Bayesian analysis of DSGE models

Econometric Reviews

Volumn 26, Issue 2-4, 2007, Pages 113-172

  An, Sungbae ^a   Schorfheide, Frank ^b  

^a SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Bayesian analysis; DSGE models; Model evaluation; Vector autoregressions

Indexed keywords

EID: 34248203740     PISSN: 07474938     EISSN: 15324168     Source Type: Journal    
DOI: 10.1080/07474930701220071     Document Type: Article

References (107)

1. Adolfson, M., Laséen, S., Lindé, J., Villani, M. (2004). Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics, forthcoming.
2. Altug, S. (1989). Time-to-build and aggregate fluctuations: some new evidence. International Economic Review 30(4):889-920.
3. Anderson, G. (2000). A reliable and computationally efficient algorithm for imposing the saddle point property in dynamic models. Manuscript, Federal Reserve Board of Governors.
4. Arulampalam, M. S., Maskell, S., Gordon, N., Clapp, T. (2002). A tutorial on particle filters for online non-linear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50 (2): 173-188.
5. Aruoba, S. B., Fernández-Villaverde, J., Rubio-Rarnírez, J. F. (2004). Comparing solution methods for dynamic equilibrium economies. Journal of Economic Dynamics and Control 30(12):2477-2508.
6. Basu, S. (1995). Intermediate goods and business cycles: implications for productivity and welfare. American Economic Review 85(3):512-531.
7. Beyer, A., Farmer, R. (2004). On the indeterminacy of new keynesian economics. ECB Working Paper 323.
8. Bierens, H. J. (2007). Econometric analysis of linearized singular dynamic stochastic general equilibrium models. Journal of Econometrics 136(2):595-627.
9. Bils, M., Klenow, P. (2004). Some evidence on the importance of sticky prices. Journal of Political Economy 112(5):947-985.
10. Binder, M., Pesaran, H. (1997). Multivariate linear rational expectations models: characterization of the nature of the solutions and their fully recursive computation. Econometric Theory 13(6):877-888.
11. Blanchard, O. J., Kahn, C. M. (1980). The solution of linear difference models under rational expectations. Econometrica 48(5):1305-1312.
12. Box, G. E. P. (1980). Sampling and Bayes inference in scientific modelling and robustness. Journal of the Royal Statistical Society Series A 143(4) :383-430.
13. Canova, F. (1994). Statistical inference in calibrated models. Journal of Applied Econometrics 9:S123-S144.
14. Canova, F. (2004). Monetary policy and the evolution of the US economy: 1948-2002. Manuscript, IGIER and Universitat Pompeu Fabra.
15. Canova, F. (2007). Methods for Applied Macroeconomic Research. NJ: Princeton University Press.
16. Canova, F., Sala, L. (2005). Back to square one: identification issues in DSGE models. Manuscript, IGIER and Bocconi University.
17. Chang, Y., Gomes, J., Schorfheide, F. (2002). Learning-by-doing as a propagation mechanism. American Economic Review 92(5):1498-1520.
18. Chang, Y., Schorfheide, F. (2003). Labor-supply shifts and economic fluctuations. Journal of Monetary Economics 50(8):1751-1768.
19. Chib, S., Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. American Statistician 49 (4):327-335.
20. Chib, S., Jeliazkov, I. (2001). Marginal likelihood from the metropolis-hastings output. Journal of the American Statistical Association 96(453):270-281.
21. Christiano, L. J. (2002). Solving dynamic equilibrium models by a methods of undetermined coefficients. Computational Economics 20(1-2):21-55.
22. Christiano, L. J., Eichenbaum, M. (1992). Current real-business cycle theories and aggregate labor-market fluctuations. American Economic Review 82(3):430-450.
23. Christiano, L. J., Eichenbaum, M., Evans, C. L. (2005). Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113(1):1-45.
24. Collard, F., Juillard, M. (2001). Accuracy of stochastic perturbation methods: the case of asset pricing models. Journal of Economic Dynamics and Control 25(6-7):979-999.
25. Crowder, M. (1988). Asymptotic expansions of posterior expectations, distributions, and densities for stochastic processes. Annals of the Institute for Statistical Mathematics 40:297-309.
26. de Walque, G., Wouters, R. (2004). An open economy DSGE model linking the Euro area and the U.S. economy. Manuscript, National Bank of Belgium.
