IDEAS home Printed from https://ideas.repec.org/a/bjz/ajisjr/820.html
   My bibliography  Save this article

Investigation of the Lucas Loss Functioning during the Period 2000-2012 in Albania

Author

Listed:
  • Llambrini Sota
  • Fejzi Kolaneci

Abstract

The main objective of the study is to investigate the Lucas loss function as well as the Okun misery index over the period January 2000-June 2012 in Albania. Some results of the study include:The Central Limit Theorem is not valid for the quarterly economic loss in the sense of R. E. Lucas, Jr. during the specified period in Albania, at a confidence level 90.5%.The quarterly economic loss caused by inflation and unemployment during the specified period in Albania is an unfair game, at the confidence level 99.9%.The quarterly Okun misery index during the specified period in Albania is an unfair game, at the confidence level 94.2%.In average, the Albanian people would trade off a 1 percent increase in the unemployment rate for a 2.35 percent increase in the inflation rate. The famous “Okun misery index†under-weights the economic loss of the Albanian people caused by joblessness.

Suggested Citation

  • Llambrini Sota & Fejzi Kolaneci, 2014. "Investigation of the Lucas Loss Functioning during the Period 2000-2012 in Albania," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 3, July.
  • Handle: RePEc:bjz:ajisjr:820
    DOI: 10.5901/ajis.2014.v3n4p127
    as

    Download full text from publisher

    File URL: https://www.richtmann.org/journal/index.php/ajis/article/view/3080
    Download Restriction: no

    File URL: https://www.richtmann.org/journal/index.php/ajis/article/view/3080/3036
    Download Restriction: no

    File URL: https://libkey.io/10.5901/ajis.2014.v3n4p127?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert J. MacCulloch & Rafael Di Tella & Andrew J. Oswald, 2001. "Preferences over Inflation and Unemployment: Evidence from Surveys of Happiness," American Economic Review, American Economic Association, vol. 91(1), pages 335-341, March.
    2. Robert E. Lucas, 2001. "Inflation and Welfare," International Economic Association Series, in: Axel Leijonhufvud (ed.), Monetary Theory as a Basis for Monetary Policy, chapter 4, pages 96-142, Palgrave Macmillan.
    3. Heinz Welsch, 2007. "Macroeconomics and Life Satisfaction: Revisiting the “Misery Index”," Journal of Applied Economics, Taylor & Francis Journals, vol. 10(2), pages 237-251, November.
    4. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    5. Stein, Jerome L, 1974. "Unemployment, Inflation, and Monetarism," American Economic Review, American Economic Association, vol. 64(6), pages 867-887, December.
    6. ., 2008. "Providing for the Optimum Quantity of Money," Chapters, in: Money and Monetary Systems, chapter 9, pages 96-104, Edward Elgar Publishing.
    7. Dotsey, Michael & Ireland, Peter, 1996. "The welfare cost of inflation in general equilibrium," Journal of Monetary Economics, Elsevier, vol. 37(1), pages 29-47, February.
    8. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    9. Thomas Sargent & Noah Williams & Tao Zha, 2006. "Shocks and Government Beliefs: The Rise and Fall of American Inflation," American Economic Review, American Economic Association, vol. 96(4), pages 1193-1224, September.
    10. James H. Stock & Mark W. Watson, 2007. "Erratum to “Why Has U.S. Inflation Become Harder to Forecast?”," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    11. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    Full references (including those not matched with items on IDEAS)

    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. Castelnuovo, Efrem & Greco, Luciano & Raggi, Davide, 2008. "Estimating regime-switching Taylor rules with trend inflation," Bank of Finland Research Discussion Papers 20/2008, Bank of Finland.
    2. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    3. Verbrugge, Randal & Zaman, Saeed, 2023. "The hard road to a soft landing: Evidence from a (modestly) nonlinear structural model," Energy Economics, Elsevier, vol. 123(C).
    4. Francesco Bianchi & Giovanni Nicolo & Dongho Song, 2023. "Inflation and Real Activity over the Business Cycle," Finance and Economics Discussion Series 2023-038, Board of Governors of the Federal Reserve System (U.S.).
    5. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    6. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
    7. Shobande, Olatunji A. & Asongu, Simplice A., 2022. "The Critical Role of Education and ICT in Promoting Environmental Sustainability in Eastern and Southern Africa: A Panel VAR Approach," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    8. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    9. Garriga, Carlos & Kydland, Finn E. & Šustek, Roman, 2021. "MoNK: Mortgages in a New-Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    10. James M. Nason & Gregor W. Smith, 2021. "Measuring the slowly evolving trend in US inflation with professional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 1-17, January.
    11. Ruhollah Eskandari & Morteza Zamanian, 2023. "Heterogeneous responses to corporate marginal tax rates: Evidence from small and large firms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 1018-1047, November.
    12. Gary Koop & Dimitris Korobilis, 2023. "Bayesian Dynamic Variable Selection In High Dimensions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
    13. Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2022. "A Model of the Fed's View on Inflation," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 686-704, October.
    14. Michael McLeay & Silvana Tenreyro, 2020. "Optimal Inflation and the Identification of the Phillips Curve," NBER Macroeconomics Annual, University of Chicago Press, vol. 34(1), pages 199-255.
    15. Anna Almosova & Niek Andresen, 2023. "Nonlinear inflation forecasting with recurrent neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 240-259, March.
    16. Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
    17. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    18. Ornela SHALARI & Fejzi KOLANECI, 2014. "Statistical analysis of the inflation in the case of Albania," EuroEconomica, Danubius University of Galati, issue 2(33), pages 67-77, November.
    19. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    20. Guido Ascari & Luca Fosso, 2021. "The Inflation Rate Disconnect Puzzle: On the International Component of Trend Inflation and the Flattening of the Phillips Curve," Working Paper 2021/17, Norges Bank.

    More about this item

    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:bjz:ajisjr:820. 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: Richtmann Publishing Ltd (email available below). General contact details of provider: https://www.richtmann.org/journal/index.php/ajis .

    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.