Modelowanie rozkładu cen skanowanych i indeksów cen za pomocą rozkładów teoretycznych z dwoma, trzema, czterema i pięcioma parametrami

Autor

DOI:

https://doi.org/10.18778/0208-6018.366.02

Słowa kluczowe:

modelowanie danych, dane skanowane, rozkłady cen

Abstrakt

W artykule podjęto problematykę odpowiedniego dopasowania teoretycznego rozkładu prawdopodobieństwa do empirycznego rozkładu cen skanerów. W badaniu empirycznym wykorzystano dane skanerowe z jednej sieci handlowej w Polsce, tj. miesięczne dane dotyczące jogurtów naturalnych, napojów jogurtowych, ryżu długoziarnistego i kawy w proszku, sprzedanych w 212 placówkach w styczniu i lutym 2022 roku. Ceny i ceny względne modelowano za pomocą pięćdziesięciu dwu‑, trzy‑, cztero‑ i pięcioparametrowych rozkładów prawdopodobieństwa z nieujemną dziedziną. Niektóre z nich składały się z dość znanych rozkładów, które nazywane są ich specjalnymi przypadkami. Łączna liczba tych rozkładów, które pośrednio wzięły udział w badaniu, to ponad sto. Do analizy porównawczej wykorzystywano kryteria informacyjne, takie jak AIC, BIC, HQIC i wartości p testów dobroci dopasowania. W artykule wykazano, że modele takie jak Frechet, Pareto IV  i Log‑Logistic można uznać za bardzo dokładne, co stanowi dobrą podstawę do badań symulacyjnych wskaźników cen czy też konstrukcji tzw. wskaźników cen ludności. Wzory na dystrybuantę wykorzystanych modeli oraz kody R niezbędne do przeprowadzenia badań przedstawiono w załączniku.

Pobrania

Brak dostępnych danych do wyświetlenia.

Bibliografia

Abdollahi Nanvapisheh A. (2019), A New Five Parameter Distribution: Properties and Applications, “International Journal of Mathematical Modelling and Computations”, vol. 9(3), pp. 201–212.
Google Scholar

Akaike H. (1974), A new look at the statistical model identification, “IEEE Transactions on Automatic Control”, vol. 19(6), pp. 716–723.
Google Scholar DOI: https://doi.org/10.1109/TAC.1974.1100705

Al Babtain A., Eid A.M., Ahmed A.H.N., Merovci F. (2015), The five parameter Lindley distribution, “Pakistan Journal of Statistics”, vol. 31(4).
Google Scholar DOI: https://doi.org/10.1186/2193-1801-4-2

Awodutire P. (2020), Chen Pareto Distribution: Properties and Application, “Pakistan Journal of Statistics and Operation Research”, vol. 16(4), pp. 812–826.
Google Scholar DOI: https://doi.org/10.18187/pjsor.v16i4.3418

Bakouch H.S., Saboor A., Khan M.N. (2021), Modified beta linear exponential distribution with hydrologic applications, “Annals of Data Science”, no. 8, pp. 131–157.
Google Scholar DOI: https://doi.org/10.1007/s40745-019-00222-7

Barreto Souza W., Morais A.L. de, Cordeiro G.M. (2011), The Weibull geometric distribution, “Journal of Statistical Computation and Simulation”, vol. 81(5), pp. 645–657.
Google Scholar DOI: https://doi.org/10.1080/00949650903436554

Barreto Souza W., Santos A.H., Cordeiro G.M. (2010), The beta generalized exponential distribution, “Journal of Statistical Computation and Simulation”, vol. 80(2), pp. 159–172.
Google Scholar DOI: https://doi.org/10.1080/00949650802552402

Bebbington M., Lai C.D., Zitikis R. (2007), A flexible Weibull extension, “Reliability Engineering and System Safety”, vol. 92(6), pp. 719–726.
Google Scholar DOI: https://doi.org/10.1016/j.ress.2006.03.004

Bemmaor A.C. (1994), Modeling the diffusion of new durable goods: Word of mouth effect versus consumer heterogeneity, [in:] G. Laurent, G.L. Lilien, B. Pras (eds.), Research Traditions in Marketing, Kluwer, Boston, pp. 201–229.
Google Scholar DOI: https://doi.org/10.1007/978-94-011-1402-8_6

Białek J. (2015), Construction of confidence intervals for the Laspeyres price index, “Journal of Statistical Computation and Simulation”, vol. 85(14), pp. 2962–2973.
Google Scholar DOI: https://doi.org/10.1080/00949655.2014.946416

