Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatility

Environmental Modelling and Software

Volumn 51, Issue , 2014, Pages 286-295

  Reisen, Valdério Anselmo ^a,b   Sarnaglia, Alessandro José Queiroz ^a   Reis, Neyval Costa ^b   Lévy Leduc, Céline ^c   Santos, Jane Méri ^b  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b FEDERAL UNIVERSITY OF ESPÍRITO SANTO   (Brazil)

^c AgroParisTech   (France)

Author keywords

ARFIMA; Fractional differencing; Heteroscedasticity; Long memory; PM10 contaminant; Seasonality

Indexed keywords

ARFIMA; FRACTIONAL DIFFERENCING; HETEROSCEDASTICITY; LONG-MEMORY; SEASONALITY;

COMPUTER SOFTWARE; ECOLOGY;

TIME SERIES;

CONCENTRATION (COMPOSITION); ERROR ANALYSIS; NUMERICAL MODEL; PARTICULATE MATTER; SEASONALITY; VOLATILIZATION;

BRAZIL; CARIACICA; ESPIRITO SANTO;

EID: 84887825224     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2013.09.027     Document Type: Article

References (48)

1. Arteche J., Robinson P.M. Semiparametric inference in seasonal and cyclical long memory processes. J.Time Ser. Anal. 2000, 21(1):1-25.
2. Baillie R.T., Chung C.F., Tieslau M.A. Analysing inflation by fractionally integrated ARFIMA-GARCH model. J.Appl. Econ. 1996, 11:23-40.
3. Bennett N.D., Croke B.F.W., Guarisso G., Guillaume J.H.A., Hamilton S.H., Jakeman A.J., Marsili-Libelli S., Newham L.T.H., Norton J.P., Perrin C., Pierce S.A., Robson B., Seppelt R., Voinov A.A., Fath B.D., Andreassian V. Characterising performance of environmental models. Environ. Model. Softw. 2013, 40:1-20.
4. Bollerslev T. Generalized autoregressive conditional heteroscedasticity. J.Econ. 1986, 31:307-327.
5. Bollerslev T., Chou R.Y., Kroner K.F. Arch modeling in finance. a review of the theory and empirical evidence. J.Econ. 1992, 52:5-59.
6. Box G., Jenkins G., Reinsel G. Time Series Analysis: Forecasting and Control 2008, Prentice Hall. fourth ed.
7. Brockwell P.J., Davis R.A. Time Series: Theory and Methods. Springer Series in Statistics 1991, second ed.
8. Chelani A.B., Devotta S. Air quality forecasting using a hybrid autoregressive and nonlinear model. Atmos. Environ. 2005, 40:1774-1780.
9. Díaz Robles L.A., Ortega J.C., Fu J.S., Reed G.D., Chow J.C., Watson J.G., Moncada Herrera J.A. Ahybrid arima and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmos. Environ. 2008, 42(35):8331-8340.
10. Engle R.F. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica 1982, 50:987-1007.
11. Fajardo M., Reisen V.A., Cribari Neto F. Robust estimation in long-memory processes under additive outliers. J.Stat. Plan. Inference 2009, 139:2511-2525.
12. Flores B.E. Apragmatic view of accuracy measurement in forecasting. Omega Int. J. Manag. Sci. 1986, 14:93-98.
13. Franco G.C., Reisen V.A., de Assis F. Bootstrap tests for fractional integration and cointegration: a comparison study. Math. Comput. Simul. 2013, 87:19-29.
14. Geweke J., Porter-Hudak S. The estimation and application of long memory time series model. J.Time Ser. Anal. 1983, 4(4):221-238.
15. Giraitis L., Leipus R. Ageneralized fractionally differencing approach in long-memory modeling. Lith. Math. J. 1995, 35:53-65.
16. Goyal P., Chan A.T., Jaiswal N. Statistical models for the prediction of respirable suspended particulate matter in urban cities. Atmos. Environ. 2006, 40:2068-2077.
17. Granger C.W.J., Joyeux R. An introduction to long-memory times series models and fractional differencing. J.Time Ser. Anal. 1980, 1:15-29.
18. Hosking J.R. Fractional differencing. Biometrika 1981, 68:165-176.
19. Hsu N.-J., Tsai H. Semiparametric estimation for seasonal long-memory time series using generalized exponential models. J.Stat. Plan. Inference 2009, 139:1992-2009.
20. Hurvich C.M., Deo R. Plug-in selection of the numbers of frequencies in regression estimates of the memory parameter of a long-memory time series. J.Time Ser. Anal. 1999, 20(3):331-342.
