Liniowe modele ekonometryczne. Zaawansowane zastosowania języka R

Piotr Wdowiński

doi:10.18778/8331-975-9

Słowa kluczowe: program R, RStudio, RShiny, programowanie i algorytmika, statystyka, ekonometria, prognozowanie

Opublikowane: 23 grudnia 2025

Kategorie: Ekonomia i zarządzanie, Otwarty dostęp

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Monografia przedstawia zaawansowane zastosowania języka R w analizie ekonometrycznej, ze szczególnym uwzględnieniem liniowych modeli ekonometrycznych. Publikacja łączy aspekty teoretyczne z praktycznymi, obejmując implementację algorytmów w R, przetwarzanie zbiorów danych oraz automatyzację raportów w formacie R/Markdown. Autor omawia procesy stochastyczne, metody estymacji i testowania specyfikacji modeli, a także zagadnienia związane z oceną jakości dopasowania modeli do danych. Książka zawiera liczne przykłady kodu w R, ilustrujące modele ekonometryczne, analizy empiryczne oraz metody prognozowania zmiennych ekonomicznych i finansowych. Szczególną wartość stanowi integracja metod ekonometrycznych z narzędziami wizualizacji danych oraz aplikacjami R/Shiny, umożliwiającymi tworzenie interaktywnych systemów prognostycznych. Publikacja jest skierowana do badaczy, analityków danych, studentów kierunków ekonomicznych i finansowych oraz wszystkich osób zainteresowanych nowoczesnymi metodami analizy danych w ekonometrii z wykorzystaniem języka R.

Piotr Wdowiński - Uniwersytet Łódzki, Wydział Ekonomiczno-Socjologiczny, Katedra Ekonometrii

– doktor habilitowany nauk ekonomicznych, profesor Uniwersytetu Łódzkiego, w latach 2012–2016 dyrektor Instytutu Ekonometrii UŁ, w latach 2016–2024 kierownik Katedry Ekonometrii UŁ. Autor monografii i publikacji naukowych w recenzowanych czasopismach. Redaktor naczelny czasopisma „Econometric Research in Finance”. W latach 2002–2016 organizator międzynarodowych konferencji naukowych pod nazwą „Forecasting Financial Markets and Economic Decision-Making (FindEcon)”. Specjalizuje się w modelowaniu i prognozowaniu ekonometrycznym procesów makroekonomicznych i finansowych, a także w zagadnieniach stabilności finansowej, polityki makroostrożnościowej i ryzyka kredytowego.

Abedin, J., 2017. Modern R Programming Cookbook. Packt Publishing, Birmingham.

Acerbi, C., 2002. Spectral Measures of Risk: A Coherent Representation of Subjective Risk Aversion. Journal of Banking & Finance 26(7), 1505–1518. https://doi.org/10.1016/S0378-4266(02)00207-7.

Acerbi, C., Tasche, D., 2002. On the Coherence of Expected Shortfall. Journal of Banking & Finance 26(7), 1487–1503. https://doi.org/10.1016/S0378-4266(02)00206-5.

Akaike, H., 1974. A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705.

Akaike, H., 1973. Information Theory and an Extension of the Maximum Likelihood Principle, w: Petrov, B.N., Csáki, F. (red.), Second International Symposium on Information Theory. Akadémiai Kiadó, Budapest, s. 267–281.

Allaire, J., Xie, Y., Dervieux, C., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., Iannone, R., 2025. rmarkdown: Dynamic Documents for R, R package version 2.30, https://github.com/rstudio/rmarkdown.

Anderson, E., 1935. The Irises of the Gaspé Peninsula. Bulletin of the American Iris Society 59, 2–5.

Angeloni, I., Kashyap, A.K., Mojon, B. (Red.), 2003. Monetary Policy Transmission in the Euro Area: A Study by the Eurosystem Monetary Transmission Network. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511492372.

Araci, D., 2019. FinBERT: Financial Sentiment Analysis with Pre-Trained Language Models, arXiv preprint arXiv:1908.10063, https://arxiv.org/abs/1908.10063.University of Amsterdam.

Arnold, J.B., 2024. ggthemes: Extra Themes, Scales and Geoms for ggplot2, R package version 5.1.0, https://jrnold.github.io/ggthemes/.

Arratia, A., 2014. Computational Finance: An Introductory Course with R, Atlantis Studies in Computational Finance and Financial Engineering. Atlantis Press, Paris.

Auguie, B., 2017. gridExtra: Miscellaneous Functions for ”Grid” Graphics, R package version 2.3, https://CRAN.R-project.org/package=gridExtra.

Baba, Y.J., Engle, R.F., Kraft, D.F., Kroner, K.F., 1990. Multivariate Simultaneous Generalized ARCH. Journal of Econometrics 47(1-2), 27–60. https://doi.org/10.1016/0304-4076(90)90092-X.

Bache, S.M., Wickham, H., 2022. magrittr: A Forward-Pipe Operator for R, R package version 2.0.3, https://magrittr.tidyverse.org.

Baranowski, P., 2008. Optymalna stopa inflacji w modelowaniu wzrostu gospodarczego. Ekonomista (4), 447–466.

Baranowski, P., Doryń, W., Łyziak, T., Stanisławska, E., 2021. Words and Deeds in Managing Expectations: Empirical Evidence from an Inflation Targeting Economy. Economic Modelling 95, 49–67. https://doi.org/10.1016/j.econmod.2020.12.003.

Baranowski, P., Górajski, M., Malaczewski, M., Szafrański, G., 2016. Inflation in Poland under State-Dependent Pricing. Ekonomický časopis 64(10), 937–957.

Barrett, T., Dowle, M., Srinivasan, A., Gorecki, J., Chirico, M., Hocking, T., 2024. data.table: Extension of ‘data.frame‘, R package version 1.15.4, https://r-datatable.com.

Becker, R.A., Chambers, J.M., Wilks, A.R., 1988. The New S Language: A Programming Environment for Data Analysis and Graphics. Wadsworth & Brooks/Cole Advanced Books & Software, Pacific Grove, CA.

Becker, R., Osborn, D.R., Yildirim, D., 2012. A Threshold Cointegration Analysis of Interest Rate Pass-Through to UK Mortgage Rates. Economic Modelling 29(6), 2504–2513. https://doi.org/10.1016/j.econmod.2012.08.004.

Bera, A.K., Jarque, C.M., 1981. Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals: Monte Carlo Evidence. Economics Letters 7(4), 313–318. https://doi.org/10.1016/0165-1765(81)90035-5.

Berlinger, E., Illés, F., Somogyi, E., 2015. Mastering R for Quantitative Finance. Packt Publishing, Birmingham.

Białek, J., 2022. Scanner data processing in a newest version of the PriceIndices package. Statistical Journal of the IAOS 38(4), 1369–1397. https://doi.org/10.3233/SJI-220963.

Biecek, P., 2013. Analiza danych z programem R: modele liniowe z efektami stałymi, losowymi i mieszanymi. PWN, Warszawa.

