Applied Econometrics Methods and Monetary Policy: Empirical Evidence from the Mexican Case

  • Luis Miguel Galindo
  • Horacio Catalán
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 551)


The main objective of this paper is to illustrate, using Mexican data, how the results yield by modern econometric methods are dependent upon each specific technique as well as upon the statistical properties of the series analyzed. The problems are even stronger and more evident in the case of economic series with structural changes and high variability as is the case of Mexico. Applied econometrics should be explicitly based upon a probability viewpoint, and different methods should be taken to produce only approximations to the actual data generation process. Thus, alternative techniques can only show distinctive features of the actual data that still need to be validated with the rest of empirical evidence. This indicates that applied econometricians have to look for maximum information by correctly applying different techniques without forgetting the relevance of economic reasoning. Using Mexican data, alternative econometric estimations are evaluated indicating that the formulation of a monetary policy only on the basis of some specific technique, without considering its potential pitfalls, should not be recommended.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Badillo, R., J. Belaire-France, and D. Contreras, (2002), Spurious rejection of the stationary hypothesis in the presence of a break point, Applied Economics, 34, 1917–1923.CrossRefGoogle Scholar
  2. 2.
    Baffes, J. and J.C. Valle, (2003), Unit root versus trend stationary in growth rate estimation, Applied Economic Letters, 10, 9–14.Google Scholar
  3. 3.
    Bahmani-Oskooee, M. and T.J. Brooks, (2003), A new criteria for selecting the optimum lags in Johansens cointegration techniques, Applied Economics, 35, 875–880.CrossRefGoogle Scholar
  4. 4.
    Bailliu, J., Garcés, D. and M. Messmacher, (2003), Explicación y descripción de la inflación en mercados emergentes: el caso de México, Bank of Canada y Banco de México, Documentos de Investigación, No. 2000-3, February.Google Scholar
  5. 5.
    Banco de México (1996), La conducción de la política monetaria del Banco de México a través del régimen de saldos acumulados, Informe Anual del Banco de México, Anexo 4.Google Scholar
  6. 6.
    Caporale, G.M. and N. Pittis, (1999), Efficient estimation of cointegrating vectors and testing for causality in vector autoregressions, Journal of Economic Surveys, vol. 13, No. 1, 1–35.Google Scholar
  7. 7.
    Carstens A. and A. Reynoso (1997), Alcances de la política monetaria: marco teórico y regularidades empíricas en la experiencia mexicana, Documento de Investigación, no. 9705, Banco de México, pp. 45.Google Scholar
  8. 8.
    Castellanos, S.G. (2000), El efecto del corto sobre la estructura de tasas de interes, Documento de Investigación No. 2000-1, Dirección General de Investigación económica, Banco de México.Google Scholar
  9. 9.
    Chang-Jin, K. and C.R. Nelson (2000), State space models with regime switching, MIT Press.Google Scholar
  10. 10.
    Charemza, W.W. and E.M. Syczewska (1998), Joint application of the Dickey-Fuller and KPSS tests, Economic Letters, 61, 17–21.CrossRefGoogle Scholar
  11. 11.
    Chen, M.Y. (2002), Testing stationary against unit roots and structural changes, Applied Economic Letters, 9, 459–464.Google Scholar
  12. 12.
    Copeland, M. and A.M. Werner, (1997), El mecanismo de la transmisión monetaria en México, Trimestre Económico, 75–104.Google Scholar
  13. 13.
    Díaz de León, A. and L. Greenham, (2001), Política monetaria y tasas de interés: experiencia reciente para el caso de México, Economía Mexicana. Nueva poca, vol. X, no. 2, segundo semestre, 213–208.Google Scholar
  14. 14.
    De Jong, D.N. and C.H. Whiteman (1991), Reconsidering trends and random walks in macroeconomic time series, Journal of Monetary Economics, 28, pp. 221–254.Google Scholar
  15. 15.
    Dickey, D. A. and W.A. Fuller, (1981), Likelihood ratio statistics for autoregressive time series with a unit root, Econometrica, 49, pp.1057–1072.MathSciNetGoogle Scholar
  16. 16.
    Dolado, J. J. and H. Lutkepohl, (1996), Making Wald tests work for cointegrated VAR systems, Econometric Reviews, 15,4, 369–386.Google Scholar
  17. 17.
    Doornick, J.A., D.F. Hendry, and B. Nielsen, (1998), Inference in cointegrating models: UK M1 revisited, Journal of Economic Surveys, vol. 12, no. 5, 533–572.Google Scholar
  18. 18.
    Engle, R.F. and C.W.J. Granger (1987), “Cointegration and error correction” representation, estimation and testing”, Econometrica, 55, 251–276MathSciNetGoogle Scholar
  19. 19.