Białek J. (2022), Elementary price indices under the GBM price model, “Communications in Statistics – Theory and Methods”, vol. 51(5), pp. 1232–1251.
Google Scholar DOI: https://doi.org/10.1080/03610926.2021.1938127

Białek J., Beręsewicz M. (2021), Scanner data in inflation measurement: from raw data to price indices, “The Statistical Journal of the IAOS”, no. 37, pp. 1315–1336.
Google Scholar DOI: https://doi.org/10.3233/SJI-210816

Białek J., Bobel A. (2019), Comparison of price index methods for CPI measurement using scanner data, 16th Meeting of the Ottawa Group on Price Indices, Rio de Janeiro.
Google Scholar

Birnbaum Z.W., Saunders S.C. (1969), A new family of life distributions, “Journal of Applied Probability”, vol. 6(2), pp. 637–652.
Google Scholar DOI: https://doi.org/10.2307/3212003

Bourguignon M., Lima M.D.C.S., Leão J., Nascimento A.D., Pinho L.G.B., Cordeiro G.M. (2015), A new generalized gamma distribution with applications, “American Journal of Mathematical and Management Sciences”, vol. 34(4), pp. 309–342.
Google Scholar DOI: https://doi.org/10.1080/01966324.2015.1040178

Brandt S. (2014), Data analysis, Springer International Publishing, Switzerland.
Google Scholar

Brazauskas V . (2003), Information matrix for Pareto (IV), Burr, and related distributions, “Communications in Statistics Theory and Methods”, vol. 32(2), pp. 315–325.
Google Scholar DOI: https://doi.org/10.1081/STA-120018188

Carli G. (1804), Del valore e della proporzione de’metalli monetati, “Scrittori Classici Italiani di Economia Politica”, no. 13, pp. 297–336.
Google Scholar

Carrasco J.M., Ortega E.M., Cordeiro G.M. (2008), A generalized modified Weibull distribution for lifetime modelling, “Computational Statistics and Data Analysis”, vol. 53(2), pp. 450–462.
Google Scholar DOI: https://doi.org/10.1016/j.csda.2008.08.023

Castillo E., Hadi A.S., Balakrishnan N., Sarabia J.S. (2005), Extreme Value and Related Models with Applications in Engineering and Science, Wiley Interscience, Hoboken.
Google Scholar

Chen Z. (2000), A new two parameter lifetime distribution with bathtub shape or increasing failure rate function, “Statistics and Probability Letters”, no. 49, pp. 155–161.
Google Scholar DOI: https://doi.org/10.1016/S0167-7152(00)00044-4

Chesneau C., Bakouch H.S., Hussain T. (2018), A new class of probability distributions via cosine and sine functions with applications, “Communications in Statistics Simulation and Computation”, vol. 48(8), pp. 2287–2300.
Google Scholar DOI: https://doi.org/10.1080/03610918.2018.1440303

Chhikara R.S., Folks J.L. (1989), The Inverse Gaussian Distribution: Theory, Methodology and Applications, Marcel Dekker, New York.
Google Scholar

Cooray K. (2006), Generalization of the Weibull distribution: The odd Weibull family, “Statistical Modelling”, vol. 6(3), pp. 265–277.
Google Scholar DOI: https://doi.org/10.1191/1471082X06st116oa

Cordeiro G.M., Ortega E.M., Silva G.O. (2011), The exponentiated generalized gamma distribution with application to lifetime data, “Journal of Statistical Computation and Simulation”, vol. 81(7), pp. 827–842.
Google Scholar DOI: https://doi.org/10.1080/00949650903517874

Cordeiro G.M., Castellares F., Montenegro L.C., Castro M. de (2013), The beta generalized gamma distribution, “Statistics”, vol. 47(4), pp. 888–900.
Google Scholar DOI: https://doi.org/10.1080/02331888.2012.658397

Drapella A. (1993), The complementary Weibull distribution: unknown or just forgotten?, “Quality and Reliability Engineering International”, vol. 9(4), pp. 383–385.
Google Scholar DOI: https://doi.org/10.1002/qre.4680090426

Dutot C.F. (1738), Reflexions Politiques sur les Finances et le Commerce, Les Freres, The Hague.
Google Scholar

El Gohary A., Alshamrani A., Al Otaibi A.N. (2013), The generalized Gompertz distribution, “Applied Mathematical Modelling”, vol. 37(1–2), pp. 13–24.
Google Scholar DOI: https://doi.org/10.1016/j.apm.2011.05.017