Blanchard, O., Johnson, D.R., 2012. Macroeconomics, 6th ed. Pearson, Upper Saddle River, NJ.

Blangiewicz, M., Miłobędzki, P., 2009. The Rational Expectations Hypothesis of the Term Structure at the Polish Interbank Market. Przegląd Statystyczny 56(1), 23–39. https://doi.org/10.59139/ps.2009.01.2.

Blangiewicz, M., Miłobędzki, P., 2008. Is there a Nonlinear Mean Reversion in the Term Structure of Interest Rates at the Polish Interbank Market?, w: Soares, J.O., Pina, J., Catalão‐Lopes, M. (red.), New Developments in Financial Modelling. Cambridge Scholars Publishing, Newcastle upon Tyne, s. 77–94.

Bollerslev, T., 1986. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.

Boole, G., 1854. An Investigation of the Laws of Thought on Which are Founded the Mathematical Theories of Logic and Probabilities. Walton & Maberly, London.

Box, G.E.P., Jenkins, G.M., 1970. Time Series Analysis: Forecasting and Control. HoldenDay, San Francisco.

Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M., 2015. Time Series Analysis: Forecasting and Control, 5th ed. Wiley, Hoboken, NJ.

Box, G.E.P., Pierce, D.A., 1970. Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models. Journal of the American Statistical Association 65(332), 1509–1526. https://doi.org/10.1080/01621459.1970.10481180.

Boyd, S., Vandenberghe, L., 2004. Convex Optimization. Cambridge University Press, Cambridge.

Boysel, S., Vaughan, D., 2021. fredr: An R Client for the FRED API, R package version 2.1.0, https://github.com/sboysel/fredr.

Brandt, P., 2016. MSBVAR: Markov-Switching, Bayesian, Vector Autoregression Models, R package version 0.9-3, https://CRAN.R-project.org/package=MSBVAR.

Breiman, L., 2001. Random Forests. Machine Learning 45(1), 5–32. https://doi.org/10.1023/A:1010933404324.

Breusch, T.S., 1978. Testing for Autocorrelation in Dynamic Linear Models. Australian Economic Papers 17(31), 334–355. https://doi.org/10.1111/j.1467-8454.1978.tb00635.x.

Breusch, T.S., Pagan, A.R., 1979. A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica 47(5), 1287–1294. https://doi.org/10.2307/1911963.

Brooks, C., 2019. Introductory Econometrics for Finance, 4th ed. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108524872.

Brzeszczyński, J., 2018. International Volatility Spillovers: WIG20, DJIA and FTSE100. Acta Universitatis Lodziensis. Folia Oeconomica 6(339), 125–146.

Brzoza-Brzezina, M., 2002. The Relationship between Real Interest Rates and Inflation (NBP Working Paper No. 23), https://static.nbp.pl/publikacje/materialy-i-studia/23_en.pdf. Narodowy Bank Polski.

Brzoza-Brzezina, M., Kłos, B., Kot, A., Łyziak, T., 2002. Hipoteza neutralności pieniądza (Raport). Narodowy Bank Polski, Departament Analiz i Badań.

Brzoza-Brzezina, M., Kolasa, M., Koloch, G., Makarski, K., Rubaszek, M., 2013. Monetary Policy in a Non-Representative Agent Economy: A Survey. Journal of Economic Surveys 27(4), 641–669. https://doi.org/10.1111/j.1467-6419.2011.00710.x.

Bukowski, S.I., 2025. The Determinants of Economic Growth in Poland in 2018–2023. European Research Studies Journal 28(1), 622–639. https://doi.org/10.35808/ersj/3926.

Bukowski, S.I., Gowers, R., 2016. The Degree of Integration of Equity Markets in Central Europe with the US and UK Equity Markets. Central European Review of Economics & Finance 11(1), 5–26.

Burns, P., 2012. The R Inferno. Lulu.com.

Cairns, A.J.G., 2004. Interest Rate Models: An Introduction. Princeton University Press, Princeton, NJ.

Chambers, J.M., 2008. Software for Data Analysis: Programming with R. Springer, New York, NY. https://doi.org/10.1007/978-0-387-75936-4.

Chambers, J.M., 1998. Programming with Data: A Guide to the S Language. Springer, New York, NY.

Chambers, J.M., Hastie, T.J. (Red.), 1992. Statistical Models in S, 1st ed. Chapman & Hall/CRC, London. https://doi.org/10.1201/9780203738535.

Chang, W., Borges Ribeiro, B., 2021. shinydashboard: Create Dashboards with Shiny, R package version 0.7.2, http://rstudio.github.io/shinydashboard/.

Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., Borges, B., 2024. shiny: Web Application Framework for R, R package version 1.9.1, https://shiny.posit.co/.

Charemza, W.W., Deadman, D.F., 1997. New Directions in Econometric Practice: General to Specific Modelling, Cointegration, and Vector Autoregression, 2nd ed, https://ideas.repec.org/b/elg/eebook/1139.html. Edward Elgar Publishing, Cheltenham.

Chen, T., Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System, w: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, s. 785–794. https://doi.org/10.1145/2939672.29 39785.

Chirico, M., Oehlschlägel, J., 2025. bit: Classes and Methods for Fast Memory-Efficient Boolean Selections, R package version 4.6.0, https://github.com/r-lib/bit.

Cohen, Y., Cohen, J.Y., 2008. Statistics and Data with R: An Applied Approach Through Examples. Wiley, Hoboken, NJ.

Condylios, S., Rudis, B., Kiener, P., 2022. netstat: Retrieve Network Statistics Including Available TCP Ports, R package version 0.1.2, https://github.com/stevecondylios/ne tstat.

Crawley, M.J., 2012. The R Book, 2nd ed. Wiley, Chichester.

Croissant, Y., Millo, G., 2018. Panel Data Econometrics with R. Wiley, Chichester.

Cryer, J.D., Chan, K.-S., 2008. Time Series Analysis: With Applications in R. Springer, New York, NY. https://doi.org/10.1007/978-0-387-75959-3.

Dahl, D.B., Scott, D., Roosen, C., Magnusson, A., Swinton, J., 2019. xtable: Export Tables to LaTeX or HTML, R package version 1.8-4, http://xtable.r-forge.r-project.org/.

Dahl, O.-J., Dijkstra, E.W., Hoare, C.A.R. (Red.), 1972. Structured Programming. Academic Press, London.

Dickey, D.A., Fuller, W.A., 1981. Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica 49(4), 1057–1072.

Dickey, D.A., Fuller, W.A., 1979. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association 74(366a), 427–431. https://doi.org/10.1080/01621459.1979.10482531.

Diebold, F.X., Li, C., 2006. Forecasting the Term Structure of Government Bond Yields. Journal of Econometrics 130(2), 337–364. https://doi.org/10.1016/j.jeconom.2005.03.005.

