Abeysinghe T (1998). Forecasting Singapore's Quarterly GDP with Monthly External Trade. Int. J. Forecasting 14:505-513.
Crossref

Ahmad S, Latif H (2011). MP1 Competencies Analysis of Box-Jenkins Method in Forecasting Electricity Demand, Empowering Science, Technology and Innovation Towards a Better Tomorrow. UMTAS, pp. 201-204.

Assis K, Amran A, Remali Y (2010). Forcasting Cocoa Beans Pricis Using Univariate Time Series Models. Int. Refereed Res. J. 1:71-80.

Baffigi A, Golinelli R, Parigi G (2004). Bridge Models Forecast the Euro Area GDP. Int. J. Forecasting, 20:447-460.
Crossref

Barth J. Caprio G, Phumiwasana T (2009). China's Emerging Financial Markets, The Milken Institute Series on Financial Innovation and Economic Growth 8:73-110.
Crossref

Bourbonnais R (2004). Manuel et excercises corrigés, 4e edition Dunod, Paris.

Bollerslev T (1986). Generalized Autoregressive Conditional Heteroscedasticity. J. Econ. 31(3):3 07-327.

Box G, Jenkins G (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.

Box G, Jenkins G (1976). Time Series Analysis: Forecasting and Control. 1st Edn., Holden-Day, San Fransisco, , P 575.

Cameroon (2009). Working paper. Yaounde: Ministry of the Economy, Planning and Regional Development.

Dobre I, Alexandru A (2008). Modelling Unemployment Rate Using Box-Jekins Methodology. J. Appl. Quant. Method. 3(2):156-166

Engle F (1982). Autoregressive Conditional Heteroscedasticity with Estimates of theVariance of United Kingdom Inflation. Econometrica, 50(4):987-1008.
Crossref

Elham K, Masoumeh Z, Ebrahim B (2010). Prediction of added value of agriculturalsubsections using artificial neural networks: Box-Jenkins and Holt-Winters methods. J. Dev. Agri. Econ. 2(4):115-121.

Etuk H, Uchendu B, Uyodhu V (2012). Arima Fit to Nigerian Unemployment Data. Journal of Basic and Applied Scientific Research, 2(6)5964-5970 ISSN, pp. 2090-4304.

Freedom House (2014). 

View

Floros C (2005). Forcasting the UK Unemployment Rate: Model Comparisons. International Journal of Applied Econometrics and Quantitative Studies.2-4:55-72.

Gan W, Wong F (1993). A Bayesian Vector Autoregression Model for Forecasting Quarterly GDP: The Singapore Experience. Singapore Economic Rev. 38:15-34.

Gil-Alana A (2001). A fractionally Integrated Exponential Model for UK Unemployment. J.Forecasting 20:329-340.
Crossref

Golan A, Perloff J (2002). Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method". UDARE Working Papers, P 956. 

Greene W (2003). Econometric Analysis.3th ed, Englewood Cliffs, N.J.: Prentice Hall.

Ghosh S (2008). Univariate Time-Series Forecasting of Monthly Peak Demand of Electricity in Northern India. Int. J. Indian Culture and Bus. Manage. 1(4):466-474 .
Crossref

Gujarati D (2004). Essentials of Econometrics. 4th ed., McGraw-Hill,New York.

Floros C (2005). Forcasting the UK Unemployment Rate: Model Comparisons. Int. J. Appl. Econ. Quant. Stud. 2-4:55-72.

Hukkinen J, Viren M (1999). Assessing the Forecasting Performance of a Macroeconomic Model. J. Policy Model. 21:753-768.
Crossref

Javedani H, Suhartono L (2010). An Evaluation of Some Classical Methods for Forecasting Electricity Usage on Specific Problem. Proceedings of the Regional Conference on Statistical Sciences (RCSS'10), pp. 47-56.