El Gohary A., El Bassiouny A.H., El Morshedy M. (2015), Inverse flexible Weibull extension distribution, “International Journal of Computer Applications”, vol. 115(2), pp. 46–51.
Google Scholar DOI: https://doi.org/10.5120/20127-2211

Eltehiwy M., Ashour S. (2013), Transmuted Exponentiated Modified Weibull Distribution, “International Journal of Basic and Applied Sciences”, vol. 2(3), pp. 258–269.
Google Scholar DOI: https://doi.org/10.14419/ijbas.v2i3.1074

Felipe R.Sd.G., Edwin M.M.O, Gauss M.C. (2009), The generalized inverse Weibull distribution, “Statistical Papers”, vol. 52(3), pp. 591–619.
Google Scholar DOI: https://doi.org/10.1007/s00362-009-0271-3

Gaddum J.H. (1945), Lognormal distributions, “Nature”, vol. 156(3964), pp. 463–466.
Google Scholar DOI: https://doi.org/10.1038/156463a0

Ghitany M.E., Al Hussaini E.K., Al Jarallah R.A. (2005), Marshall–Olkin extended Weibull distribution and its application to censored data, “Journal of Applied Statistics”, vol. 32(10), pp. 1025–1034.
Google Scholar DOI: https://doi.org/10.1080/02664760500165008

Ghitany M.E., Al Mutairi D.K., Balakrishnan N., Al Enezi L.J. (2013), Power Lindley distribution and associated inference, “Computational Statistics and Data Analysis”, no. 64, pp. 20–33.
Google Scholar DOI: https://doi.org/10.1016/j.csda.2013.02.026

Gumbel E.J. (1958), Statistics of Extremes, Columbia University Press, New York.
Google Scholar DOI: https://doi.org/10.7312/gumb92958

Hannan E.J., Quinn B.G. (1979), The determination of the order of an autoregression, “Journal of the Royal Statistical Society: Series B (Methodological)”, vol. 41(2), pp. 190–195.
Google Scholar DOI: https://doi.org/10.1111/j.2517-6161.1979.tb01072.x

Javed M., Nawaz T., Irfan M. (2019), The Marshall Olkin kappa distribution: properties and applications, “Journal of King Saud University Science”, vol. 31(4), pp. 684–691.
Google Scholar DOI: https://doi.org/10.1016/j.jksus.2018.01.001

Jevons W.S. (1865), The variation of prices and the value of the currency since 1782, “Journal of the Statistical Society of London”, no. 28, pp. 294–320.
Google Scholar DOI: https://doi.org/10.2307/2338419

Jędrzejczak A., Pekasiewicz D. (2020), Teoretyczne rozkłady dochodów gospodarstw domowych i ich estymacja, Wydawnictwo Uniwersytetu Łódzkiego, Łódź.
Google Scholar DOI: https://doi.org/10.18778/8142-899-6

Johnson N.L., Kotz S., Balakrishnan N. (1995), Continuous univariate distributions, vol. 2, John Wiley & Sons, New York.
Google Scholar

Kleiber C., Kotz S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley Interscience, Hoboken.
Google Scholar DOI: https://doi.org/10.1002/0471457175

Kotz S., Nadarajah S. (2000), Extreme Value Distributions: Theory and Applications, Imperial College Press, London.
Google Scholar DOI: https://doi.org/10.1142/9781860944024

Lindsey J.K. (2004), Statistical analysis of stochastic processes in time, vol. 14, Cambridge University Press, Cambridge.
Google Scholar DOI: https://doi.org/10.1017/CBO9780511617164

Lu W., Shi D. (2012), A new compounding life distribution: the Weibull–Poisson distribution, “Journal of Applied Statistics”, vol. 39(1), pp. 21–38.
Google Scholar DOI: https://doi.org/10.1080/02664763.2011.575126

Mahdavi A (2015), Two Weighted Distributions Generated by Exponential Distribution, “Journal of Mathematical Extension”, vol. 9(1), pp. 1–12.
Google Scholar

McDonald J.B. (1984), Some generalized functions for the size distribution of income, “Econometrica”, vol. 52(3), pp. 647–663.
Google Scholar DOI: https://doi.org/10.2307/1913469

Nadarajah S., Rocha R. (2016), Newdistns: An R package for new families of distributions, “Journal of Statistical Software”, no. 69, pp. 1–32.
Google Scholar DOI: https://doi.org/10.18637/jss.v069.i10