    Engle, R.F. D.F. Hendry and J. Richard (1983), “Exogeneity”, Econometrica, 51Google Scholar
  20. 20.
    Ericsson, N.R. and J.S. Irons (eds) (1994) Testsing exogeneity, Oxford University Press.Google Scholar
  21. 21.
    Favero, C.A. (2001), Applied Macroeconometrics, Oxford University Press.Google Scholar
  22. 22.
    Favero, C.A. (2000), New econometric techniques and macroeconomics, in R. Backhouse (ed.), Macroeconomics and the real world, Oxford University Press, pp. 225–236.Google Scholar
  23. 23.
    Franses, P.H. (2001), How to deal with intercept and trend in practical cointegration analysis, Applied Economics, 33, 577–579.Google Scholar
  24. 24.
    Frances, P.H. and M. McAller, (1998), Cointegration analysis of seasonal time series, Journal of Economic Surveys, vol. 12, no. 5, 651–678.Google Scholar
  25. 25.
    Galindo, L.M. and M.E. Cardero, (1997), Un modelo econométrico de vectores autoregresivos y cointegración de la economía mexicana, 1980–1996, Economía Mexicana, Nueva Época, vol. VI, no. 2, segundo semestre, pp. 223–247.Google Scholar
  26. 26.
    Garcés, D.G. (2002), Agregados monetarios, inflación y actividad económica, Documento de investigación, No. 2002-07, Banco de México.Google Scholar
  27. 27.
    Gil-Díaz, F. (1997), La política monetaria y sus canales de transmisión en MéxicoGoogle Scholar
  28. 28.
    Gaceta de Economía, suplemento, ao 3, no. 5, 79–102. 25Google Scholar
  29. 29.
    Granger, C.W.J. and P. Newbold (1974), Spurious regression in econometrics, Journal of Econometrics, 2, pp. 111–120.CrossRefGoogle Scholar
  30. 30.
    Granger, C.W.J. (1983), “Developments in the study of cointegrated economic variables”, Oxford Bullettin of Economics and statistics, 48, 213–228Google Scholar
  31. 31.
    Gunter, J.W. and R.R. Moore, (1993), Credito y actividad económica en México, Economía Mexicana, Nueva Época, vol. II, no. 2, julio-diciembre, 415–428.Google Scholar
  32. 32.
    Hallman, J.J., R.D. Porter, and D.H. Small (1991), Is the price level tied to the M2 monetary aggregate in the long run?, American Economic Review, vol. 81, No. 4, 841–858.Google Scholar
  33. 33.
    Hatemi, A. (2003), A new method to choose optimal lag order in stable and unstable VAR models, Applied Economics Letters, 10, 135–137.Google Scholar
  34. 34.
    Hendry, D.F. and M.P. Clements (1999), Forecasting non-stationary economic time series, M.I.T. Press.Google Scholar
  35. 35.
    Hoover, K.D. (2000), Models all the way down: comments on Smith and Juselius, in in R. Backhouse (ed.), Macroeconomics and the real world, Oxford University Press, pp. 219–223.Google Scholar
  36. 36.
    Ibarra, C. (1998), Exchange rate policy credibility in Mexico, 1991–1994, Economía Mexicana, Nueva Época, vol. VII, segundo semestre, 229–266.Google Scholar
  37. 37.
    Johansen, S. (1995), Likelihood-based inference in cointegrated vector autoregressive models, Oxford University Press.Google Scholar
  38. 38.
    Johansen, S. (1988), Statistical analysis of co-integrating vector, Journal of Economics, Dynamics and Control, 12, pp. 231–54.MATHMathSciNetGoogle Scholar
  39. 39.
    Johansen, S. and B.G. Nielsen (1993) Asymptotics for the Cointegrqation Rank Tests in the Presence of Intervention Dummies. Manual for the Simulation Program DisCo. Working Paper, University of Copenaghen.Google Scholar
  40. 40.
    Juseluis, K, (2000), Models and relations in economics and econometrics, in R. Backhouse (ed.), Macroeconomics and the real world, Oxford University Press, pp. 168–197.Google Scholar
  41. 41.
    Kwiatkowsky, D., P.C.B. Phillips, P. Schmidt and Y. Shin, (1992), Testing the null hypothesis of stationary against the alternative of a unit root, Journal of Econometrics, 54, pp. 159–178.Google Scholar
  42. 42.
    Lee, J. and M. Strazicich, (2001), Testing the null of stationary in the presence of a structural break, Applied Economic Letters, 8, 377–382.Google Scholar
  43. 43.
    Leybourne, S.J. and P. Newbold, (2003), Spurious rejections by cointegration tests induced by structural breaks, Applied Economics, 35, 1117–1121.CrossRefGoogle Scholar
  44. 44.
