[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/rbp/wpaper/2010-018.html
   My bibliography  Save this paper

Estimación de la Tasa Natural de Interés para el Perú: Un Enfoque Financiero

Author

Listed:
  • Pereda, Javier

    (Banco Central de Reserva del Perú)

Abstract
En el presente trabajo se estima la tasa natural de interés para el Perú en el periodo 2004-2010 mediante dos modelos dentro de un enfoque financiero: un modelo basado en la paridad de intereses y el otro basado en la tasa forward de la curva de rendimiento. La ventaja práctica de los modelos financieros es que son modelos de fácil aplicación y permiten estimados de alta frecuencia. Los estimados de la tasa natural de interés de ambos modelos indicarían una tendencia decreciente de la tasa natural de interés en el periodo analizado, en línea con la evidencia reportada para otros países, así como una posición de política monetaria expansiva durante dicho periodo.

Suggested Citation

  • Pereda, Javier, 2010. "Estimación de la Tasa Natural de Interés para el Perú: Un Enfoque Financiero," Working Papers 2010-018, Banco Central de Reserva del Perú.
    • Handle: RePEc:rbp:wpaper:2010-018
    as

    Download full text from publisher

    File URL: https://www.bcrp.gob.pe/docs/Publicaciones/Documentos-de-Trabajo/2010/Documento-de-Trabajo-18-2010.pdf
    File Function: Application/pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    2. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
    3. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
    4. Helmut Lütkepohl, 1985. "Comparison Of Criteria For Estimating The Order Of A Vector Autoregressive Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 6(1), pages 35-52, January.
    5. Geweke, John & Meese, Richard, 1981. "Estimating regression models of finite but unknown order," Journal of Econometrics, Elsevier, vol. 16(1), pages 162-162, May.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    7. Robert J. Gordon & Stephen R. King, 1982. "The Output Cost of Disinflation in Traditional and Vector Autoregressive Models," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 13(1), pages 205-244.
    8. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
    9. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    10. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
    11. Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
    12. Preston J. Miller & Thomas M. Supel & Thomas H. Turner, 1980. "Estimating the effects of the oil-price shock," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 4(Win).
    13. Yi, Gang & Judge, George, 1988. "Statistical model selection criteria," Economics Letters, Elsevier, vol. 28(1), pages 47-51.
    14. Nickelsburg, Gerald, 1985. "Small-sample properties of dimensionality statistics for fitting VAR models to aggregate economic data : A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 28(2), pages 183-192, May.
    15. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    16. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    17. Clifford M. Hurvich & Chih‐Ling Tsai, 1993. "A Corrected Akaike Information Criterion For Vector Autoregressive Model Selection," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(3), pages 271-279, May.
    18. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
    19. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 161-179, April.
    20. Guy Melard & Jean-Michel Pasteels, 2000. "Automatic ARIMA modeling including interventions, using time series expert software," ULB Institutional Repository 2013/13744, ULB -- Universite Libre de Bruxelles.
    21. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-1291, November.
    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. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, CEMLA, vol. 0(4), pages 517-589, octubre-d.
    2. Tamara Burdisso & Eduardo Ariel Corso, 2011. "Incertidumbre y dolarización de cartera: el caso argentino en el último medio siglo," Monetaria, CEMLA, vol. 0(4), pages 461-515, octubre-d.
    3. Carlos A. Medel Vera, 2011. "¿Akaike o Schwarz? ¿Cuál utilizar para predecir el PIB chileno?," Monetaria, CEMLA, vol. 0(4), pages 591-615, octubre-d.
    4. Carlos Medel, 2012. "¿Akaike o Schwarz? ¿Cuál elegir para Predecir el PIB Chileno?," Working Papers Central Bank of Chile 658, Central Bank of Chile.
    5. Carlos A. Medel & Sergio C. Salgado, 2013. "Does the Bic Estimate and Forecast Better than the Aic?," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 28(1), pages 47-64, April.
    6. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    7. Kilian, Lutz & Ivanov, Ventzislav, 2001. "A Practitioner's Guide to Lag-Order Selection for Vector Autoregressions," CEPR Discussion Papers 2685, C.E.P.R. Discussion Papers.
    8. Carlos A. Medel, 2015. "Probabilidad Clásica de Sobreajuste con Criterios de Información: Estimaciones con Series Macroeconómicas Chilenas," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 30(1), pages 57-72, Abril.
    9. Ivanov Ventzislav & Kilian Lutz, 2005. "A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-36, March.
    10. Luca Brugnolini, 2018. "About Local Projection Impulse Response Function Reliability," CEIS Research Paper 440, Tor Vergata University, CEIS, revised 09 Jun 2018.
    11. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    12. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    13. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    14. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    15. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    16. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    17. Singh, Tarlok, 2010. "Does domestic saving cause economic growth? A time-series evidence from India," Journal of Policy Modeling, Elsevier, vol. 32(2), pages 231-253, March.
    18. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    19. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.
    20. Terrence Kinal & Jonathan Ratner, 1986. "A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy," International Regional Science Review, , vol. 10(2), pages 113-126, August.

    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:rbp:wpaper:2010-018. 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: Research Unit (email available below). General contact details of provider: https://edirc.repec.org/data/bcrgvpe.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.