Advertisement

Vector Autoregressions I: Basics

  • John D. Levendis
Chapter
Part of the Springer Texts in Business and Economics book series (STBE)

Abstract

If we take the notion of general equilibrium seriously, then everything in the economy is related to everything else. For this reason, it is impossible to say which variable is exogenous. It is possible that all variables are endogenous: they can all be caused by, and simultaneously be the cause of, some other variable.

References

  1. Amisano, G., & Giannini, C. (2012). Topics in structural VAR econometrics. Berlin: Springer Science and Business Media.Google Scholar
  2. Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655–673.Google Scholar
  3. Brandt, P. T., & Williams, J. T. (2007). Multiple time series models. Quantitative Applications in the Social Sciences (Vol. 148). Thousand Oaks, CA: Sage.Google Scholar
  4. Braun, P. A., & Mittnik, S. (1993). Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions. Journal of Econometrics, 59(3), 319–341.Google Scholar
  5. Chamberlain, G. (1982). The general equivalence of Granger and Sims causality. Econometrica: Journal of the Econometric Society, 50, 569–581.Google Scholar
  6. Christiano, L. J., Eichenbaum, M., & Evans, C. L. (1999). Monetary policy shocks: What have we learned and to what end? Handbook of Macroeconomics, 1, 65–148.Google Scholar
  7. Cooley, T. F., & LeRoy, S. F. (1985). Atheoretical macroeconometrics: A critique. Journal of Monetary Economics, 16(3), 283–308.Google Scholar
  8. DeJong, D. N., Ingram, B. F., & Whiteman, C. H. (2000). A Bayesian approach to dynamic macroeconomics. Journal of Econometrics, 98(2), 203–223.Google Scholar
  9. Dickey, D. A., Jansen, D. W., & Thornton, D. L. (1991). A primer on cointegration with an application to money and income (Technical report), Federal Reserve Bank of St. Louis.Google Scholar
  10. Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.Google Scholar
  11. Eichenbaum, M., & Singleton, K. J. (1986). Do equilibrium real business cycle theories explain postwar us business cycles? NBER Macroeconomics Annual, 1, 91–135.Google Scholar
  12. Florens, J.-P., & Mouchart, M. (1982). A note on noncausality. Econometrica: Journal of the Econometric Society, 50, 583–591.Google Scholar
  13. Friedman, M. (1969). The optimum quantity of money and other essays. Chicago: Aldine Publisher.Google Scholar
  14. Friedman, M., & Schwartz, A. J. (1963). A monetary history of the United States, 1867–1960. Princeton: Princeton University Press.Google Scholar
  15. Geweke, J., & Whiteman, C. (2006). Bayesian forecasting. Handbook of Economic Forecasting, 1, 3–80.Google Scholar
  16. Gonzalo, J., & Pitarakis, J.-Y. (2002). Lag length estimation in large dimensional systems. Journal of Time Series Analysis, 23(4), 401–423.Google Scholar
  17. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 37, 424–438.Google Scholar
  18. Granger, C. W. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2, 329–352.Google Scholar
  19. Hall, R. E. (1978). Stochastic implications of the life cycle-permanent income hypothesis: Theory and evidence. Journal of Political Economy, 86(6), 971–987.Google Scholar
  20. Hatemi-J, A. (2003). A new method to choose optimal lag order in stable and unstable VAR models. Applied Economics Letters, 10(3), 135–137.Google Scholar
  21. Hosoya, Y. (1977). On the Granger condition for non-causality. Econometrica (pre-1986), 45(7), 1735.Google Scholar
  22. Hsiao, C. (1979). Autoregressive modeling of Canadian money and income data. Journal of the American Statistical Association, 74(367), 553–560.Google Scholar
  23. Hsiao, C. (1981). Autoregressive modelling and money-income causality detection. Journal of Monetary Economics, 7(1), 85–106.Google Scholar
  24. Ivanov, V., Kilian, L., et al. (2005). A practitioner’s guide to lag order selection for VAR impulse response analysis. Studies in Nonlinear Dynamics and Econometrics, 9(1), 1–34.Google Scholar
  25. Keating, J. W. (2000). Macroeconomic modeling with asymmetric vector autoregressions. Journal of Macroeconomics, 22(1), 1–28.Google Scholar
  26. Khon, R. (1981). A characterization of Granger-Sims exogeneity. Economics Letters, 8(2), 129–133.Google Scholar
  27. Leamer, E. E. (1985). Vector autoregressions for causal inference? In Carnegie-Rochester conference series on public policy (Vol. 22, pp. 255–304) Amsterdam: North-Holland.Google Scholar
  28. Litterman, R. B. (1985). A Bayesian procedure for forecasting with vector autoregressions and forecasting with Bayesian vector autoregressions–four years of experience. Minneapolis: Federal Reserve Bank of Minneapolis.Google Scholar
  29. Lucas, R. E. (1976). Econometric policy evaluation: A critique. In Carnegie-Rochester conference series on public policy (Vol. 1, pp. 19–46). Amsterdam: Elsevier.Google Scholar
  30. Lütkepohl, H. (1982). Non-causality due to omitted variables. Journal of Econometrics, 19(2–3), 367–378.Google Scholar
  31. Lütkepohl, H. (1985). Comparison of criteria for estimating the order of a vector autoregressive process. Journal of Time Series Analysis, 6(1), 35–52.Google Scholar
  32. Otrok, C., & Whiteman, C. H. (1998). Bayesian leading indicators: Measuring and predicting economic conditions in Iowa. International Economic Review, 39, 997–1014.Google Scholar
  33. Ozcicek, O., & McMillin, D. W. (1999). Lag length selection in vector autoregressive models: Symmetric and asymmetric lags. Applied Economics, 31(4), 517–524.Google Scholar
  34. Qin, D. (2011). Rise of VAR modelling approach. Journal of Economic Surveys, 25(1), 156–174.Google Scholar
  35. Runkle, D. E. (1987). Vector autoregressions and reality. Journal of Business and Economic Statistics, 5(4), 437–442.Google Scholar
  36. Sargent, T. J. (1976). The observational equivalence of natural and unnatural rate theories of macroeconomics. Journal of Political Economy, 84(3), 631–640.Google Scholar
  37. Sargent, T. J., Sims, C. A., et al. (1977). Business cycle modeling without pretending to have too much a priori economic theory. New Methods in Business Cycle Research, 1, 145–168.Google Scholar
  38. Schorfheide, F. (2005). VAR forecasting under misspecification. Journal of Econometrics, 128(1), 99–136.Google Scholar
  39. Sims, C. A. (1972). Money, income, and causality. The American Economic Review, 62(4), 540–552.Google Scholar
  40. Sims, C. A. (1980a). Comparison of interwar and postwar business cycles: Monetarism reconsidered. The American Economic Review, 70(2), 250–257.Google Scholar
  41. Sims, C. A. (1980b). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48, 1–48.Google Scholar
  42. Stock, J. H., & Watson, M. W. (1989). Interpreting the evidence on money-income causality. Journal of Econometrics, 40(1), 161–181.Google Scholar
  43. Thornton, D. L., & Batten, D. S. (1985). Lag-length selection and tests of Granger causality between money and income. Journal of Money, Credit and Banking, 17(2), 164–178.Google Scholar
  44. Uhlig, H. (2005). What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics, 52(2), 381–419.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • John D. Levendis
    • 1
  1. 1.Department of EconomicsLoyola University New OrleansNew OrleansUSA

Personalised recommendations