Vector Autoregressions
Vector Autoregressions
By: James H. Stock and Mark W. Watson
A Critique Paper presented to
The Faculty of the School of Economics
De La Salle University - Manila
In partial fulfillment
Of the course requirements in
Advanced Econometrics (ECOMET2)
3rd Term, AY 2014 - 2015
Submitted to:
Dr. Cesar C. Rufino
Submitted by:
Arjonillo Jr., Rabboni Francis K.
11148624
V25
March 4, 2015
James H. Stock and Mark W. Watson are both professors in Political Economy and Econometrics respectively. They assess the competence of VARs or Vector Autogregressions on the four macroeconomic tasks, which are data description, forecasting, structural inference and policy analysis. In the 1970’s, these four tasks were used with a variety of techniques and models but were somewhat inefficient and unreliable by the time the inflationary chaos of the 1970’s set in. In 1980, a man named Christopher Sims presented his own macroeconomic framework: vector autoregressions or VARs. According to Sims, the VAR is an n equation, n variable linear model wherein each of the variables are explained by its own lagged values including past and current values of the remaining n-1 variables. This is obviously a level up at that time from a univariate autoregression in which from the term “uni” means having one equation and one variable linear model. According to Sims, this simple framework provides a systematic way to capture rich dynamics in multiple time series with its best feature is that it is easy to comprehend. Sims (1980) argued that the VARs is a comprehensible approach to the four macroeconomic tasks. In the study made by James Stock and Mark Watson, they find that it does not quite live up to its supposed argument by Sims mentioned earlier that it would satisfy all four tasks. Although, this does not mean that it is unreliable to all either. It depends. In data description and forecasting, VARS have proven to be reliable. However, structural inference and policy
By: James H. Stock and Mark W. Watson
A Critique Paper presented to
The Faculty of the School of Economics
De La Salle University - Manila
In partial fulfillment
Of the course requirements in
Advanced Econometrics (ECOMET2)
3rd Term, AY 2014 - 2015
Submitted to:
Dr. Cesar C. Rufino
Submitted by:
Arjonillo Jr., Rabboni Francis K.
11148624
V25
March 4, 2015
James H. Stock and Mark W. Watson are both professors in Political Economy and Econometrics respectively. They assess the competence of VARs or Vector Autogregressions on the four macroeconomic tasks, which are data description, forecasting, structural inference and policy analysis. In the 1970’s, these four tasks were used with a variety of techniques and models but were somewhat inefficient and unreliable by the time the inflationary chaos of the 1970’s set in. In 1980, a man named Christopher Sims presented his own macroeconomic framework: vector autoregressions or VARs. According to Sims, the VAR is an n equation, n variable linear model wherein each of the variables are explained by its own lagged values including past and current values of the remaining n-1 variables. This is obviously a level up at that time from a univariate autoregression in which from the term “uni” means having one equation and one variable linear model. According to Sims, this simple framework provides a systematic way to capture rich dynamics in multiple time series with its best feature is that it is easy to comprehend. Sims (1980) argued that the VARs is a comprehensible approach to the four macroeconomic tasks. In the study made by James Stock and Mark Watson, they find that it does not quite live up to its supposed argument by Sims mentioned earlier that it would satisfy all four tasks. Although, this does not mean that it is unreliable to all either. It depends. In data description and forecasting, VARS have proven to be reliable. However, structural inference and policy
References: Bernanke, Ben S. and Alan Blinder. 1992. “The Federal Funds Rate and the Channels of Monetary Transmission.” American Economic Re- view. September, 82:4, pp. 901–21. Bernanke, Ben S. and Ilian Mihov. 1998. “Mea- suring Monetary Policy.” Quarterly Journal of Eco- nomics. August, 113:3, pp. 869–902. Sims, Christopher A. 1980. “Macroeconomics and Reality.” Econometrica. January, 48:1, pp. 1– 48. Stock, James H. and Mark W. Watson. 1996. “Evidence on Structural Instability in Macroeco- nomic Time Series Relations.” Journal of Business and Economic Statistics. January, 14:1, pp. 11–30. Stock, James H. 1997. “Cointegration, Long- Run Comovements, and Long-Horizon Forecast- ing,” in Advances in Econometrics: Proceedings of the Seventh World Congress of the Econometric Society, Volume III. David Kreps and Kenneth F. Wallis, eds. Cambridge: Cambridge University Press, pp. 34–60. Watson, Mark W. 1994. “Vector Autoregres- sions and Cointegration,” in Handbook of Econo- metrics, Volume IV. Robert Engle and Daniel Mc- Fadden, eds. Amsterdam: Elsevier Science Ltd., pp. 2844–915.