Khung dự đoán trong mô hình độ trễ phân phối với hàm mục tiêu: ứng dụng cho dữ liệu biến đổi khí hậu toàn cầu

Environmental and Ecological Statistics - Tập 28 - Trang 87-134 - 2021
Nimet Özbay1, Selma Toker1
1Department of Statistics, Çukurova University, Adana, Turkey

Tóm tắt

Do bản chất của mô hình độ trễ phân phối, các nhà nghiên cứu có thể gặp phải vấn đề đa cộng tuyến trong mô hình này. Các kỹ thuật ước lượng có thiên lệch, trong đó có ước lượng Almon ridge, được xem xét như một sự thay thế cho ước lượng Almon với mục tiêu phục hồi đa cộng tuyến. Mặc dù hiệu suất ước lượng thường được xem xét, nhưng hiệu suất dự đoán hiếm khi được xử lý trong mô hình độ trễ phân phối. Mục tiêu chính của bài báo này là điều tra hiệu suất dự đoán của mô hình độ trễ phân phối thông qua hàm mục tiêu. Trong bối cảnh này, chúng tôi sử dụng ước lượng Almon ridge để xác định một biến dự đoán mới có tính chống chịu cao hơn với đa cộng tuyến. Để phân tích một cách toàn diện về vấn đề dự đoán trong mô hình độ trễ phân phối, chúng tôi tập trung vào các kết quả lý thuyết và so sánh. Sau đó, vấn đề xác định các tham số tối ưu được xem xét thông qua việc giảm thiểu sai số bình phương dự đoán. Phân tích số liệu dựa trên dữ liệu biến đổi khí hậu toàn cầu được xem xét để xác thực các kết quả lý thuyết của chúng tôi. Hơn nữa, một thí nghiệm Monte Carlo được thực hiện để đánh giá khả năng dự đoán của các ước lượng.

Từ khóa

#mô hình độ trễ phân phối #đa cộng tuyến #ước lượng Almon ridge #hiệu suất dự đoán #hàm mục tiêu #sai số bình phương dự đoán

