Inference in nearly nonstationary SVAR models with long-run identifying restrictions

Journal of Business and Economic Statistics

Volumn 28, Issue 1, 2010, Pages 1-12

  Gospodinov, Nikolay ^a  

^a Dept of Electrical and Computer Engineering   (Canada)

Author keywords

Impulse response analysis; Local to unity asymptotics; Long run restrictions; Persistence

Indexed keywords

EID: 73149083749     PISSN: 07350015     EISSN: None     Source Type: Journal    
DOI: 10.1198/jbes.2009.08116     Document Type: Article

References (30)

1. Blanchard, O., and Quah, D. (1989), "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, 79, 655-673. [1]
2. Chari, V., Kehoe, P., and McGrattan, E. (2005), "A Critique of Structural VARs Using Real Business Cycle Theory," Working Paper 631, Federal Reserve Bank of Minneapolis. [1]
3. Christiano, L., Eichenbaum, M., and Vigfusson, R. (2003), "What Happens After a Technology Shock?" International Finance Discussion Paper 768, Board of Governors of the Federal Reserve System. [2-4]
4. (2006), "Assessing Structural VARs," in NBER Macroeconomics Annual, Vol.21, eds. D. Acemoglu, K. Rogoff, and M.Woodford, Cambridge, MA: MIT Press, pp. 1-72. [1,6]
5. Cooley, T., and Dwyer, M. (1998), "Business Cycle Analysis Without Much Theory: A Look at Structural VARs," Journal of Econometrics, 83, 57-88. [1,4]
6. Elliott, G. (1998), "On the Robustness of Cointegration Methods When Regressors Almost Have Unit Roots," Econometrica, 66, 149-158. [2]
7. Faust, J., and Leeper, E. (1997), "When Do Long-Run Restrictions Give Reliable Results?" Journal of Business & Economic Statistics, 15, 345-353. [1]
8. Francis, N., and Ramey, V. (2005), "Is the Technology-Driven Real Business Cycle Hypothesis Dead? Shocks and Aggregate Fluctuations Revisited," Journal of Monetary Economics, 52, 1379-1399. [1,2]
9. Fry, R., and Pagan, A. (2005), "Some Issues in Using VARs for Macroeconometric Research," CAMA Working Paper 19, Australian National University. [3]
10. Gali, J. (1999), "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?" American Economic Review, 89, 249-71. [1,2]
11. Gospodinov, N. (2004), "Asymptotic Confidence Intervals for Impulse Responses of Near-Integrated Processes," Econometrics Journal, 7, 505-527. [10]
12. Gospodinov, N., Maynard, A., and Pesavento, E. (2008), "Sensitivity of Impulse Responses to Small Low Frequency Co-Movements: Reconciling the Evidence on the Effects of Technology Shocks," unpublished manuscript, Concordia University. [6]
13. Hahn, J., and Kuersteiner, G. (2002), "Discontinuities ofWeak Instrument Limiting Distributions," Economics Letters, 75, 325-331. [3]
14. Hahn, J., Hausman, J., and Kuersteiner, G. (2007), "Long Difference Instrumental Variables Estimation for Dynamic Panel Models With Fixed Effects," Journal of Econometrics, 140, 574-617. [4]
15. Han, C., and Phillips, P. C. B. (2007), "GMM Estimation for Dynamic Panels With Fixed Effects and Strong Instruments at Unity," Cowles Foundation Discussion Paper 1599, Yale University. [4]
16. Hausman, J. A., Newey, W. K., and Taylor, W. E. (1987), "Efficient Estimation and Identification of Simultaneous Equation Models With Covariance Restrictions," Econometrica, 55, 849-874. [3]
17. Levtchenkova, S., Pagan, A., and Robertson, J. (1998), "Shocking Stories," Journal of Economic Surveys, 12, 507-532. [4]
18. Pagan, A., and Robertson, J. (1998), "Structural Models of the Liquidity Effect," Review of Economics and Statistics, 80, 202-217. [3]
19. Pesavento, E., and Rossi, B. (2005), "Do Technology Shocks Drive Hours Up or Down? A Little Evidence From an Agnostic Procedure," Macroeconomic Dynamics, 9, 478-488. [1,2,5]
20. (2006), "Small Sample Confidence Intervals for Multivariate Impulse Response Functions at Long Horizons," Journal of Applied Econometrics, 21, 1135-1155. [2,4,5,10]
21. Phillips, P. C. B. (1988), "Regression Theory for Near-Integrated Time Series," Econometrica, 56, 1021-1043. [2,7]
22. (1998), "Impulse Response and Forecast Error Variance Asymptotics in Nonstationary VARs," Journal of Econometrics, 83, 21-56. [5,10]
23. Sarte, P. (1997), "On the Identification of Structural Vector Autoregressions," Economic Quarterly, 83 (3), 45-67. [3,4]
24. Shapiro, M., and Watson, M. W. (1988), "Sources of Business Cycle Fluctuations," NBER Macroeconomics Annual, 3, 111-156. [3]
25. Staiger, D., and Stock, J. H. (1997), "Instrumental Variables Regression With Weak Instruments," Econometrica, 65, 557-586. [3]
26. Stock, J. H. (1991), "Confidence Intervals for the Largest Autoregressive Root in U.S. Macroeconomic Time Series," Journal of Monetary Economics, 28, 435-459. [2,5]
27. Stock, J. H., and Yogo, M. (2005), "Testing for Weak Instruments in Linear IV Regression," in Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, eds. D. W. K. Andrews and J. H. Stock, Cambridge: Cambridge University Press, pp. 80-108. [4]
28. Watson, M. W. (1994), "Vector Autoregressions and Cointegration," in Handbook of Econometrics, Vol.4, eds. R. Engle and D. McFadden, Amsterdam: North Holland. [3]
29. (2006), Comment on "Assessing Structural VARs," by L. Christiano, M. Eichenbaum, and R. Vigfusson, in NBER Macroeconomics Annual, Vol.21, eds. D. Acemoglu, K. Rogoff, and M. Woodford, Cambridge, MA: MIT Press, pp. 97-102. [3,4]
30. Wright, J. H. (1996), "Inference for Impulse Response Functions," unpublished manuscript, Harvard University. [2]