    Maddala, G.S. and I-M. Kim (1998), Unit roots, cointegration and structural change, Cambridge University Press.Google Scholar
  45. 45.
    Martínez, L., O. Sánchez, and A. Werner, (2001), Consideraciones sobre la conducción de la política monetaria y el mecanismo de transmisión en México, Banco de México, Documento de Investigacion no. 2001-02, marzo.Google Scholar
  46. 46.
    McCallum, B.T. (2000), Recent developments in monetary policy analysis: the roles of theory and policy, in R. Backhouse (ed.), Macroeconomics and the real world, Oxford University Press, pp. 115–1139.Google Scholar
  47. 47.
    Mills, T, (1998), “Recent developments in modeling nonstationary vectors autoregressions”, Journal of Economic Surveys, 12,3, 279–311CrossRefGoogle Scholar
  48. 48.
    Naka, A. and D. Tufte, (1997), Examining impulse response functions in cointegrated systems, Applied Economics, 29, 1593–1603.Google Scholar
  49. 49.
    Ng, S. and P. Perron, (1995), Unit root tests in ARMA models with data depend methods for the selection of the truncation lag, Journal of the American Statistical Association, 90, pp. 268–281.MathSciNetGoogle Scholar
  50. 50.
    Nelson, C.R. and C.I. Plosser (1982), Trends versus random walks in economic time series. Some evidence and implications, Journal of Monetary Economics, 10, pp. 139–162.CrossRefGoogle Scholar
  51. 51.
    Ohanian, L.E. (1988), The spurious effects of unit roots on vector autoregressions, Journal of Econometrics, 39, 251–266.CrossRefMathSciNetGoogle Scholar
  52. 52.
    Patterson, K. and S. Heravi, (2003), Weighted symmetric tests for a unit root: response functions, power, test dependence and test conflict, Applied Economics, 35, 779–790.Google Scholar
  53. 53.
    Perron P.C.B. (1997), Further evidence on breaking trend functions in macroeconomic variables, Journal of Econometrics, 80, pp. 355–385.CrossRefMATHMathSciNetGoogle Scholar
  54. 54.
    Phillips, P.C.B. and P. Perron, (1988), Testing for unit roots in time series regression, Biometrika, 75, pp. 335–346.MathSciNetGoogle Scholar
  55. 55.
    Phillips, P.C.B. and Z. Xiao, (1998), A primer on unit root testing, Journal of economic Surveys, vol. 12, No. 5, 423–469.CrossRefGoogle Scholar
  56. 56.
    Obstfeld, M. y K. Rogoff (1996), Foundations of international macroeconomics, The M.I.T. Press.Google Scholar
  57. 57.
    Ozcicek, O. and D. McMillan, (1999). “Lag length selection in vector autoregressive models: symmetric and asymmetric lags”, Applied Economics, 31, 517–524Google Scholar
  58. 58.
    Pesaran, M.H. and R.P. Smith, (1998), Structural analysis of cointegrating VARs, Journal of Economic Surveys, Vol. 12, No. 5, 471–505.CrossRefGoogle Scholar
  59. 59.
    Schwartz, M.J., Tijerina, A. and Torre, L. (2002), Volatilidad del tipo de cambio y tasas de interes en México: 1996–2001, Economía Mexicana, Nueva Época, vol. XI, no. 2, Segundo semester, 299–331.Google Scholar
  60. 60.
    Sims, C, J. Stock and M. Watson (1990), Inference in linear time series models with some unit roots, Econometrica, 58,1, pp. 113–144.MathSciNetGoogle Scholar
  61. 61.
    Spanos, A. (1986), Statistical foundations of econometric modeling, Cambridge University Press.Google Scholar
  62. 62.
    Toda, H.Y. and P.C.B. Phillips, (1993.a), Vector autoregressions and causality, Econometrica, 61,6, 1367–1393.MathSciNetGoogle Scholar
  63. 63.
    Toda, H.Y. and P.C.B. Phillips (1993), The spurious effect of unit roots on vector autoregressions, Journal of Econometrics, 59, 229–255.CrossRefMathSciNetGoogle Scholar
  64. 64.
    Toda, H.Y. and T. Yamamoto, (1995), Statistical inference in vector autoregressions with possible integrated process, Journal of Econometrics, 66, 225–250.CrossRefMathSciNetGoogle Scholar
  65. 65.
    Walsh, C.E. (2000), Monetary theory and policy, The M.I.T. Press.Google Scholar
  66. 66.
    Weber, C. E. (2001) “F-test for lag length selection in Argumented Dickey-Fuller regression: some Monte Carlo Evidence”, Applied Economics Letters, 8, pp. 455–458CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Luis Miguel Galindo
    • 1
  • Horacio Catalán
    • 1
  1. 1.Faculty of EconomicsUNAMMexico

Personalised recommendations