2023). Repr: Serializable representations. R package version 1.1.6. https://CRAN.R-project.org/package=repr
  • Arčabić, V., & Škrinjarić, T. (2021). Sharing is caring: Spillovers and synchronization of business cycles in the European Union. Economic Modelling, 96, 25–39. https://doi.org/10.1016/j.econmod.2020.12.023
  • Atesoglu, H. S., & Vilasuso, J. (1999). A band spectral analysis of exports and economic growth in the United States. Review of International Economics, 7(1), 140–152. https://doi.org/10.1111/1467-9396.00152
  • Azzalini, A., & Genz, A. (2022). The R package\texttt{mnormt}: The multivariate normal and $t$ distributions (version 2.1.1). http://azzalini.stat.unipd.it/SW/Pkg-mnormt/
  • Bache, S., & Wickham, H. (2022). Magrittr: A forward-pipe operator for R. R package version 2.0.3. https://CRAN.R-project.org/package=magrittr
  • Batorova, I. (2012). Spectral techniques for economic time series. Unpublish dissertation thesis, Comenius University.
  • Belke, A., Domnick, C., & Gros, D. (2017). Business cycle desynchronisation. VOX, CEPR Policy Portal. https://voxeu.org/article/business-cycle-desynchronisation
  • Ben-Shachar, M. S., Lüdecke, D., & Makowski, D. (2020). Effectsize: Estimation of effect size indices and standardized parameters. Journal of Open Source Software, 5(56), 2815. https://doi.org/10.21105/joss.02815
  • Berger, T., Everaert, G., & Pozzi, L. (2021). Testing for international business cycles: A multilevel factor model with stochastic factor selection. Journal of Economic Dynamics and Control, 128, 104134. https://doi.org/10.1016/j.jedc.2021.104134
  • Blinova, T. V., Rusanovskii, V. A., & Markov, V. A. (2021). Estimating the impact of economic fluctuations on unemployment in Russian regions based on the Okun model. Studies on Russian Economic Development, 32(1), 103–110. https://doi.org/10.1134/S1075700721010032
  • Bukharbayeva, A. Z., Nauryzbayev, A. Z., Jrauova, K. S., Oralbayeva, K., & Aimagambetova, A. D. (2020). Formation of the transport and logistics system as the basis for rice production development in the Kazakhstan Aral Sea region in the context of the EAEU economic integration. Academy of Strategic Management Journal, 19(3), 1–7.
  • Burakov, N. A., Bukhvald, E. M., Kolchugina, A. V., Rubinstein, A. Y., Slavinskaya, O. A., & Slutskin, L. N. (2019). Regional Index of economic development and ranking of the subjects of the Russian Federation. Institute of Economics.
  • Campos, N. F., Fidrmuc, J., & Korhonen, I. (2019). Business cycle synchronisation and currency unions: A review of the econometric evidence using meta-analysis. International Review of Financial Analysis, 61, 274–283. https://doi.org/10.1016/j.irfa.2018.11.012
  • Cavicchioli, M., & Pistoresi, B. (2020). Unfolding the relationship between mortality, economic fluctuations, and health in Italy. The European Journal of Health Economics: HEPAC: health Economics in Prevention and Care, 21(3), 351–362. https://doi.org/10.1007/s10198-019-01135-1
  • Chai, K. C., Li, Q., Bao, X. L., Zhu, J., & He, X. X. (2021). An empirical study of economic cycle, air quality, and national health since reform and opening up. Frontiers in Public Health, 9, 706955. https://doi.org/10.3389/fpubh.2021.706955
  • Chomen, M T. (2022). Institutions–economic growth nexus in Sub-Saharan Africa. Heliyon, 8(12), e12251. https://doi.org/10.1016/j.heliyon.2022.e12251
  • Correia, S., Luck, S., & Verner, E. (2020). Pandemics depress the economy, public health interventions do not: Evidence from the 1918 flu. SSRN Electronic Journal, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3561560 https://doi.org/10.2139/ssrn.3561560
  • Croux, C., Dekimpe, M. G., & Lemmens, A. (2005). The European consumer: United in diversity? ERIM Report Series Reference No. ERS-2005-022-MKT.