Doman, M., 2004. Prognozowanie zmienności polskich indeksów giełdowych za pomocą modeli GARCH przy użyciu danych wysokiej częstotliwości. Acta Universitatis Lodziensis. Folia Oeconomica 177, 291–309.

Doman, M., Doman, R., 2004. Ekonometryczne modelowanie dynamiki polskiego rynku finansowego, Prace Habilitacyjne. Akademia Ekonomiczna w Poznaniu, Poznań.

Domański, C., Wagner, W., 1992. Uwagi o mocy testu wielowymiarowej normalności Shapiro–Wilka. Acta Universitatis Lodziensis. Folia Oeconomica 117, 33–45.

Doornik, J.A., Hansen, H., 2008. An Omnibus Test for Univariate and Multivariate Normality. Oxford Bulletin of Economics and Statistics 70(s1), 927–939. https://doi.org/10.1111/j.1468-0084.2008.00537.x.

Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V., 1997. Support Vector Regression Machines, w: Mozer, M.C., Jordan, M.I., Petsche, T. (red.), Advances in Neural Information Processing Systems 9, NIPS. MIT Press, Cambridge, MA, s. 155–161.

Durbin, J., Watson, G.S., 1950. Testing for Serial Correlation in Least Squares Regression: I. Biometrika 37(3-4), 409–428. https://doi.org/10.1093/biomet/37.3-4.409.

Embrechts, P., Klüppelberg, C., Mikosch, T., 1997. Modelling Extremal Events for Insurance and Finance, Stochastic Modelling and Applied Probability. Springer, Berlin.

Engle, R.F., Ng, V.K., 1993. Measuring and Testing the Impact of News on Volatility. Journal of Finance 48(5), 1749–1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x.

Fair, R.C., Shiller, R.J., 1990. Comparing Information in Forecasts from Econometric Models. The American Economic Review 80(3), 375–389.

Fałdziński, M., Osińska, M., Zdanowicz, T., 2012. Detecting Risk Transfer in Financial Markets Using Different Risk Measures. Central European Journal of Economic Modelling and Econometrics 4(1), 45–64.

Fellows, I., 2018. wordcloud: Word Clouds, R package version 2.6, http://blog.fellstat.com/?cat=11.

Fischer, T., Krauss, C., 2018. Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions. European Journal of Operational Research 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054.

Fisher, R.A., 1936. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7(2), 179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.

Fiszeder, P., 2009. Modele klasy GARCH w empirycznych badaniach finansowych. Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, Toruń.

Fiszeder, P., 2003. Zastosowanie modeli GARCH w analizie procesów obserwowanych na GPW w Warszawie oraz wybranych rynkach akcji na świecie. Metody ilościowe w naukach ekonomicznych, Trzecie Warsztaty Doktorskie z Zakresu Ekonometrii i Statystyki 11–33.

Fiszeder, P., Bruzda, J., 2001. Badanie zależności pomiędzy indeksami giełdowymi na GPW w Warszawie – analiza wielorównaniowych modeli GARCH. Prace Naukowe Akademii Ekonomicznej we Wrocławiu 890(1), 110–122.

Fiszeder, P., Kwiatkowski, J., 2005. Model GARCH-M ze zmiennym parametrem – analiza wybranych spółek i indeksów notowanych na GPW. Przegląd Statystyczny 52(3), 73–88.

Fiszeder, P., Rowiński, S., 2012. Modelowanie zależności pomiędzy wybranymi procesami makroekonomicznymi a warszawskim indeksem giełdowym. Ekonomia i Prawo 10(3), 153–167.

Forbes, K., Kirkham, L., Theodoridis, K., 2021. A Trendy Approach to UK Inflation Dynamics. Manchester School 89(S1), 23–75. https://doi.org/10.1111/manc.12293.

Franta, M., Horvath, R., Rusnak, M., 2014. Evaluating Changes in the Monetary Transmission Mechanism in the Czech Republic. Empirical Economics 46(3), 827–842. https://doi.org/10.1007/s00181-013-0699-0.

Freitas, W., 2024. bizdays: Business Days Calculations and Utilities, R package version 1.0.16, https://github.com/wilsonfreitas/R-bizdays.

Fuller, W.A., 1976. Introduction to Statistical Time Series. John Wiley & Sons, New York, NY.

Gagolewski, M., 2022. stringi: Fast and portable character string processing in R. Journal of Statistical Software 103(2), 1–59. https://doi.org/10.18637/jss.v103.i02.

Gajdka, J., Pietraszewski, P., 2019. Fundamental Determinants of Polish Stock Market Returns: P/E and Price-to-Book Perspective. Economic Computation and Economic Cybernetics Studies and Research.

Gajdka, J., Schabek, T., 2018. Performance of Textile and Apparel Companies Listed on the Warsaw Stock Exchange. Central European Financial Studies. https://doi.org/10.5604/01.3001.0012.5161.

Gambacorta, L., Iannotti, S., 2007. Are There Asymmetries in the Response of Bank Interest Rates to Monetary Shocks? Applied Economics 39(19), 2503–2517. https://doi.org/10.1080/00036840600707241.

Gatnar, E., Walesiak, M. (Red.), 2009. Statystyczna analiza danych z wykorzystaniem programu R. PWN, Warszawa.

Gągolewski, M., 2016. Programowanie w języku R: analiza danych, obliczenia, symulacje. PWN, Warszawa.

Genz, A., Bretz, F., Miwa, T., Mi, X., Hothorn, T., 2025. mvtnorm: Multivariate Normal and t Distributions, R package version 1.3-3, http://mvtnorm.R-forge.R-project.org.

Gerrard, P., Johnson, R.M., 2015. Mastering Scientific Computing with R. Packt Publishing, Birmingham.

Gibson, R., Lhabitant, F.-S., Talay, D., 2010. Modeling the Term Structure of Interest Rates: A Review of the Literature. Foundations and Trends in Finance 5(1-2), 1–156. https://doi.org/10.1561/0500000032.

Gillespie, C., Lovelace, R., 2017. Efficient R Programming: A Practical Guide to Smarter Programming. O’Reilly Media, Sebastopol, CA.

Godfrey, L.G., 1978. Testing Against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables. Econometrica 46(6), 1293–1301. https://doi.org/10.2307/1913829.

Goldfeld, S.M., Quandt, R.E., 1965. Some Tests for Homoscedasticity. Journal of the American Statistical Association 60(310), 539–547. https://doi.org/10.1080/01621459.1965.10480811.

Gordon, R.J., 1997. The Time-Varying NAIRU and Its Implications for Economic Policy. Journal of Economic Perspectives 11(1), 11–32. https://doi.org/10.1257/jep.11.1.11.

Gostkowska-Drzewicka, M., Majerowska, E., 2022. Capital Structure Formation in Stock Exchange Listed Companies of the Visegrad Group: A Dynamic Approach. Zeszyty Naukowe Politechniki Śląskiej. Organizacja i Zarządzanie (166), 255–273. https://doi.org/10.29119/1641-3466.2022.166.17.