Tài liệu tham khảo

Ali HS, Law SH, Zannah TI (2016) Dynamic impact of urbanization, economic growth, energy consumption, and trade openness on CO2 emissions in Nigeria. Environ Sci Pollut Res 23(12):12435–12443 Alkhamisi M, Shukur G (2008) Developing ridge parameters for SUR model. Commun Stat 37(4):544–564 Almon S (1965) The distributed lag between capital appropriations and expenditures. Econometrica 33(1):178–196 Baltagi BH (2011) Econometrics, 5th edn. Springer, New York Benarde MA (1992) Global warming. Wiley, New York Benitez Gilabert M, Alvarez Cobelas M, Angeler DG (2010) Effects of climatic change on stream water quality in Spain. Environ Sci Pollut Res 103(3):339–352 Boucher O, Reddy MS (2008) Climate trade-off between black carbon and carbon dioxide emissions. Energy Policy 36(1):193–200 Broecker WS (2012) The carbon cycle and climate chance: memories of my 60 years in science. Geochem Perspect 1(2):221–340 Chanda AK, Maddala GS (1984) Ridge estimators for distributed lag models. Commun Stat 13(2):217–225 Chaturvedi A, Shalabh, (2014) Bayesian estimation of regression coefficients under extended balanced loss function. Commun Stat 43:4253–4264 De Laat ATJ, Maurellis AN (2004) Industrial CO2 emissions as a proxy for anthropogenic influence on lower tropospheric temperature trends. Geophys Res Lett 31:05204 Diniz CAR, Rodrigues CP, Leite JG, Pires RM (2014) A Bayesian estimation of lag lengths in distributed lag models. J Stat Comput Simul 84(2):415–427 Fisher I (1937) Income in theory and income taxation practice. Econometrica 5(1):1–55 Frost PA (1975) Some properties of the Almon lag technique when one searches for degree of polynomial and lag. J Am Stat Assoc 70(351):606–612 Genthon G, Barnola J, Raynaud D et al (1987) Vostok ice core: climatic response to CO2 and orbital forcing changes over the last climatic cycle. Nature 329:414–418 Greene WH (2003) Econometric analysis, 5th edn. Prentice Hall, New Jersey Gujarati DN (2003) Basic econometrics, 4th edn. McGraw-Hill, New York Güler H, Gültay B, Kaçıranlar S (2017) Comparisons of the alternative biased estimators for the distributed lag models. Commun Stat 46(4):3306–3318 Gültay B, Kaçıranlar S (2015) Mean square error comparisons of the alternative estimators for the distributed lag models. Hacettepe J Math Stat 44(5):1215–1233 Hansen J, Lebedeff S (1987) Global trends of measured surface air temperature. J Geophys Res 92(D11):13345–13372 Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67 Hoerl AE, Kennard RW, Baldwin KF (1975) Ridge regression: some simulations. Commun Stat 4:105–123 IPCC (2014) Climate change 2014: mitigation of climate change. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, C. von Stechow, T. Zwickel and J.C. Minx (eds.) Contribution of Working Group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge Jongsik H, Shin YS, Kim H (2011) Distributed lag effects in the relationship between temperature and mortality in three major cities in South Korea. Sci Total Environ 409(18):3274–3280 Kaufmann RK, Kauppi H, Stock JH (2006) Emissions, concentrations, & temperature: a time series analysis. Climat Change 77:249–278 Keeling CD (1960) The concentration and isotopic abundances of carbon dioxide in the atmosphere. Tellus 12(2):200–203 Keeling CD, Bacastow RB, Bainbridge AE et al (1976) Atmospheric carbon dioxide variations at Mauna Loa Observatory. Hawaii Tellus 28(6):538–551 Keeling CD (1978) The influence of Mauna Loa Observatory on the development of atmospheric CO2 research. In: Miller J (ed) Mauna Loa Observatory: a 20th anniversary report. (National Oceanic and Atmospheric Administration Special Report, September 1978, pp 36–54. NOAA Environmental Research Laboratories, Boulder Kennedy PA (2003) Guide to econometrics. MIT Press, Cambridge Kibria BMG (2003) Performance of some new ridge regression estimators. Commun Stat 32(2):419–435 Maddala GS (1974) Ridge estimators for distributed lag models. NBER Working Paper, 69 Majid A, Aslam M, Altaf S, Amanullah M (2019) Addressing the distributed lag models with heteroscedastic errors. Commun Stat. https://doi.org/10.1080/03610918.2019.1643884 Muniz G, Kibria BMG (2009) On some ridge regression estimators: an empirical comparisons. Commun Stat 38:621–630 Özbay N (2019) Two-parameter ridge estimation for the coefficients of Almon distributed lag model. Ir J Sci Technol Trans A 43(A4):1819–1828 Özbay N, Kaçıranlar S (2017a) Improvement of the Liu-type Shiller estimator for distributed lag models. J Forecast 36(7):776–783 Özbay N, Kaçıranlar S (2017b) The Almon two parameter estimator for the distributed lag models. J Stat Comput Simul 87(4):834–843 Özbay N, Toker S (2018a) Developing prediction performance of Ridge and Liu estimators by using cross validation criterion in Almon Model. In: 3rd international mediterranean science and engineering congress (IMSEC 2018), 24–26 October, Adana, Turkey Özbay N, Toker S (2018b) Implementation of linear constraints in distributed lag model. Im: International conference on multidisciplinary sciences (icomus 2018), 15–16 December, İstanbul, Turkey Özbay N, Toker S (2020) Efficiency of Mansson’s method: Some numerical findings about the role of biasing parameter in the estimation of distributed lag model. Commun Stat. 49(9):2333–2346 Özbay N, Toker S (2019a) Determination of biasing parameters for Almon Liu type estimator via a mathematical programming approach. In: 6th IFS and contemporary mathematics conference (IFSCOM 2019), 7–10 June, Mersin, Turkey Özbay N, Toker S (2019b) Considering linear constraints for Almon two parameter ridge estimation. In: 11th international statistics congress (ISC 2019), 4–8 October 2019, Muğla, Turkey Özbay N, Toker S (2019c) Defining some adaptive optimal estimators for the distributed lag model. In: 5th international researchers, statisticians and young statisticians congress (IRSYSC 2019), 18–20 October, Aydın, Turkey Pales JC, Keeling CD (1965) The concentration of atmospheric carbon dioxide in Hawaii. J Geophys Res 70(24):6053–6076 Revelle R (1982) Carbon dioxide and world climate. Sci Am 247(2):35–43 Ruth M, Davidsdottir B, Laitner S (2000) Impacts of market-based climate change policies on the US pulp and paper industry. Energy Policy 28(4):259–270 Saudi MHM, Sinaga O, Roespinoedji D, Razimi MSA (2019) The role of renewable, non-renewable electricity consumption and carbon emission in development in Indonesia: evidence from distributed lag tests. Int J Energy Econ Policy 9(3):46–52 Shalabh (1995) Performance of Stein rule procedure for simultaneous prediction of actual and average values of study variable in linear regression model. Bull Int Stat Inst 56:1375–1390 Shalabh, Toutenburg H, Heumann C (2009) Stein-rule estimation under an extended balanced loss function. J Stat Comput Simul 79(10):1259–1273 Shiller RJ (1973) A distributed lag estimator derived from smoothness priors. Econometrica 41:775–788 Skripnuk DF, Samylovskaya EA (2018) Human activity and the global temperature of the planet. IOP Conf Ser 180(1):012021 Solomon S, Plattner GK, Knutti R, Friedlingstein P (2009) Irreversible climate change due to carbon dioxide emissions. Proc Natl Acad Sci USA 106(6):1704–1709 Sun L, Wang M (1996) Global warming and global dioxide emission: an empirical study. J Environ Manag 46(4):327–343 Thurman SS, Swamy PAVB, Mehta JS (1986) An examination of distributed lag model coefficients estimated with smoothness priors. Commun Stat 15(6):1723–1749 Toker S, Özbay N (2019) The effect of target function on the predictive performance of the two stage ridge estimator. J Forecast 38:749–772 Tol RSJ, De Vos AF (1998) A bayesian statistical analysis of the enhanced greenhouse effect. Climat Change 38(1):87–112 Toutenburg H, Shalabh, (1996) Predictive performance of the methods of restricted and mixed regression estimators. Biometr J 38(8):951–959 Toutenburg H, Shalabh, (2000) Improved predictions in linear regression models with stochastic linear constraints. Biometr J 42:71–86 Triacca U (2005) Is Granger causality analysis appropriate to investigate the relationship between atmospheric concentration of carbon dioxide and global surface air temperature? Theoret Appl Climatol 81(3–4):133–135 Ullah A, Raj B (1980) A polynomial distributed lag model with stochastic coefficients and priors. Empir Econ 5:219–232 Vinod HD, Ullah A (1981) Recent advances in regression methods. Marcel Dekker, New York Yeo SJ, Trivedi PK (1989) On using ridge type estimators for a distributed lag model. Oxford Bull Econ Stat 51(1):85–90