  • Croux, C., Dekimpe, M. G., & Lemmens, A. (2008). Measuring and testing granger causality over the spectrum: An application to European production expectation surveys. International Journal of Forecasting, 24(3), 414–431. https://doi.org/10.1016/j.ijforecast.2008.03.004
  • Croux, C., Forni, M., & Reichlin, L. (1999). A measure of comovement for economic variables: Theory and empirics. CEPR Discussion paper No. 2339.
  • Csárdi, G. (2019). Pkgconfig: Private configuration for 'R' packages. R package version 2.0.3. https://CRAN.R-project.org/package=pkgconfig
  • Desmet, K., Greif, A., & Parente, S. L. (2017). Spatial competition, innovation and institutions: The industrial revolution and the great divergence. SSSN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2927147
  • Dowle, M., & Srinivasan, A. (2023). Data.table: Extension of ′data.framè. R package version 1.14.8. https://CRAN.R-project.org/package=data.table
  • Dubovik, M. V., & Dmitriev, S. G. (2022). Application of spectral analysis to diagnose cyclical economic development. Drukerovskij Vestnik, 2(2), 229–242. https://doi.org/10.17213/2312-6469-2022-2-229-242
  • Economist. (2018). Economists still lack a proper understanding of business cycles. https://www.economist.com/finance-and-economics/2018/04/19/economists-still-lack-a-proper-understanding-of-business-cycles
  • Federal State Statistics Service of Russian Federation. (2021). National accounts. Gross Regional Product. https://eng.rosstat.gov.ru/folder/13913
  • Fernández-Villaverde, J., & Guerrón-Quintana, P A. (2020). Uncertainty shocks and business cycle research. Review of Economic Dynamics, 37(S1), S118–S146. https://doi.org/10.1016/j.red.2020.06.005
  • Gao, Y., & Gong, G. (2020). Stabilizing and destabilizing mechanisms: A new perspective to understand business cycles. Economic Modelling, 93, 51–68. https://doi.org/10.1016/j.econmod.2020.07.002
  • Garner, R. (2017). New night lights maps open up possible real-time applications. NASA. https://www.nasa.gov/feature/goddard/2017/new-night-lights-maps-open-up-possible-real-time-applications
  • Gaslam, B. (2023). Fansi: ANSI control sequence aware string functions. R package version 1.0.4. https://CRAN.R-project.org/package=fansi
  • Granger, C. W. I. (1966). The typical spectral shape of an economic variable. Econometrica, 34(1), 150–161. https://doi.org/10.2307/1909859
  • Granger, C. W. I. (1969). Investigating casual relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791
  • Granger, C. W. I., & Hatanak, M. (1964). Spectral analysis of economic time series. Princeton University Press.
  • Grolemund, G., & Wickham, H. (2011). Dates and times made easy with lubridate. Journal of Statistical Software, 40(3), 1–25. https://doi.org/10.18637/jss.v040.i03
  • Hang, Y., & Xue, W. (2020). The asymmetric effects of monetary policy on the business cycle: Evidence from the panel smoothed quantile regression model. Economics Letters, 195, 109450. https://doi.org/10.1016/j.econlet.2020.109450
  • Hara, K., Uwasu, M., Kobayashi, H., Kurimoto, S., Yamanaka, S., Shimoda, Y., & Umeda, Y. (2012). Enhancing meso level research in sustainability science—challenges and research needs. Sustainability, 4(8), 1833–1847. https://doi.org/10.3390/su4081833
  • Harrington, J., & Rubold, U. (2018). Spectral analysis. https://www.phonetik.uni-muenchen.de/∼jmh/lehre/sem/ws1819/emuR/LESSON5/SpectralAnalysis.html
  • Harvey, A. C., & Jaeger, A. (1993). Detrending, stylized facts and the business cycle. Journal of Applied Econometrics, 8(3), 231–247. https://doi.org/10.1002/jae.3950080302
  • Hughes-Hallett, A., & Richter, C. R. (2004). A time-frequency analysis of the coherences of the US business cycle and the European business cycle. Center for Economic Policy Research Discussion Paper No. 4751.