Gostkowska-Drzewicka, M., Majerowska, E., 2018. Przynależność sektorowa a wyniki spółek notowanych na GPW w Warszawie. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu (531), 139–148.

Gostkowska-Drzewicka, M., Majerowska, E., 2017. Sektor finansowy a koniunktura – analiza ryzyka inwestycyjnego. Zarządzanie i Finanse 15, 153–169.

Gradzewicz, M., Jabłonowski, J., Sasiela, M., Żółkiewski, Z., 2024. The Impact of Energy Price Increases on the Polish Economy. Energy Economics 140. https://doi.org/10.1 016/j.eneco.2024.107944.

Granjon, D., van Leemput, V., Perrier, V., Rudolf, I., 2024. shinyMobile: Mobile Ready shiny Apps with Standalone Capabilities, R package version 2.0.0, https://github.c om/RinteRface/shinyMobile.

Greene, W.H., 2003. Econometric Analysis, 5th ed. Prentice Hall, Upper Saddle River, New Jersey.

Gregor, J., Melecký, A., Melecký, M., 2021. Interest Rate Pass-Through: A Meta-Analysis of the Literature. Journal of Economic Surveys 35(1), 141–191. https://doi.org/10.1111/joes.12393.

Gries, S.T., 2009. Statistics for Linguistics with R: A Practical Introduction, Trends in Linguistics: Studies and Monographs. Mouton de Gruyter, Berlin. https://doi.org/10.1515/9783110307474.

Grolemund, G., 2014. Hands-On Programming with R: Write Your Own Functions and Simulations. O’Reilly Media, Sebastopol, CA.

Hair, Jr., Joseph F., Black, W.C., Babin, B.J., Anderson, R.E., 2010. Multivariate Data Analysis, 7th ed. Pearson, Upper Saddle River, NJ.

Hałka, A., Leszczyńska-Paczesna, A., 2023. Inflation Measurement in Times of Large Consumption Shifts – Evidence of the CPI Bias from Poland. Acta Oeconomica 73(3), 383–401. https://doi.org/10.1556/032.2023.00022.

Hamilton, J.D., 1994. Time Series Analysis. Princeton University Press, Princeton, NJ.

Harrison, J., 2022. RSelenium: R Bindings for Selenium WebDriver, R package version 1.7.9, https://docs.ropensci.org/RSelenium/.

Harrison, J., 2016. seleniumPipes: R Client Implementing the W3C WebDriver Specification, R package version 0.3.7, https://github.com/johndharrison/seleniumPipes.

Harvey, A.C., 1990. The Econometric Analysis of Time Series. MIT Press, Cambridge.

Harvey, A.C., Collier, P., 1977. Testing for Functional Misspecification in Regression Analysis. Journal of Econometrics 6(1), 103–119. https://doi.org/10.1016/0304-4076(77)90057-4.

Hastings, C., Mosteller, F., Tukey, J.W., Winsor, C.P., 1947. Low Moments for Small Samples: A Comparative Study of Order Statistics. The Annals of Mathematical Statistics 18(3), 413–426. https://doi.org/10.1214/aoms/1177730388.

Hertel, K., Leszczyńska, A., 2013. Uporczywość inflacji i jej komponentów – badanie empiryczne dla Polski. Przegląd Statystyczny 60(2), 187–210. https://doi.org/10.59139/ps.2013.02.2.

Hlavac, M., 2022. stargazer: Well-Formatted Regression and Summary Statistics Tables, R package version 5.2.3, https://CRAN.R-project.org/package=stargazer.

Holt, C.C., 1957. Forecasting Trends and Seasonal by Exponentially Weighted Averages (Research Memorandum No. 52). Carnegie Institute of Technology.

Hong, H., Liu, A., 2011. Causality in Risk. Journal of Financial Econometrics 9(3), 535–575. https://doi.org/10.1093/jjfinec/nsq019.

Hothorn, T., Zeileis, A., Farebrother, R.W., Cummins, C., 2022. lmtest: Testing Linear Regression Models, R package version 0.9-40, https://CRAN.R-project.org/package=lmtest.

International Accounting Standards Board (Red.), 2023. IFRS Accounting Standards – Issued 2023, Part A: Required Standards, Includes IAS, IFRIC and SIC Standards, plus Conceptual Framework as at 1 January 2023. IFRS Foundation, London.

Jacobson, D., Brail, G., Woods, D., 2012. APIs: A Strategy Guide. O’Reilly Media, Sebastopol, CA.

Janecki, J., 2024. Oczekiwania inflacyjne w Polsce – stan wiedzy i propozycje jej poszerzenia. Zeszyty Naukowe Kolegium Zarządzania i Finansów, Szkoła Główna Handlowa w Warszawie 198, 9–24.

Jarque, C.M., Bera, A.K., 1987. A Test for Normality of Observations and Regression Residuals. International Statistical Review 55(2), 163–172. https://doi.org/10.2307/1403192.

Jarque, C.M., Bera, A.K., 1980. Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economics Letters 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5.

Kaas, R., Goovaerts, M., Dhaene, J., Denuit, M., 2008. Modern Actuarial Risk Theory: Using R. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70998-5.

Kamiński, B., Zawisza, M., 2012. Receptury w R: podręcznik dla ekonomistów. Oficyna Wydawnicza SGH, Warszawa.

Kelm, R., 2010. The Exchange Rate and Two Price Inflations in Poland in the Period 1999–2009: Do Globalization and Balassa–Samuelson Effect Matter? Central European Journal of Economic Modelling and Econometrics 2(4), 315–349. https://doi.org/10.24425/cejeme.2010.119333.

Kelm, R., Majsterek, M., 2005. An I(2) Analysis of Inflationary Processes in Poland. Acta Universitatis Lodziensis. Folia Oeconomica 190, 55–73.

Kelm, R., Sobiech Pellegrini, I., 2023. Imported Inflation and the Wage-Price Nexus in Poland. Gospodarka Narodowa 315(3), 48–70. https://doi.org/10.33119/GN/169436.

Kianifard, F., Swallow, W.H., 1996. A Review of the Development and Application of Recursive Residuals in Linear Models. Journal of the American Statistical Association 91(433), 391–400. https://doi.org/10.2307/2291419.

Kleiber, C., Zeileis, A., 2008. Applied Econometrics with R. Springer, New York, NY. https://doi.org/10.1007/978-0-387-77318-6.

Kliber, A., Kliber, P., Płuciennik, P., Piwnicka, M., 2016. POLONIA Dynamics During the Years 2006–2012 and the Effectiveness of the Monetary Policy of the National Bank of Poland. Empirica 43(1), 37–59. https://doi.org/10.1007/s10663-015-9287-1.

Koenker, R., 1981. A Note on Studentizing a Test for Heteroscedasticity. Journal of Econometrics 17(1), 107–112. https://doi.org/10.1016/0304-4076(81)90062-2.

