Abstract
This paper assesses the empirical plausibility of the real business cycle view that shocks to real variables are the dominant sources of economic fluctuations and that monetary policy shocks play an insignificant role in determining the behavior of real variables. I reconsider the vector autoregressive model of King et al. (Am Econ Rev 81:819–840, 1991), but propose an alternative identification method, based on graphical causal models. This method selects the contemporaneous causal structure using the information incorporated in the partial correlations among the residuals. The residuals orthogonalization which follows and the study of the impulse response functions confirm the results of King et al. (Am Econ Rev 81:819–840, 1991): permanent productivity shocks are not the dominant sources of aggregate fluctuations in US economy.
Article PDF
Similar content being viewed by others
References
Anderson TW (1958). An introduction to multivariate statistical analysis. Wiley, New York
Awokuse TO and Bessler DA (2003). Vector autoregressions, policy analysis and directed acyclic graphs: an application to the U.S. economy. J Appl Econ 6: 1–24
Bernanke BS (1986). Alternative explanations of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy 25: 49–100
Bessler DA and Lee S (2002). Money and prices: US data 1869-1914 (a study with directed graphs). Emp Econ 27: 427–446
Bessler DA and Yang J (2003). The structure of interdependence in international stock markets. J Int Money Financ 22: 261–287
Demiralp S and Hoover KD (2003). Searching for the causal structure of a vector autoregression. Oxf Bull Econ Stat 65: 745–767
Doan TA (2000). RATS version 5, user’s Guide. Estima, Evanston
Faust J and Leeper EM (1997). When do long-run identifying restrictions give reliable results?. J Buss Econ Stat 15: 345–353
Granger CWJ (1988). Some recent developments in a concept of causality. J Economet 39: 199–211
Haigh M and Bessler DA (2004). Causality and price discovery: an application of directed acyclic graphs. J Buss 77: 1099–1121
Hamilton J (1994). Time series analysis. Princeton University Press, Princeton
Hoover KD (2001). Causality in macroeconomics. Cambridge University Press, Cambridge
Johansen S (1988). Statistical analysis of cointegrating vectors. J Econ Dyn Control 12: 231–254
Johansen S (1991). Estimation and hypothesis testing of cointegrating vectors in Gaussian vector autoregressive models. Econometrica 59: 1551–1580
King RG, Plosser CI, Stock JH and Watson MW (1991). Stochastic trends and economic fluctuations. Am Econ Rev 81: 819–840
Lauritzen SL (2001). Causal inference from graphical models. In: Barndorff-Nielsen, E, Cox, DR and Klüppelberg, C (eds) Complex stochastic systems., pp. CRC Press, London
Lauritzen SL and Richardson TS (2002). Chain graph models and their causal interpretations. J R Stat Soc 64: 321–361
Lehmann EL and Casella G (1998). Theory of point estimation. Springer, New York
Lütkepohl H (1991). Introduction to multiple time series analysis. Springer, Berlin
Moneta A (2003). Graphical models for structural vector autoregressions. LEM working paper series, Sant’Anna School of Advanced Studies, Pisa
Moneta A (2004a) Graphical causal models and VAR-based macroeconometrics. Unpublished Ph.D. Thesis, Sant’Anna School of Advanced Studies, Pisa
Moneta A (2004). Identification of monetary policy shocks: a graphical causal approach. Notas Económicas 20: 39–62
Pearl J (1988). Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Francisco
Pearl J (2000). Causality: models, reasoning and inference. Cambridge University Press, Cambridge
Reale M and Tunnicliffe W (2001). Identification of vector AR models with recursive structural errors using conditional independence graphs. Stat Math Appl 10: 49–65
Richardson T and Spirtes P (1999). Automated discovery of linear feedback models. In: Glymour, C and Cooper, GF (eds) Computation, causation and discovery, pp. AAAI Press and MIT Press, Menlo Park
Scheines R, Spirtes P, Glymour C and Meek C (1994). TETRAD 2: user’s manual and software. Lawrence Erlbaum Associates, New Jersey
Sims CA (1980). Macroeconomics and reality. Econometrica 48: 1–47
Spirtes P, Scheines R, Meek C, Richardson T, Glymour C, Hoijtink H, Boomsma A (1996) TETRAD 3: tools for Causal Modeling, at http://www.phil.cmu.edu/tetrad/tet3/master.htm
Spirtes P, Glymour C and Scheines R (2000). Causation, prediction, and search. MIT Press, Cambridge
Spohn W (1980). Stochastic independence, causal independence and shield-ability. J Phil Logic 9: 73–99
Stock JH and Watson MW (2001). Vector autoregressions. J Econ Perspect 15: 101–115
Swanson NR and Granger CWJ (1997). Impulse response function based on a causal approach to residual orthogonalization in vector autoregressions. J Am Stat Assoc 92: 357–367
Whittaker J (1990). Graphical models in applied multivariate statistics. Wiley, Chichester
Zellner A (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J Am Stat Assoc 57: 348–368
Author information
Authors and Affiliations
Corresponding author
Additional information
I would like to thank Peter Spirtes, Marco Lippi, and Clark Glymour for helpful comments on early versions of the paper. I am also grateful to Valentina Corradi for providing me with an updated version of the King et al. (1991) data set. The usual disclaimer applies.
Rights and permissions
Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License ( https://creativecommons.org/licenses/by-nc/2.0 ), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Moneta, A. Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis. Empir Econ 35, 275–300 (2008). https://doi.org/10.1007/s00181-007-0159-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00181-007-0159-9