  • Hughes-Hallett, A., & Richter, C. R. (2007). Are the new member states converging on the Eurozone? A business cycle analysis for economies in transition. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2, 49–68. https://doi.org/10.1787/19952899
  • Huntington-Klein, N. (2023). Vtable: Variable table for variable documentation. R package version 1.4.4. https://CRAN.R-project.org/package=vtable
  • Hutter, C. (2021). Cyclicality of labour market search: A new big data approach. Journal for Labour Market Research, 55(1), 1–16. https://doi.org/10.1186/s12651-020-00283-9
  • Iliopulos, E., Perego, E., & Sopraseuth, T. (2021). International business cycles: Information matters. Journal of Monetary Economics, 123, 19–34. https://doi.org/10.1016/j.jmoneco.2021.06.001
  • Iyetomi, H., Aoyama, H., Fujiwara, Y., Souma, W., Vodenska, I., & Yoshikawa, H. (2020). Relationship between macroeconomic indicators and economic cycles in US. Scientific Reports, 10(1), 8420. https://doi.org/10.1038/s41598-020-65002-3
  • Jacob, M. (2023). Kit: Data manipulation functions implemented in C. R package version 0.0.13. https://CRAN.R-project.org/package=kit
  • Jawadi, F., Ameur, H. B., Bigou, S., & Flageollet, A. (2022). Does the real business cycle help forecast the financial cycle? Computational Economics, 60(4), 1529–1546. https://doi.org/10.1007/s10614-021-10193-8
  • Jiang, D., & Weder, M. (2021). American business cycles 1889-1913: An accounting approach. Journal of Macroeconomics, 67, 103285. https://doi.org/10.1016/j.jmacro.2020.103285
  • Jones, J. H. (2018). Time series and spectral analysis. http://web.stanford.edu/class/earthsys214/notes/series.html
  • Kapounek, S., & Pomenkova, J. (2010). Business cycle development in Czech and Slovak economies. Bulletin of the Transilvania University of Brasov, 3(52), 155–166.
  • Kassambara, A. (2023). Ggpubr: 'ggplot2' based publication ready plots. R package version 0.6.0. https://CRAN.R-project.org/package=ggpubr
  • Kim, D. (2021). Economies of scale and international business cycles. Journal of International Economics, 131, 103459. https://doi.org/10.1016/j.jinteco.2021.103459
  • Korotayev, A. V., & Tsirel, S. V. (2010). A spectral analysis of world GDP dynamics: Kondratieff Waves, Kuznets Swings, Juglar and Kitchin Cycles in global economic development, and the 2008-2009 economic crisis. Structural Dynamics, 4(1), 3–32. https://doi.org/10.5070/SD941003306
  • Krantz, S. (2023a). Collapse: Advanced and fast data transformation. R package version 196. https://CRAN.R-project.org/package=collapse
  • Krantz, S. (2023b). Fastverse: A suite of high-performance packages for statistics and data manipulation. R package version 0.3.1. https://CRAN.R-roject.org/package=fastverse
  • Leal, P. H., & Marques, A C. (2022). The evolution of the environmental Kuznets curve hypothesis assessment: A literature review under a critical analysis perspective. Heliyon, 8(11), e11521. https://doi.org/10.1016/j.heliyon.2022.e11521
  • Li, X. L., Yan, J., & Wei, X. (2021). Dynamic connectedness among monetary policy cycle, financial cycle and business cycle in China. Economic Analysis and Policy, 69, 640–652. https://doi.org/10.1016/j.eap.2021.01.014
  • Lilley, A., Lilley, M., & Rinaldi, G. (2020). Public health interventions and economic growth: Revisiting the Spanish flu evidence. SSRN Electronic Journal, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3590008 https://doi.org/10.2139/ssrn.3590008
  • Lin, Y. C. (2021). Business cycle fluctuations in Taiwan – A Bayesian DSGE analysis. Journal of Macroeconomics, 70, 103349. https://doi.org/10.1016/j.jmacro.2021.103349
  • Liu, D., Wang, Q., & Song, Y. (2020). China’s business cycles at the provincial level: National synchronization, interregional coordination and provincial idiosyncrasy. International Review of Economics & Finance, 69, 629–650. https://doi.org/10.1016/j.iref.2020.06.006
  • Liu, Y. H., & Huang, W. H. (2020). Asymmetric effect of business cycles on population health: Evidence from the ASEAN Countries. Frontiers in Public Health, 8, 32. https://doi.org/10.3389/fpubh.2020.00032