Kolasa, M., Rubaszek, M., Skrzypczyński, P., 2012. Putting the New Keynesian DSGE Model to the Real-Time Forecasting Test. Journal of Money, Credit and Banking 44(7), 1301–1324. https://doi.org/10.1111/j.1538-4616.2012.00533.x.

Kopczewska, K., Kopczewski, T., Wójcik, P., 2016. Metody ilościowe w R: aplikacje ekonomiczne i finansowe, 2nd ed. CeDeWu, Warszawa.

Kostyra, T.P., Rubaszek, M., 2020. Forecasting the Yield Curve for Poland. Econometric Research in Finance 5(2), 103–117. https://doi.org/10.2478/erfin-2020-0006.

Kotłowski, J., 2008. Forecasting Inflation with Dynamic Factor Model – The Case of Poland (Working Paper No. 24), Published 24 February 2008. Retrieved via SGH Repository (handle 20.500.12182/1359), http://kolegia.sgh.waw.pl/pl/KAE/struktura/IE/struktura/ZES/Documents/Working_Papers/aewp02-08.pdf. Department of Applied Econometrics, Warsaw School of Economics.

Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992. Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root. Journal of Econometrics 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y.

Kwiatkowski, J., 2019. Modele ze zmiennymi ukrytymi w analizie inflacji w Polsce. Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, Toruń.

Kwiatkowski, Ł., 2010. Markov Switching In-Mean Effect. Bayesian Analysis in Stochastic Volatility Framework. Central European Journal of Economic Modelling and Econometrics 2(1), 59–94. https://doi.org/10.24425/cejeme.2010.119320.

Lahti, L., Huovari, J., Kainu, M., Biecek, P., 2023. eurostat: Tools for Eurostat Open Data, R package version 4.0.0, https://ropengov.github.io/eurostat/.

Le Pennec, E., Slowikowski, K., 2024. ggwordcloud: A Word Cloud Geom for ggplot2, R package version 0.6.2, https://github.com/lepennec/ggwordcloud.

Leroy, A., Lucotte, Y., 2016. Structural and Cyclical Determinants of Bank Interest-Rate Pass-Through in the Eurozone. Comparative Economic Studies 58(2), 196–225. https://doi.org/10.1057/ces.2016.6.

Leszczyńska-Paczesna, A., 2019. Inflacja bazowa w polityce pieniężnej – analiza w świetle modelu DSGE (Rozprawa doktorska). Uniwersytet Łódzki, Wydział Ekonomiczno-Socjologiczny.

Lewis, P.D., 2010. R for Medicine and Biology. Jones & Bartlett Publishers, Sudbury.

Li, H., Majerowska, E., 2008. Testing Stock Market Linkages for Poland and Hungary: A Multivariate GARCH Approach. Research in International Business and Finance 22(3), 247–266. https://doi.org/10.1016/j.ribaf.2007.04.001.

Lim, G.C., 2001. Bank Interest Rate Adjustments: Are They Asymmetric? Economic Record 77(237), 135–147. https://doi.org/10.1111/1475-4932.00009.

Lintner, J., 1965. The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics 47(1), 13–37. https://doi.org/10.2307/1924119.

Ljung, G.M., Box, G.E.P., 1978. On a Measure of Lack of Fit in Time Series Models. Biometrika 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297.

Lütkepohl, H., Krätzig, M. (Red.), 2004. Applied Time Series Econometrics. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511606885.

Łyziak, T., 2019. Do Global Output Gaps Help Forecast Domestic Inflation? Evidence from Phillips Curves for Poland. International Journal of Forecasting 35(3), 1032–1041. https://doi.org/10.1016/j.ijforecast.2019.03.006.

Łyziak, T., 2016a. Do Inflation Expectations Matter in a Stylised New Keynesian Model? The Case of Poland (NBP Working Paper No. 234), https://static.nbp.pl/publikacje/materialy-i-studia/234_en.pdf. Narodowy Bank Polski.

Łyziak, T., 2016b. Survey Measures of Inflation Expectations in Poland: Are They Relevant from the Macroeconomic Perspective? Baltic Journal of Economics 16(1), 33–52. https://doi.org/10.1080/1406099X.2016.1165402.

Maddala, G.S., 2006. Ekonometria. PWN, Warszawa.

Mailund, T., 2017. Advanced Object-Oriented Programming in R: Statistical Programming for Data Science, Analysis and Finance. Apress, New York, NY.

Majerowska, E., Spigarska, E., 2021. Influence of Selected Internal Factors on the Outputs of the Financial-Sector Companies Traded on the Warsaw Stock Exchange, w: Bilgin, M.H., Danis, H., Demir, E., Vale, S. (red.), Eurasian Economic Perspectives, Eurasian Studies w Business i Economics. Springer International Publishing, Cham, s. 193–204. https://doi.org/10.1007/978-3-030-63149-9_14.

Makhabel, B., 2015. Learning Data Mining with R. Packt Publishing, Birmingham. Marcinkowska, M., Wdowiński, P., Flejterski, S., Bukowski, S., Zygierewicz, M., 2014.

Wpływ regulacji sektora bankowego na wzrost gospodarczy – wnioski dla Polski. Materiały i Studia 305, 3–227.

Marin, J.-M., Robert, C.P., 2014. Bayesian Essentials with R. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8687-9.

Meschiari, S., 2022. latex2exp: Use LaTeX Expressions in Plots, R package version 0.9.6, https://www.stefanom.io/latex2exp/.

Milo, W. (Red.), 2002. Prognozowanie i symulacja. Wydawnictwo Uniwersytetu Łódzkiego, Łódź.

Milo, W., 1990. Szeregi czasowe. PWE, Warszawa.

Milo, W., Cieśluk, U., Zglińska-Pietrzak, A., 2001. Supply and Price Effects of Monetary and Fiscal Policy in Poland under Transition: An Econometric Analysis, w: Papazoglou, C., Pentecost, E.J. (red.), Exchange Rate Policies, Prices and Supply-Side

Response. Palgrave Macmillan, London. https://doi.org/10.1057/9780230554535_11.

Miłobędzki, P., 2020. Price Clustering in Stocks from the WIG 20 Index, w: Jajuga, K., Locarek-Junge, H., Orłowski, L.T., Staehr, K. (red.), Contemporary Trends and Challenges in Finance, Springer Proceedings w Business i Economics. Springer International Publishing, Cham, Switzerland, s. 169–177. https://doi.org/10.1007/ 978-3-030-43078-8_14.

Miłobędzki, P., 2010. The Term Structure of the Polish Interbank Rates. A Note on the Symmetry of their Reversion to the Mean. Dynamic Econometric Models 10, 83–95. https://doi.org/10.12775/DEM.2010.007.

Miłobędzki, P., 2009. Stock Price and Volume Relation at the Warsaw Stock Exchange, w: Milo, W., Szafrański, G., Wdowiński, P. (red.), Financial Markets: Principles of Modelling Forecasting and Decision-Making. University of Łódź Press, Łódź, s. 11–21.

