[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/ara/wpaper/014.html
   My bibliography  Save this paper

Fitting Armenian Data to the Simple DSGE Model with Permanent Productivity Growth

Author

Listed:
  • Haykaz Igityan

    (Monetary Policy Department, Central Bank of Armenia)

  • Hovhannes Manukyan

    (Monetary Policy Department, Central Bank of Armenia)

Abstract
This paper discusses the evaluation of structural parameters and estimated potential economic growth of Armenia using different specifications of DSGE models. We extend the simple models so that they are consistent with a balanced steady state growth path driven by deterministic labor-augmenting technological progress. Using a Bayesian likelihood approach, paper estimates DSGE models for the Armenian economy using three macro-economic time series. As a result, the dynamics of estimated potential economic growth of the model with demand and mark-up shocks is consistent with economic stylized facts contrary to other models that have no demand and markup shocks or only have one of these shocks. Additionally, estimated potential economic growth of the model with demand and markup shocks shows high correlation with other estimates of Central Bank of Armenia. Paper then structures and estimates two specifications of simple RBC model and the estimated potential economic growth of the model with persistent permanent productivity is identical with DSGE’s one. We show that our models are able to beat Vector Autoregression (VAR) models in out-of-sample forecasting of economic growth.

Suggested Citation

  • Haykaz Igityan & Hovhannes Manukyan, 2020. "Fitting Armenian Data to the Simple DSGE Model with Permanent Productivity Growth," Working Papers 14, Central Bank of the Republic of Armenia.
  • Handle: RePEc:ara:wpaper:014
    as

    Download full text from publisher

    File URL: https://www.cba.am/EN/panalyticalmaterialsresearches/Analytical_03.02.2020.pdf
    File Function: First version, 2020
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lucas, Robert E, Jr & Prescott, Edward C, 1971. "Investment Under Uncertainty," Econometrica, Econometric Society, vol. 39(5), pages 659-681, September.
    2. Hasumi, Ryo & Iiboshi, Hirokuni & Nakamura, Daisuke, 2018. "Trends, cycles and lost decades: Decomposition from a DSGE model with endogenous growth," Japan and the World Economy, Elsevier, vol. 46(C), pages 9-28.
    3. Christiano, Lawrence J. & Trabandt, Mathias & Walentin, Karl, 2011. "Introducing financial frictions and unemployment into a small open economy model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 1999-2041.
    4. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    5. Günter Coenen & Frank Smets & Igor Vetlov, 2009. "Estimation of the Euro Area Output Gap Using the NAWM," Bank of Lithuania Working Paper Series 5, Bank of Lithuania.
    6. Pierre Lafourcade & Joris de Wind, 2012. "Taking Trends Seriously in DSGE Models: An Application to the Dutch Economy," DNB Working Papers 345, Netherlands Central Bank, Research Department.
    7. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    8. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    9. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.
    10. Yongsung Chang & Taeyoung Doh & Frank Schorfheide, 2007. "Non-stationary Hours in a DSGE Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1357-1373, September.
    11. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    12. Celso Jose Costa Junior, 2016. "Understanding DSGE models," Vernon Press Titles in Economics, Vernon Art and Science Inc, edition 1, number 70.
    13. Takuji Fueki & Ichiro Fukunaga & Hibiki Ichiue & Toyoichiro Shirota, 2016. "Measuring Potential Growth with an Estimated DSGE Model of Japan’s Economy," International Journal of Central Banking, International Journal of Central Banking, vol. 12(1), pages 1-32, March.
    14. Edge, Rochelle M. & Kiley, Michael T. & Laforte, Jean-Philippe, 2008. "Natural rate measures in an estimated DSGE model of the U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2512-2535, August.
    15. Mr. Tigran Poghosyan & Samya Beidas-Strom, 2011. "An Estimated Dynamic Stochastic General Equilibrium Model of the Jordanian Economy," IMF Working Papers 2011/028, International Monetary Fund.
    16. Carl E. Walsh, 2010. "Monetary Theory and Policy, Third Edition," MIT Press Books, The MIT Press, edition 3, volume 1, number 0262013770, April.
    17. Vetlov, Igor & Pisani, Massimiliano & Hlédik, Tibor & Jonsson, Magnus & Kucsera, Henrik, 2011. "Potential output in DSGE models," Working Paper Series 1351, European Central Bank.
    18. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    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. Haykaz Igityan & Hasmik Kartashyan, 2021. "Housing Market Drivers and Dynamics in Armenia," Working Papers 16, Central Bank of the Republic of Armenia.

