IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/2018-01-2.html
   My bibliography  Save this article

Forecasting Unemployment Rates in USA using Box-Jenkins Methodology

Author

Listed:
  • Nikolaos Dritsakis

    (Department of Applied Informatics, University of Macedonia, Economics and Social Sciences, 156 Egnatia Street, 540 06 Thessaloniki, Greece,)

  • Paraskevi Klazoglou

    (Department of Applied Informatics, University of Macedonia, Economics and Social Sciences, 156 Egnatia Street, 540 06 Thessaloniki, Greece)

Abstract

Unemployment, as a measure of market conditions, appears as an economic problem in every society and is a phenomenon with considerable negative social consequences. A low rate of unemployment is one of the main objectives for governmental macroeconomic policy. The main aim of this project is to identify the most appropriate forecasting model, i.e. the seasonal autoregressive integrated moving average (SARIMA), autoregressive conditional heteroskedasticity (ARCH) and the generalized autoregressive conditional heteroskedasticity (GARCH). Using one or a combination of these models could provide the best forecast for US unemployment. Applying monthly data to the US unemployment rate from January 1955 to July 2017 proved that the SARIMA(1,1,2)(1,1,1)12 JEL classifications: C53, E27

Suggested Citation

  • Nikolaos Dritsakis & Paraskevi Klazoglou, 2018. "Forecasting Unemployment Rates in USA using Box-Jenkins Methodology," International Journal of Economics and Financial Issues, Econjournals, vol. 8(1), pages 9-20.
    • Handle: RePEc:eco:journ1:2018-01-2
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijefi/article/download/5550/pdf
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijefi/article/view/5550/pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    6. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    7. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhen Liu & Assem Abu Hatab, 2023. "Assessing stakeholder engagement in public spending, green finance and sustainable economic recovery in the highest emitting economies," Economic Change and Restructuring, Springer, vol. 56(5), pages 3015-3040, October.
    2. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    3. Robert Jay Angco & Lee Timtim & Mikee Ando & Cathy Leyson & Cristy Rose Villasin, 2021. "Time series approach on Philippines' three economic participation using ARIMA Model," Technium Social Sciences Journal, Technium Science, vol. 25(1), pages 304-332, November.
    4. Phi-Hung Nguyen & Jung-Fa Tsai & Ihsan Erdem Kayral & Ming-Hua Lin, 2021. "Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam," Sustainability, MDPI, vol. 13(14), pages 1-27, July.
    5. Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Liviu Adrian Stoica, 2021. "Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023," Sustainability, MDPI, vol. 13(13), pages 1-22, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Melina Dritsaki & Chaido Dritsaki, 2020. "Forecasting European Union CO2 Emissions Using Autoregressive Integrated Moving Average-autoregressive Conditional Heteroscedasticity Models," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 411-423.
    2. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, January.
    3. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    4. Dimitrios Kartsonakis-Mademlis & Nikolaos Dritsakis, 2022. "Asymmetric volatility transmission in Japanese stock market in the presence of structural breaks," The Japanese Economic Review, Springer, vol. 73(4), pages 647-677, October.
    5. Dritsaki, Chaido, 2019. "Modeling the Volatility of Exchange Rate Currency using GARCH Model," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 72(2), pages 209-230.
    6. Walid M. A. Ahmed, 2014. "Dynamic interactions between Egyptian equity and currency markets prior to and during political unrest," Applied Financial Economics, Taylor & Francis Journals, vol. 24(20), pages 1347-1359, October.
    7. Balaguer, Jacint & Ripollés, Jordi, 2012. "Testing for price response asymmetries in the Spanish fuel market. New evidence from daily data," Energy Economics, Elsevier, vol. 34(6), pages 2066-2071.
    8. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, January.
    9. Debabrata Mukhopadhyay & Nityananda Sarkar, 2013. "Stock Returns Under Alternative Volatility and Distributional Assumptions: The Case for India," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 1-19, April.
    10. Panayiotis Theodossiou, 1998. "Financial Data and the Skewed Generalized T Distribution," Management Science, INFORMS, vol. 44(12-Part-1), pages 1650-1661, December.
    11. Sáenz Rodríguez, Estela & Sabaté Sort, Marcela & Gadea Rivas, María Dolores, 2009. "La medición del riesgo externo. Un estudio aplicado al caso español en el periodo 1960-2000/The Measurement of External Risk. An Applied Study to the Spanish Case in the Period 1960-2000," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 27, pages 575(16á)-57, Agosto.
    12. Font, Begoña, 1998. "Modelización de series temporales financieras. Una recopilación," DES - Documentos de Trabajo. Estadística y Econometría. DS 3664, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Kondo, Koji, 1997. "Statistical analysis of foreign exchange rates: application of cointegration model and regime-switching stochastic volatility model," ISU General Staff Papers 1997010108000012997, Iowa State University, Department of Economics.
    14. Stavros Degiannakis & Evdokia Xekalaki, 2005. "Predictability and model selection in the context of ARCH models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(1), pages 55-82, January.
    15. Geoffrey F. Loudon & Wing H. Watt & Pradeep K. Yadav, 2000. "An empirical analysis of alternative parametric ARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 117-136.
    16. Bohl, Martin T. & Diesteldorf, Jeanne & Siklos, Pierre L., 2015. "The effect of index futures trading on volatility: Three markets for Chinese stocks," China Economic Review, Elsevier, vol. 34(C), pages 207-224.
    17. Nidhal Mgadmi & Khemaies Bougatef, 2017. "Modeling volatility of the French stock market," Economics Bulletin, AccessEcon, vol. 37(2), pages 988-998.
    18. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038, Elsevier.
    19. Estela Sáenz & María Dolores Gadea & Marcela Sabaté, 2009. "Measuring the external risk in the United Kingdom," Economics Bulletin, AccessEcon, vol. 29(2), pages 1182-1189.
    20. Bensafta, Kamel Malik & Semedo, Gervasio, 2009. "De la transmission de la volatilité à la contagion entre marchés boursiers : l’éclairage d’un modèle VAR non linéaire avec bris structurels en variance," L'Actualité Economique, Société Canadienne de Science Economique, vol. 85(1), pages 13-76, mars.

    More about this item

    Keywords

    Unemployment Rates; Seasonal Time Series; SARIMA-CARCH Model;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ1:2018-01-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.