Miłobędzki, P., 2008. O regresyjnych sposobach weryfikacji hipotezy oczekiwań struktury terminowej stóp procentowych na rynku depozytów międzybankowych w Polsce. Prace i Materiały Wydziału Zarządzania Uniwersytetu Gdańskiego (4), 67–84.

Miłobędzki, P., 2006. Asymmetry in the Adjustment of Main Capital Market Indices in Poland, w: Milo, W., Wdowiński, P. (red.), Financial Markets: Principles of Modelling Forecasting and Decision-Making. Wydawnictwo Uniwersytetu Łódzkiego, Łódź, s. 221–239.

Miłobędzki, P., Nowak, S., 2022. The Components of Bid-Ask Spread on the Warsaw Stock Exchange, w: Nguyen, D.K. (red.), Handbook of Banking and Finance in Emerging Markets. Edward Elgar Publishing, Cheltenham, UK, s. 131–151.

Montgomery, D.C., Jennings, C.L., Kulahci, M., 2015. Introduction to Time Series Analysis and Forecasting, 2nd ed. Wiley, Hoboken, NJ.

Müller, K., Wickham, H., 2023. tibble: Simple Data Frames, R package version 3.2.1, https://tibble.tidyverse.org/.

Munzert, S., Rubba, C., Meißner, P., Nyhuis, D., 2015. Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining. Wiley, Chichester.

Neuwirth, E., 2022. RColorBrewer: ColorBrewer Palettes, R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.

Nocedal, J., Wright, S.J., 2006. Numerical Optimization, 2nd ed. Springer, New York, NY. https://doi.org/10.1007/978-0-387-40065-5.

Ooms, J., 2025a. jsonlite: A Simple and Robust JSON Parser and Generator for R, R package version 2.0.0, https://jeroen.r-universe.dev/jsonlite.

Ooms, J., 2025b. writexl: Export Data Frames to Excel xlsx Format, R package version 1.5.4, https://ropensci.r-universe.dev/writexl.

OpenAI, 2023. ChatGPT, Large language model (Mar 14 version), https://chat.openai.com/.

Orlowski, L.T., 2001. From Inflation Targeting to the Euro-Peg: A Model of Monetary Convergence for Transition Economies. Economic Systems 25(3), 233–251. https://doi.org/10.1016/S0939-3625(01)00020-6.

Osińska, M., 2006. Ekonometria finansowa. PWE, Warszawa.

Osińska, M., 1999. Wpływ oczekiwań na kształtowanie się indeksów giełdowych na przykładzie WIG. Prace i Materiały Instytutu Rozwoju Gospodarczego SGH (64), 85–89.

Osińska, M., Pietrzak, M.B., Żurek, M., 2011. Ocena wpływu czynników behawioralnych i rynkowych na postawy inwestorów indywidualnych na polskim rynku kapitałowym za pomocą modelu SEM. Przegląd Statystyczny 58(3-4), 175–194.

Osowska, E., Wójcik, P., 2024. Predicting the Reaction of Financial Markets to Federal Open Market Committee Post-Meeting Statements. Digital Finance 6(1), 145–175. https://doi.org/10.1007/s42521-023-00096-8.

Pace, L., 2012. Beginning R: An Introduction to Statistical Programming. Apress, New York, NY.

Papadamou, S., Sidiropoulos, M., Spyromitros, E., 2015. Central Bank Transparency and the Interest Rate Channel: Evidence from Emerging Economies. Economic Modelling 48, 167–174. https://doi.org/10.1016/j.econmod.2014.10.016.

Pearson, E.S., D’Agostino, R.B., Bowman, K.O., 1977. Tests for Departure from Normality: Comparison of Powers. Biometrika 64(2), 231–246. https://doi.org/10.1093/biomet/64.2.231.

Persson, E., 2025. ecb: Programmatic Access to the European Central Bank’s Data Portal, R package version 0.4.3, https://github.com/expersso/ecb.

Pfaff, B., 2008. Analysis of Integrated and Cointegrated Time Series with R, 2nd ed, http://www.pfaffikus.de. Springer, New York, NY.

Plummer, M., Best, N., Cowles, K., Vines, K., Sarkar, D., Bates, D., Almond, R., Magnusson, A., 2024. coda: Output Analysis and Diagnostics for MCMC, R package version 0.19-4.1, https://CRAN.R-project.org/package=coda.

Przybyliński, M., Gorzałczyński, A., 2016. Zastosowanie tablic przepływów międzygałęziowych do modelowania procesów inflacyjnych. Gospodarka w Praktyce i Teorii 42(1), 49–60. https://doi.org/10.18778/1429-3730.42.04.

R Core Team, 2025. R: A Language and Environment for Statistical Computing, https://www.R-project.org/. R Foundation for Statistical Computing, Vienna, Austria.

Ramsey, J.B., 1969. Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis. Journal of the Royal Statistical Society. Series B (Methodological) 31(2), 350–371. https://doi.org/10.1111/j.2517-6161.1969.tb00796.x.

Ren, K., 2021. rlist: A Toolbox for Non-Tabular Data Manipulation, R package version 0.4.6.2, https://renkun-ken.github.io/rlist/.

Robert, C.P., Casella, G., 2010. Introducing Monte Carlo Methods with R. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1576-4.

Robinson, D., Silge, J., 2024. tidytext: Text Mining using dplyr, ggplot2, and Other Tidy Tools, R package version 0.4.2, https://juliasilge.github.io/tidytext/.

Royston, P., 1995. Remark AS R94: A Remark on Algorithm AS 181: The W-test for Normality. Journal of the Royal Statistical Society: Series C (Applied Statistics) 44(4), 547–551. https://doi.org/10.2307/2986146.

Rubaszek, M., 2016. Forecasting the Yield Curve With Macroeconomic Variables. Econometric Research in Finance 1(1), 1–21.

Rubaszek, M., 2012. Modelowanie polskiej gospodarki z pakietem R. Oficyna Wydawnicza SGH, Warszawa.

Rubaszek, M., Skrzypczyński, P., 2008. On the Forecasting Performance of a SmallScale DSGE Model. International Journal of Forecasting 24(3), 498–512. https://doi.org/10.1016/j.ijforecast.2008.05.002.

Ryan, J.A., Ulrich, J.M., 2024. xts: eXtensible Time Series, R package version 0.14.0, https://joshuaulrich.github.io/xts/.

Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., Elberg, A., Crowley, J., 2024. GGally: Extension to ggplot2, R package version 2.2.1, https://ggobi.github.io/ggally/.

Schumacker, R.E., Tomek, S.R., 2013. Understanding Statistics Using R. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6227-9.

Schwarz, G., 1978. Estimating the Dimension of a Model. The Annals of Statistics 6(2), 461–464. https://doi.org/10.1214/aos/1176344136.

Serwa, D., Wdowiński, P., 2017. Modeling Macro-Financial Linkages: Combined Impulse Response Functions in SVAR Models. Central European Journal of Economic Modelling and Econometrics 9(4), 323–357. https://doi.org/10.2139/ssrn.2845737.