Kamil A, Noor A (2006). Time series modeling of malaysian raw palm oil price: Autoregressive conditional heteroskedasticity (ARCH) model approach. Discov. Math. 28:19-32.

Lee H, Rahman H, Suhartono L, Nor M, Kamisan M (2012). Seasonal ARIMA for Forecasting Air Pollution Index: A Case Study. Am. J. Appl. Sci. 9(4):570-578.
Crossref

Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen E, Winkler R (1982). The Accuracy of Extrapolative (Time Series Methods): Results of a Forecasting Competition, J. Forecasting 1(2):111-153.
Crossref

Mandal B (2005). Forecasting Sugarcane Productions in India with ARIMA Model. Www.Interstat .Statjournals.Net

Nasir M, Hwa K, Huzaifah M (2006). An Initial Study on the Forecast Model for Unemployment Rate". Workshop Review of the Labour Force/Migration Survey, Putrajaya, Malaysia.

Nelson D (1996). Conditional Heteroskedasticity in Asset Returns: A New Approach Econometrician 59(2):347-370

Nkwatoh L (2012). Forecasting Unemployment Rates in Nigeria Using Univariate Time Series Models. Int. J. Bus. Commun. 1(12):33-46.

Pardhan P (2012). Use of Univariate Time Series Models for Forecasting Cement Productions in India. Int. Res. J. Financ. Econ. 83:167-179.

Pei L, Shih-Huang C,Hui-Hua Y, Ching-Tsung H, Mei-Rong T (2008). Application of Artificial Neural Network and SARIMA in Portland Cement Supply Chain to Forecast Demand. Natural 179 International Research Journal of Finance and Economics - Issue 83 (2012). Fourth International Conference, 18- 20 Oct. 2008, 3:97-101.

Purna C (2012). Use of Univariate Time Series Models for Forecasting Cement Productions in India. Int. Res. J. Financ. Econ. 83:167-179.

Proiettti T (2001). Forecasting the US Unemployment Rate. Working Paper, Dipartimneto Di Scienze Statistiche, Universita Di Udine, Udine, Italy.

Purna C (2012). Use of Univariate Time Series Models for Forecasting Cement Productions in India. Int. Res. J. Financ. Econ. 83:167-179.

Reilly D (1980). Experiences with an automatic Box-Jenkins modeling algorithm. Time Series Analysis-Proceedings of Houstan Meeing on Time Series Analyis, Amsterdam: North-Holland publishing.

Reynolds S, Mellichamp J, Smith R (1995). Box-Jenkins forecast model identification. AI Expert, pp.15-28.

Sarbijan S (2014). A Comparison Study between Markov Switching Time Series Model and Arima Model for Forecasting Iran Economic Growth. Int. J. Sci. Res. Knowled. 2:058-065.

Slutsky E (1937). The Sommation of Random Causes as the Source of Cyclic Processes. Econometrica, 5:105-146.
Crossref

Syariza A, Noorhafiza M (2005). Comparison of time Series methods for electricity forecasting: A case study in Perlis. ICOQSIA, pp. 6-8 December, Penang, Malaysia.

Taylor W (2008). A comparison of univariate time series methods for forecasting intraday arrivals at a call center. Manage. Sci. 54:253-265.
Crossref

Wankhade R, Mahalle S, Gajbhiye S, Bodade V (2010). Use of the ARIMA Model for Forecasting Pigeon Pea Production in India. Int. Rev. Bus. Financ. 2(1):97-102

World Development Indicator (2014). 

View

Yule G (1926). Why Do We Sometimes Get Nonsense-Correlations between Time Series? A Study in Sampling and the Nature of Time Series. J. Royal Stat. Soc. 89:1-64.
Crossref

Zhou B, He D, Sun Z (2006). Modeling and Simulation Tools for Emerging Telecommunication Networks. Springer US: pp. 101-121.
Crossref

Zakion J (1996). Threshold Heteroskedastic models. Manuscript, CREST, INSEE, Paris.