  • Lüdecke, D., Ben-Shachar, M. S., Patil, I., & Makowski, D. (2020). Extracting, computing and exploring the parameters of statistical models using R. Journal of Open Source Software, 5(53), 2445. https://doi.org/10.21105/joss.02445
  • Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., & Makowski, D. (2021). Performance: An R Package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
  • Lüdecke, D., Makowski, D., Ben-Shachar, M. S., Patil, I., Wiernik, B. M., Bacher, E., & Thériault, R. (2022). Easystats: Framework for easy statistical modeling, visualization, and reporting. CRAN. R package. https://easystats.github.io/easystats/
  • Lüdecke, D., Patil, I., Ben-Shachar, M. S., Wiernik, B. M., Waggoner, P., & Makowski, D. (2021). See: An R Package for visualizing statistical models. Journal of Open Source Software, 6(64), 3393. https://doi.org/10.21105/joss.03393
  • Lüdecke, D., Waggoner, P., & Makowski, D. (2019). Insight: A unified interface to access information from model objects in R. Journal of Open Source Software, 4(38), 1412. https://doi.org/10.21105/joss.01412
  • Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2019). BayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian Framework. Journal of Open Source Software, 4(40), 1541. https://doi.org/10.21105/joss.01541
  • Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020a). Estimation of model-based predictions, contrasts and means. CRAN. https://github.com/easystats/modelbased
  • Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020b). Methods and algorithms for correlation analysis in R. Journal of Open Source Software, 5(51), 2306. https://doi.org/10.21105/joss.02306
  • Makowski, D., Lüdecke, D., Patil, I., Thériault, R., Ben-Shachar, M. S., & Wiernik, B. M. (2023). Automated results reporting as a practical tool to improve reproducibility and methodological best practices adoption. CRAN. https://easystats.github.io/report/
  • Makowski, D., Wiernik, B. M., Patil, I., Lüdecke, D., & Ben-Shachar, M. S. (2022). Correlation: Methods for correlation analysis. Version 0.8.3. https://CRAN.R-roject.org/package=correlation.
  • Mankiw, G. N., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437. https://doi.org/10.2307/2118477
  • Maršálek, R., & Poměnková, J. (2010a). Spectral analysis of the cyclical behaving of the Czech Republic industrial production. In Forum Statisticum Slovacum 2/2010. Aula EU.
  • Maršálek, R., & Poměnková, J. (2010b). Industrial production periodicity testing using R.A. Fisher Test. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 58(3), 189–196. https://doi.org/10.11118/actaun201058030189
  • Maršálek, R., & Poměnková, J. (2011). Time and frequency domain in the business cycle structure MENDELU Working Papers in Business and Economics 7/2011.
  • Mendez, C., & Santos‐Marquez, F. (2021). Regional convergence and spatial dependence across subnational regions of ASEAN: Evidence from Satellite Nighttime Light Data. Regional Science Policy & Practice, 13(6), 1750–1777. https://doi.org/10.1111/rsp3.12335
  • Mokyr, J. (2007). The market for ideas and the origins of economic growth in eighteenth century in Europe. Tijdschr. Tijdschrift voor Sociale en Economische Geschiedenis/ The Low Countries Journal of Social and Economic History, 4(1), 3–38. https://doi.org/10.18352/tseg.557
  • Monakhov, G. O., & Chechurin, L. S. (2012). The spectral analysis of the world gross domestic product’s growth and the patent activity. Nauchno-Technicheskie Vedomosti SPbGPU, 3, 176–179.
  • Müller, K., & Wickham, H. (2023). Tibble: Simple data frames. R package version 3.2.1. https://CRAN.R-project.org/package=tibble
  • Muñoz-Guillermo, M. (2021). Revisiting the business cycle model with cubic nonlinear investment function. Chaos, Solitons and Fractals, 142, 110510. https://doi.org/10.1016/j.chaos.2020.110510
  • Nelson, R. R., & Winter, S. G. (1990). An evolutionary theory of economic change. The Belknap Press of Harvard University Press.