    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. Schmidt, Sebastian & Wieland, Volker, 2013. "The New Keynesian Approach to Dynamic General Equilibrium Modeling: Models, Methods and Macroeconomic Policy Evaluation," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1439-1512, Elsevier.
    2. Sharma, Saurabh & Behera, Harendra, 2022. "A dissection of Indian growth using a DSGE filter," Journal of Asian Economics, Elsevier, vol. 80(C).
    3. Lorenzo Burlon & Paolo D'Imperio, 2019. "The euro-area output gap through the lens of a DSGE model," Questioni di Economia e Finanza (Occasional Papers) 477, Bank of Italy, Economic Research and International Relations Area.
    4. Jesús Fernández-Villaverde, 2010. "The econometrics of DSGE models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 1(1), pages 3-49, March.
    5. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    6. Benjamín García & Sebastián Guarda & Markus Kirchner & Rodrigo Tranamil, 2019. "XMAS: An extended model for analysis and simulations," Working Papers Central Bank of Chile 833, Central Bank of Chile.
    7. Jorge Fornero & Markus Kirchner, 2018. "Learning about Commodity Cycles and Saving-Investment Dynamics in a Commodity-Exporting Economy," International Journal of Central Banking, International Journal of Central Banking, vol. 14(2), pages 205-262, March.
    8. Charalampidis, Nikolaos, 2020. "On unemployment cycles in the Euro Area, 1999–2018," European Economic Review, Elsevier, vol. 121(C).
    9. Tovar, Camilo Ernesto, 2009. "DSGE Models and Central Banks," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-31.
    10. Takuji Fueki & Ichiro Fukunaga & Hibiki Ichiue & Toyoichiro Shirota, 2016. "Measuring Potential Growth with an Estimated DSGE Model of Japan’s Economy," International Journal of Central Banking, International Journal of Central Banking, vol. 12(1), pages 1-32, March.
    11. Burlon, Lorenzo & D’Imperio, Paolo, 2020. "Reliable real-time estimates of the euro-area output gap," Journal of Macroeconomics, Elsevier, vol. 64(C).
    12. Álvarez, Luis J. & Gómez-Loscos, Ana, 2018. "A menu on output gap estimation methods," Journal of Policy Modeling, Elsevier, vol. 40(4), pages 827-850.
    13. Maik H. Wolters, 2015. "Evaluating Point and Density Forecasts of DSGE Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 74-96, January.
    14. Justiniano, Alejandro & Primiceri, Giorgio E. & Tambalotti, Andrea, 2010. "Investment shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 132-145, March.
    15. Villa, Stefania, 2013. "Financial frictions in the euro area: a Bayesian assessment," Working Paper Series 1521, European Central Bank.
    16. Stefan Leist, 2013. "Driving Forces of the Swiss Output Gap," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 149(IV), pages 493-531, December.
    17. Martin Seneca, 2010. "A DSGE model for Iceland," Economics wp50, Department of Economics, Central bank of Iceland.
    18. Coenen, Günter & Straub, Roland & Trabandt, Mathias, 2013. "Gauging the effects of fiscal stimulus packages in the euro area," Journal of Economic Dynamics and Control, Elsevier, vol. 37(2), pages 367-386.
    19. Neri, Stefano & Gerali, Andrea, 2019. "Natural rates across the Atlantic," Journal of Macroeconomics, Elsevier, vol. 62(C).
    20. Rodríguez, Aldo, 2020. "Estimación Bayesiana de un Modelo de Economía Abierta con Sector Bancario," Dynare Working Papers 52, CEPREMAP.

    More about this item

    Keywords

    Bayesian Estimation; VAR; Real Business Cycles; DSGE;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ara:wpaper:014. 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: Davit Hovhannisyan (email available below). General contact details of provider: https://edirc.repec.org/data/cbagvam.html .

    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.