Shapiro, S.S., Wilk, M.B., 1965. An Analysis of Variance Test for Normality (Complete Samples). Biometrika 52(3-4), 591–611. https://doi.org/10.1093/biomet/52.3-4.591.

Sharpe, W.F., 1964. Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. The Journal of Finance 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x.

Sharpe, W.F., 1963. A Simplified Model for Portfolio Analysis. Management Science 9(2), 277–293. https://doi.org/10.1287/mnsc.9.2.277.

Sievert, C., 2020. Interactive Web-Based Data Visualization with R, plotly, and shiny. CRC Press, Boca Raton, FL.

Sievert, C., Parmer, C., Hocking, T., Chamberlain, S., Ram, K., Corvellec, M., Despouy, P., 2024. plotly: Create Interactive Web Graphics via plotly.js, R package version 4.10.4, https://plotly-r.com.

Signorell, A., 2024. DescTools: Tools for Descriptive Statistics, R package version 0.99.55, https://andrisignorell.github.io/DescTools/.

Smets, F., 1995. Central Bank Macroeconometric Models and the Monetary Policy Transmission Mechanism (BIS Conference Paper No. 394), Included in BIS Conference Volume No. 394: Financial Structure and the Monetary Policy Transmission Mechanism, March 1995, pp. 225–266, https://www.bis.org/publ/conf394.pdf. Bank for International Settlements, Basel.

Smets, F., Wouters, R., 2007. Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review 97(3), 586–606. https://doi.org/10.1257/aer.97.3.586.

Spanos, A., 1986. Statistical Foundations of Econometric Modelling. Cambridge University Press, New York, NY.

Spector, P., 2008. Data Manipulation with R. Springer, New York. https://doi.org/10.100 7/978-0-387-74731-6.

Stelmasiak, D., Szafrański, G., 2016. Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality. Central European Journal of Economic Modelling and Econometrics 8(1), 21–42. https://doi.org/10.24425/cejeme.2016.119185.

Stock, J.H., Watson, M.W., 2001. Vector Autoregressions. Journal of Economic Perspectives 15(4), 101–115. https://doi.org/10.1257/jep.15.4.101.

Strawiński, P., Ślepaczuk, R., 2008. Analysis of High-Frequency Data on the Warsaw Stock Exchange in the Context of Efficient Market Hypothesis. Journal of Applied Economic Sciences 3(5), 306–319.

Suess, E.A., Trumbo, B.E., 2010. Introduction to Probability Simulation and Gibbs Sampling with R. Springer, New York. https://doi.org/10.1007/978-0-387-68765-0.

Szafranek, K., Hałka, A., 2017. Determinants of Low Inflation in an Emerging, Small Open Economy: A Comparison of Aggregated and Disaggregated Approaches (NBP Working Paper No. 267), https://static.nbp.pl/publikacje/materialy-i-studia/267_en.pdf. Narodowy Bank Polski.

Szafranek, K., Szafrański, G., Leszczyńska-Paczesna, A., 2024. Inflation returns. Revisiting the role of external and domestic shocks with Bayesian structural VAR. International Review of Economics & Finance 93, 789–810. https://doi.org/10.101 6/j.iref.2024.03.054.

Szafrański, G., 2013. Syntetyczne wskaźniki wyprzedzające w prognozowaniu inflacji w Polsce. Przegląd Statystyczny 60(3), 395–416. https://doi.org/10.59139/ps.2013.0 3.7.

Sznajderska, A., 2013. On the Empirical Evidence of Asymmetric Effects in the Polish Interest Rate Pass-Through. The Journal of Economic Asymmetries 10(2), 78–93. https://doi.org/10.1016/j.jeca.2013.11.001.

Taylor, J.B., 1995. The Monetary Transmission Mechanism: An Empirical Framework. Journal of Economic Perspectives 9(4), 11–26. https://doi.org/10.1257/jep.9.4.11.

Teetor, P., 2011. R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. O’Reilly Media, Sebastopol, CA.

Trapletti, A., Hornik, K., 2024. tseries: Time Series Analysis and Computational Finance, R package version 0.10-58, https://CRAN.R-project.org/package=tseries.

Tsay, R.S., 2014. Multivariate Time Series Analysis: With R and Financial Applications. Wiley, Hoboken, NJ.

Tsay, R.S., 2010. Analysis of Financial Time Series, 3rd ed, Wiley Series w Probability i Statistics. Wiley, Hoboken, NJ.

Turlach, B.A., 2019. quadprog: Functions to Solve Quadratic Programming Problems, R package version 1.5-8, https://CRAN.R-project.org/package=quadprog.

Ugarte, M.D., Militino, A.F., Arnholt, A.T., 2015. Probability and Statistics with R. Chapman & Hall/CRC, Boca Raton, FL.

Vasicek, O., 1977. An Equilibrium Characterization of the Term Structure. Journal of Financial Economics 5(2), 177–188. https://doi.org/10.1016/0304-405X(77)90016-2.

Verbeek, M., 2017. A Guide to Modern Econometrics, 5th ed. Wiley, Hoboken, NJ.

Verzani, J., 2014. Using R for Introductory Statistics, 2nd ed. Chapman & Hall/CRC, Boca Raton, FL.

Wdowiński, P., 2020. Wstęp do programowania i analizy danych w języku R. Wydawnictwo Uniwersytetu Łódzkiego, Łódź. https://doi.org/10.18778/8220-278-6.

Wdowiński, P., 2010. Modele kursów walutowych. Wydawnictwo Uniwersytetu Łódzkiego, Łódź. https://doi.org/10.18778/7525-466-2.

Wdowiński, P., 2004. Determinants of Country Beta Risk in Poland (No. 1120), CESifo Working Paper, https://www.cesifo.org/en/publikationen/2004/working-paper/determinants-country-beta-risk-poland. Center for Economic Studies; ifo Institute (CESifo), Munich.

Wdowiński, P., 2002. Wielorównaniowy model cen i płac przeciętnych: analiza symulacyjna, w: Milo, W. (red.), Prognozowanie i symulacja. Wydawnictwo Uniwersytetu Łódzkiego, Łódź.

Wdowiński, P., Małecka, M., 2010. Asymmetry in Volatility: A Comparison of Developed and Transition Stock Markets (No. 2974), CESifo Working Paper, https://hdl.handle.net/10419/39039. Center for Economic Studies; ifo Institute (CESifo), Munich.

Wdowiński, P., Wrzesiński, D., 2004. Czynniki ryzyka rynku kapitałowego w Polsce. Acta Universitatis Lodziensis. Folia Oeconomica (177), 23–41.

Wdowiński, P., Zglińska-Pietrzak, A., 2005. The Warsaw Stock Exchange Index WIG: Modelling and Forecasting, w: Issues in Modeling, Forecasting and DecisionMaking in Financial Markets, Acta Universitatis Lodziensis. Folia Oeconomica. Wydawnictwo Uniwersytetu Łódzkiego, Łódź, s. 115–127.