  • Nerlove, M. (1964). Spectral analysis of seasonal adjustment procedures. Econometrica, 32(3), 241–286. https://doi.org/10.2307/1913037
  • North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press.
  • Notz, S., & Rosenkranz, P. (2021). Business cycles in emerging markets: The role of liability dollarization and valuation effects. International Review of Economics & Finance, 76, 424–450. https://doi.org/10.1016/j.iref.2021.04.010
  • Owens, R. E., & Sarte, P D G. (2005). How well do Diffusion indexes capture business cycles? A spectral analysis. Federal Reserve Bank of Richmond Economic Quarterly, 91(4), 23–42.
  • Patil, I., Makowski, D., Ben-Shachar, M. S., Wiernik, B. M., Bacher, E., & Lüdecke, D. (2022). Datawizard: An R Package for easy data preparation and statistical transformations. Journal of Open Source Software, 7(78), 4684. https://doi.org/10.21105/joss.04684
  • Perry, P. (2023). Utf8: Unicode text processing. R package version 1.2.3. https://CRAN.R-project.org/package=utf8
  • Pollock, D. S. G. (2008). The frequency analysis of the business cycle. Working Paper No. 08/12. University of Leicester.
  • Pu, X., Zeng, M., & Luo, Y. (2021). The effect of business cycles on health expenditure: A story of income inequality in China. Frontiers in Public Health, 9, 653480. https://doi.org/10.3389/fpubh.2021.653480
  • Qiao-Li, X., Wang, Y., & Zhou, W. X. (2021). Regional economic convergence in China: A comparative study of Nighttime Light and GDP. Frontiers in Physics, 9, 525162. https://doi.org/10.3389/fphy.2021.525162
  • R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  • Revelle, W. R. (2023). Psych: Procedures for psychological, psychometric, and personality research. Northwestern University. R package version 2.3.6. https://CRAN.R-project.org/package=psych
  • Romer, P. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), S71–S102. https://doi.org/10.1086/261725
  • Romer, P. (2015). Speeding-up: Theory. https://paulromer.net/speeding-up-theory/
  • Rossi-Hansberg, E., Sarte, P. D., & Schwartzman, F. (2019). Cognitive hubs and spatial redistribution, working paper series. WP 19-16. Cognitive Hubs and Spatial Redistribution. https://doi.org/10.21144/wp19-16
  • Rostan, P., & Rostan, A. (2019). Forecasting Spanish GDPs with spectral analysis. Studies of Applied Economics, 36(1), 217–234. https://doi.org/10.25115/eea.v36i1.2526
  • Saini, S., Ahmad, W., & Bekiros, S. (2021). Understanding the credit cycle and business cycle dynamics in India. International Review of Economics & Finance, 76, 988–1006. https://doi.org/10.1016/j.iref.2021.08.006
  • Sturn, S., & Epstein, G. (2021). How much should we trust five-year averaging to purge business cycle effects? A reassessment of the finance-growth and capital accumulation-unemployment nexus. Economic Modelling, 96, 242–256. https://doi.org/10.1016/j.econmod.2020.12.028
  • Takongmo, C. O. M., & Lebihan, L. (2021). Government spending, GDP and exchange rate in zero lower bound: Measuring causality at multiple horizons. Journal of Quantitative Economics: journal of the Indian Econometric Society, 19(1), 139–160. https://doi.org/10.1007/s40953-020-00213-z
  • Taymaz, E. (2022). Regional convergence or polarization: The case of the Russian Federation. Regional Research of Russia, 12(4), 469–482. https://doi.org/10.1134/S2079970522700198
  • Tisdell, C., & Svizzero, S. (2015). The Malthusian Trap and development in pre-industrial societies: A view differing from the standard one. ISSN: 1442-8563. Working Paper on Social, Economics, Policy and Development The University of Queensland.
  • Tool, M. R. (1994). Institutional adjustment and instrumental value. Review of International Political Economy, 1(3), 405–443. https://doi.org/10.1080/09692299408434293
  • Umirzakov, S. Y., Nauryzbayev, A. Z., Bukharbayeva, A. Z., Bekesheva, D. A., & Oralbayeva, A. K. (2019). Assessment of the supply chain management and problems of agricultural production development and marketing in Kazakhstan. International Journal of Supply Chain Management, 8(3), 256–265.