21. Hurvich C.M., Ray B.K. Estimation of the memory parameter for nonstationary or noninvertible fractionally integrated processes. J.Time Ser. Anal. 1995, 16(1):17-42.
22. Iglesias P., Jorqueira H., Palma W. Data analysis using regression model with missing observations and long memory. Comput. Stat. Data Anal. 2006, 50(8):2028-2043.
23. Jakeman A.J., Letcher R.A., Norton L.P. Ten interative steps in development and evaluation of environmental models. Environ. Model. Softw. 2006, 21:602-614.
24. Karlaftis M.G., Vlahogianni E.I. Memory properties and fractional integration in transportation time-series. Transp. Res. C 2009, 17:444-453.
25. 10 forecasting in Europe. Atmos. Environ. 2009, 42:6425-6434.
26. Kumar U., Ridder K.D. GARCH modelling in association with FFT-ARIMA to forecast ozone episodes. Atmos. Environ. 2010, 44:4252-4265.
27. Lee J.H. ALagrange multiplier test for Grch models. Econ. Lett. 1991, 37:265-271.
28. Ling S., Li W.K. On fractionally integrated autoregressive moving-average time series with conditional heteroscedasticity. J.Am. Stat. Assoc. 1997, 92(439):1184-1194.
29. 10 concentrations at Ta-Liao with Box-Jenkins time series models and multivariate analysis. Atmos. Environ. 2009, 43(13):2104-2113.
30. McAleer M., Chan F. Modelling trends and volatility in atmospheric carbon dioxide concentrations. Environ. Model. Softw. 2006, 21(9):1273-1279.
31. Nelson D.B., Cao C.Q. Inequality constraints in the univariate Garch model. J.Bus. Econ. Stat. 1992, 10:229-235.
32. Palma W. Long-memory Time Series: Theory and Methods 2007, Wiley.
33. Palma W., Chan N.H. Efficient estimation of seasonal long-range-dependent processes. J.Time Ser. Anal. 2005, 26:863-892.
34. Porter Hudak S. An application of the seasonal fractionally differenced model to the monetary aggregates. J.Am. Stat. Assoc. 1990, 85:338-344.
35. 10 composition and cell toxicity. a multivariate and graphical approach. Chemosphere 2007, 67(6):1218-1228.
36. Reisen V.A. Estimation of the fractional difference parameter in the ARIMA(p, d, q) model using the smoothed periodogram. J.Time Ser. Anal. 1994, 15(3):335-350.
37. Reisen V.A., Lopes S. Some simulations and applications of forecasting long-memory time-series models. J.Stat. Plan. Inference 1999, 80:269-287.
38. Reisen V.A., Moulines E., Soulier P., Franco G.C. On the properties of the periodogram of a stationary long-memory process over different epochs with applications. J.Time Ser. Anal. 2010, 31:20-36.
39. Reisen V.A., Rodrigues A., Palma W. Estimating seasonal long-memory processes: a Monte Carlo study. J.Stat. Comput. Simulation 2006, 76(4):305-316.
40. Reisen V.A., Rodrigues A., Palma W. Estimation of seasonal fractionally integrated processes. Comput. Stat. Data Anal. 2006, 50:568-582.
41. Reisen V.A., Zamprogno B., Palma W., Arteche J. Asemiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model. Math. Comput. Simul 2013.
42. Sena M.R., Reisen V.A., Lopes S.R. Correlated error in the parameters estimation of the arfima model: a simulated study. Commun. Stat. Simul. Comput. 2006, 35:789-802.
43. 10 daily forecasting model. Atmos. Environ. 2008, 42(6):1098-1109.
44. Touloumi G., Atkinson R., Le Tertre A., Samoli R., Schwaartz J., Schlinder C., Vonk M.J., Rossi G., Saez M., Rabszenko D., Katsouyanni K. Analysis of health outcome time series data in epidemiological studies. Environmetrics 2004, 15(2):101-117.
45. Tsai H., Chan K.-S. Anote on inequality constraints in the GARCH model. Econ. Theor. 2008, 24:823-828.
46. Windsor H.L., Toumi R. Scaling and persistence of UK pollution. Atmos. Environ. 2001, 35:4545-4556.
47. Woodward W.A., Cheng Q.C., Gray H.L. Ak-factor GARMA long-memory model. J.Time Ser. Anal. 1998, 19(4):485-504.
48. 10 and ozone in life cycle impact assessment. Atmos. Environ. 2008, 42(3):441-453.