Wdowiński, P., Zglińska-Pietrzak, A., Tomasik, J., 1997. Kilka uwag na temat interpretacji parametrów strukturalnych modelu ekonometrycznego, w: Milo, W. (red.), Badania ekonometryczne. Wydawnictwo Uniwersytetu Łódzkiego, Łódź, s. 125–135.

Welfe, A., 2000. Modeling Inflation in Poland. Economic Modelling 17(3), 375–385. https://doi.org/10.1016/S0264-9993(99)00044-9.

Welfe, A., Karp, P., Kębłowski, P., 2004. Modelling Polish Economy: An Application of SVEqCM, w: Handbook of Comparative Economic Policies. s. 557–602. https://doi.org/10.1016/S0573-8555(04)69008-0.

Welfe, A., Majsterek, M., 2002. Wage and Price Inflation in Poland in the Period of Transition: The Cointegration Analysis. Economics of Planning 35, 205–219. https://doi.org/10.1023/A:1022420518609.

Welfe, A., Majsterek, M., 2001. Długookresowy model sprzężenia inflacyjnego w gospodarce polskiej okresu transformacji. Ekonomista (5), 619–633.

Wesołowski, G., 2016. Do Long Term Interest Rates Drive GDP and Inflation in Small Open Economies? Evidence from Poland (NBP Working Paper No. 242), https://static.nbp.pl/publikacje/materialy-i-studia/242_en.pdf. Narodowy Bank Polski.

White, H., 1980. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica 48(4), 817–838. https://doi.org/10.2307/1912934.

Wickham, H., 2024. rvest: Easily Harvest (Scrape) Web Pages, R package version 1.0.4, https://rvest.tidyverse.org/.

Wickham, H., 2023a. forcats: Tools for Working with Categorical Variables (Factors), R package version 1.0.0, https://forcats.tidyverse.org/.

Wickham, H., 2023d. httr: Tools for Working with URLs and HTTP, R package version 1.4.7, https://httr.r-lib.org/.

Wickham, H., 2023c. stringr: Simple, Consistent Wrappers for Common String Operations, R package version 1.5.1, https://stringr.tidyverse.org.

Wickham, H., 2023b. tidyverse: Easily Install and Load the Tidyverse, R package version 2.0.0, https://tidyverse.tidyverse.org.

Wickham, H., 2020. reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package, R package version 1.4.4, https://github.com/hadley/reshape.

Wickham, H., 2019. Advanced R, 2nd ed, Chapman & Hall/CRC The R Series. Chapman & Hall/CRC, Boca Raton, FL.

Wickham, H., Bryan, J., 2023. readxl: Read Excel Files, R package version 1.4.3, https://readxl.tidyverse.org.

Wickham, H., Çetinkaya-Rundel, M., Grolemund, G., 2023a. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd ed. O’Reilly Media, Sebastopol, California.

Wickham, H., Chang, W., Henry, L., Pedersen, T.L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., Dunnington, D., van den Brand, T., 2024a. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics, R package version 3.5.1, https://ggplot2.tidyverse.org.

Wickham, H., François, R., Henry, L., Müller, K., Vaughan, D., 2023b. dplyr: A Grammar of Data Manipulation, R package version 1.1.4, https://dplyr.tidyverse.org.

Wickham, H., Grolemund, G., 2016. R for Data Science: Visualize, Model, Transform, Tidy, and Import Data. O’Reilly Media, Sebastopol, CA.

Wickham, H., Henry, L., 2023. purrr: Functional Programming Tools, R package version 1.0.2, https://purrr.tidyverse.org/.

Wickham, H., Hester, J., Bryan, J., 2024b. readr: Read Rectangular Text Data, R package version 2.1.5, https://readr.tidyverse.org.

Wickham, H., Hester, J., Chang, W., Bryan, J., 2022. devtools: Tools to Make Developing R Packages Easier, R package version 2.4.5, https://devtools.r-lib.org/.

Wickham, H., Vaughan, D., Girlich, M., 2024c. tidyr: Tidy Messy Data, R package version 1.3.1, https://tidyr.tidyverse.org.

Wiley, G.M., Wiley, J.F., 2016. Advanced R: Data Programming and the Cloud. Apress, New York, NY.

Wirth, N., 1989. Algorytmy + struktury danych = programy. Wydawnictwa Naukowo-Techniczne, Warszawa.

Wooldridge, J.M., 2020. Introductory Econometrics: A Modern Approach, 7th ed. Cengage, Boston, MA.

Wójcik, S., 2015. Prognozowanie inflacji w Polsce na podstawie modeli autoregresji wektorowej. Wiadomości Statystyczne 60(1), 28–41. https://doi.org/10.59139/ws.2015.01.3.

Wu, L., 2024. Interest Rate Modeling: Theory and Practice, 3rd ed, Chapman & Hall/CRC Financial Mathematics Series. Chapman & Hall/CRC, Boca Raton, FL. https://doi.or g/10.1201/9781003389101.

Xie, Y., 2025a. bookdown: Authoring Books and Technical Documents with R Markdown, R package version 0.45, https://github.com/rstudio/bookdown.

Xie, Y., 2025b. knitr: A General-Purpose Package for Dynamic Report Generation in R, R package version 1.50, https://yihui.org/knitr/.

Xie, Y., 2025c. tinytex: Helper Functions to Install and Maintain TeX Live, and Compile LaTeX Documents, R package version 0.58, https://github.com/rstudio/tinytex.

Xie, Y., 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Chapman & Hall/CRC, Boca Raton, FL.

Xie, Y., 2015. Dynamic Documents with R and knitr. Chapman & Hall/CRC, Boca Raton, Florida.

Xie, Y., Allaire, J.J., Grolemund, G., 2018. R Markdown: The Definitive Guide, https://bookdown.org/yihui/rmarkdown. Chapman & Hall/CRC, Boca Raton, FL.

Zeileis, A., Grothendieck, G., Ryan, J.A., 2023. zoo: S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations), R package version 1.8-12, https://zoo.R-Forge.R-project.org/.

Zhang, G.P., Patuwo, B.E., Hu, M.Y., 1998. Forecasting with Artificial Neural Networks: The State of the Art. International Journal of Forecasting 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7.

Zhu, H., 2025. kableExtra: Construct Complex Table with kable and Pipe Syntax, R package version 1.4.0.12, http://haozhu233.github.io/kableExtra/.

Ziarko-Siwek, U., Kamiński, M., 2003. Empiryczna weryfikacja teorii oczekiwań terminowej struktury stóp procentowych w Polsce (No. 159), Materiały i Studia. Narodowy Bank Polski, Warszawa.

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Wdowiński, Piotr. 2025. Liniowe Modele Ekonometryczne. Zaawansowane Zastosowania języka R. Poland: Wydawnictwo Uniwersytetu Łódzkiego. https://doi.org/10.18778/8331-975-9.