  • Waring, E., Quinn, M., McNamara, A., de la Rubia, E. A., Zhu, H., Lowndes, J., Ellis, S., McLeod, H., Wickham, H., Müller, K., Kirkpatrick, C., Brenstuhl, S., Schrat, P., Mikko Korpela, I., Thompson, J., McGehee, H., Roepke, M., Kennedy, P., Possenriede, D., … Stewart, H. M. (2022). Skimr: Compact and flexible summaries of data. R package version 2.1.5. https://CRAN.R-project.org/package=skimr
  • Weible, C. M., Nohrstedt, D., Cairney, P., Carter, D. P., Crow, D. A., Durnová, A. P., Heikkila, T., Ingold, K., McConnell, A., & Stone, D. (2020). COVID-19 and the policy sciences: Initial reactions and perspectives. Policy Sciences, 53(2), 225–241. https://doi.org/10.1007/s11077-020-09381-4
  • Wickham, H. (2022). Stringr: Simple, consistent wrappers for common string operations. R package version 1.5.0. https://CRAN.R-project.org/package=stringr
  • Wickham, H. (2023). Forcats: Tools for working with categorical variables (factors). R package version 1.0.0. https://CRAN.R-project.org/package=forcats
  • Wickham, H., & Bryan, J. (2023). Readxl: Read Excel Files. R package version 1.4.2. https://CRAN.R-project.org/package=readxl
  • Wickham, H., & Henry, L. (2023). Purrr: Functional programming tools. R package version 1.0.1. https://CRAN.R-project.org/package=purrr
  • Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
  • Wickham, H., Bryan, J., Barrett, M., & Teucher, A. (2023). Usethis: Automate package and project setup. R package version 2.2.1. https://CRAN.R-project.org/package=usethis
  • Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., & Dunnington, D. (2016). Ggplot2: Elegant graphics for data analysis. Springer-Verlag. https://ggplot2.tidyverse.org
  • Wickham, H., Francois, J. H. R., Bryan, J., & Bearrows, S. (2023). Readr: Read rectangular text data. R package version 2.1.4. https://CRAN.R-project.org/package=readr
  • Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). Dplyr: A grammar of data manipulation. R package version 1.1.2. https://CRAN.R-project.org/package=dplyr
  • Wickham, H., Hester, J., Chang, W., & Bryan, J. (2022). Devtools: Tools to make developing R packages easier. R package version 2.4.5. https://CRAN.R-project.org/package=devtools
  • Wickham, H., Kuhn, M., & Vaughan, D. (2022). Generics: Common S3 generics not provided by base R methods related to model fitting. R package version 0.1.3. https://CRAN.R-project.org/package=generics
  • Wickham, H., Vaughan, D., Girlich, M., & Ushey, K. (2023). Tidyr: Tidy messy data. R package version 1.3.0. https://CRAN.R-project.org/package=tidyr
  • Windle, A., Javanparast, S., Freeman, T., & Baum, F. (2023). Factors that influence evidence-informed meso-level regional primary health care planning: A qualitative examination and conceptual framework. Health Research Policy and Systems, 21(1), 99. https://doi.org/10.1186/s12961-023-01049-8
  • Yang, M. (2020). Remeasuring and decomposing stochastic trends in business cycles. Research in Economics, 74(4), 354–362. https://doi.org/10.1016/j.rie.2020.10.006
  • Ye, L., & Zhang, X. (2018). Nonlinear granger causality between health care expenditure and economic growth in the OECD and major developing countries. International Journal of Environmental Research and Public Health, 15(9), 1953. https://doi.org/10.3390/ijerph15091953
  • Zhang, Y., & Ye, A. (2022). Improving global gross primary productivity estimation by fusing multi-source data products. Heliyon, 8(3), e09153. https://doi.org/10.2139/ssrn.3978981
  • Zhu, H. (2021). KableExtra: Construct complex table with 'kable’ and pipe syntax. R package version 1.3.4. https://CRAN.R-project.org/package=kableExtra