Nakagami M. (1960), The m Distribution – A General Formula of Intensity Distribution of Rapid Fading, [in:] W.C. Hoffman (ed.), Statistical Methods in Radio Wave Propagation, Pergamon, Oxford, pp. 3–36.
Google Scholar DOI: https://doi.org/10.1016/B978-0-08-009306-2.50005-4

Okasha H.M., El Baz A.H., Tarabia A.M.K., Basheer A.M. (2017), Extended inverse Weibull distribution with reliability application, “Journal of the Egyptian Mathematical Society”, vol. 25(3), pp. 343–349.
Google Scholar DOI: https://doi.org/10.1016/j.joems.2017.02.006

Pal M., Tiensuwan M. (2015), Exponentiated transmuted modified Weibull distribution, “European Journal of Pure and Applied Mathematics”, vol. 8(1), pp. 1–14.
Google Scholar

R Core Team (2021), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, https://www.R project.org/ [accessed: 24.11.2023].
Google Scholar

Sarhan A.M., Apaloo J. (2013), Exponentiated modified Weibull extension distribution, “Reliability Engineering and System Safety”, no. 112, pp. 137–144.
Google Scholar DOI: https://doi.org/10.1016/j.ress.2012.10.013

Sarhan A.M., Zaindin M. (2009), Modified Weibull distribution, “APPS. Applied Sciences”, no. 11, pp. 123–136.
Google Scholar

Schwarz G. (1978), Estimating the dimension of a model, “The Annals of Statistics”, vol. 6(2), pp. 461–464.
Google Scholar DOI: https://doi.org/10.1214/aos/1176344136

Shahbaz M.Q., Shahbaz S., Butt N.S. (2012), The Kumaraswamy–Inverse Weibull Distribution, “Pakistan Journal of Statistics and Operation Research”, vol. 8(3), pp. 479–489.
Google Scholar DOI: https://doi.org/10.18187/pjsor.v8i3.520

Shanker S., Shukla K.K. (2019), A generalization of Generalized Gamma distribution, “International Journal of Computational and Theoretical Statistics”, vol. 6(1), pp. 33–42.
Google Scholar

Silver H., Heravi S. (2007), Why elementary price index number formulas differ: Evidence on price dispersion, “Journal of Econometrics”, no. 140, pp. 874–883.
Google Scholar DOI: https://doi.org/10.1016/j.jeconom.2006.07.017

Stacy E.W., Mihram G.A. (1965), Parameter estimation for a generalized gamma distribution, “Technometrics”, vol. 7(3), pp. 349–358.
Google Scholar DOI: https://doi.org/10.1080/00401706.1965.10490268

Subhradev S., Mustafa C.K., Haitham M.Y. (2018), The Quasi XGamma Poisson distribution: Properties and Application, “Istatistik: Journal of the Turkish Statistical Assocation”, vol. 11(3), pp. 65–76.
Google Scholar

Sulewski P., Białek J. (2022), Probability Distribution Modelling of Scanner Prices and Relative Prices, “Statistika: Statistics & Economy Journal”, vol. 102(3).
Google Scholar DOI: https://doi.org/10.54694/stat.2022.14

Tieling Z., Min X . (2007), Failure Data Analysis with Extended Weibull Distribution, “Communications in Statistics – Simulation and Computation”, no. 36, pp. 579–592.
Google Scholar DOI: https://doi.org/10.1080/03610910701236081

Witkovsky V . (2001), Computing the distribution of a linear combination of inverted gamma variables, “Kybernetika”, vol. 37(1), pp. 79–90.
Google Scholar

Yusuf A., Qureshi S. (2019), A five parameter statistical distribution with application to real data, “Journal of Statistics Applications and Probability Letters”, no. 8, pp. 11–26.
Google Scholar DOI: https://doi.org/10.18576/jsap/080102

Opublikowane

2024-06-20

Jak cytować

Sulewski, P. (2024). Modelowanie rozkładu cen skanowanych i indeksów cen za pomocą rozkładów teoretycznych z dwoma, trzema, czterema i pięcioma parametrami. Acta Universitatis Lodziensis. Folia Oeconomica, 23–61. https://doi.org/10.18778/0208-6018.366.02

Numer

Dział

Artykuł

Podobne artykuły

<< < 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 > >> 

Możesz również Rozpocznij zaawansowane wyszukiwanie podobieństw dla tego artykułu.