27. Dejong, D. N., Ingram, B. F. (2001). The cyclical behavior of skill acquisition. Review of Economic Dynamics 4(3):536-561.
28. Dejong, D. N., Ingram, B. F., Whiteman, C. H. (1996). A Bayesian approach to calibration. Journal of Business and Economic Statistics 14(1):1-9.
29. Dejong, D. N., Ingram, B. F., Whiteman, C. H. (2000). A Bayesian approach to dynamic macroeconomics. Journal of Econometrics 98(2):203-223.
30. Del Negro, M. (2003). Fear of floating: a structural estimation of monetary policy in Mexico. Manuscript, Federal Reserve Bank of Atlanta.
31. Del Negro, M., Schorfheide, F. (2004). Priors from general equilibrium models for VARs. International Economic Review 45(2):643-673.
32. Del Negro, M., Schorfheide, F., Smets, F., Wouters, R. (2006). On the fit and forecasting performance of new keynesian models. Journal of Business and Economic. Statistics, forthcoming.
33. Den Haan, W. J., Marcet, A. (1994). Accuracy in simulation. Review of Economic Studies 61(1):3-17.
34. Diebold, F. X., Ohanian, L. E., Berkowitz, J. (1998). Dynamic equilibrium economies: a framework for comparing models and data. Review of Economic Studies 65(3):433-452.
35. Dridi, R., Guay, A., Renault, E. (2007). Indirect inference and calibration of dynamic stochastic general equilibrium models. Journal of Econometrics 136(2):397-430.
36. DYNARE (2005). software available from http://cepremap.cnrs.fr/dynare/.
37. Fernández-Villaverde, J., Rubio-Ramírez, J. F. (2004). Comparing dynamic equilibrium models to data: aBayesian approach. Journal of Econometrics 123(1):153-187.
38. Fernández-Villaverde, J., Rubio-Ramírez, J. F. (2005). Estimating dynamic equilibrium economies: linear versus non-linear likelihood. Journal of Applied Econometrics 20(7):891-910.
39. Galí, J., Rabanal, P. (2005). Technology shocks and aggregate fluctuations: how well does the RBC model fit postwar U.S. data. In: Gertler, M., Rogoff, K., eds. NBER Macroeconomics Annual 2004. Cambridge, MA: MIT Press, pp. 225-228.
40. GAUSS (2004). Software available from http://www.aptech.com/.
41. Gelfand, A. E., Dey, D. K. (1994). Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society Series B 56(3):501-514.
42. Gelman, A., Carlin, J., Stern, H., Rubin, D. (1995). Bayesian Data Analysis, New York: Chapman & Hall.
43. Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57(6):1317-1339.
44. Geweke, J. (1999a). Computational experiments and reality. Manuscript, University of Iowa.
45. Geweke, J. (1999b). Using simulation methods for bayesian econometric models: inference, development and communication. Econometric Reviews 18(1):1-126.
46. Geweke, J. (2005). Contemporary Bayesian Econometrics and Statistics, Hoboken: John Wiley & Sons.
47. Good, I. J. (1952). Rational decisions. Journal of the Royal Statistical Society Series B 14(1):107-114.
48. Gordon, N. J., Salmond, D. J., Smith, A. F. M. (1993). Novel approach to non-linear and non-gaussian Bayesian state estimation. IEE Proceedings-F 140(2):107-113.
49. Hammersley, J., Handscomb, D. (1964). Monte Carlo Methods. New York: John Wiley.
50. Hansen, L. P., Heckman, J. J. (1996). The empirical foundations of calibration. Journal of Economic Perspectives 10(1):87-104.
51. Hastings, W. K. (1970). Monte Carlo sampling methods using markov chain and their applications. Biometrika 57(1):97-109.
52. Ingram, B. F., Whiteman, C. M. (1994). Supplanting the Minnesota prior: forecasting macroeconomic time series using real business cycle model priors. Journal of Monetary Economics 34(3):497-510.
53. Ireland, P. N. (2004). A method for taking models to the data. Journal of Economic Dynamics and Control 28(6):1205-1226.
54. Jin, H.-H., Judd, K. L. (2002). Perturbation methods for general dynamic stochastic models. Manuscript, Hoover Institute.
55. Judd, K. L. (1998). Numerical Methods in Economics. Cambridge, MA: MIT Press.
56. Justiniano, A., Preston, B. (2004). Small open economy Dsge models - specification, estimation, and model fit. Manuscript, International Monetary Fund and Columbia University.
57. Kass, R. E., Rafterty, A. E. (1995). Bayes factors. Journal of the American Statistical Association 90(430): 773-795.
58. Kim, J. (2000). Constructing and estimating a realistic optimizing model of monetary policy. Journal of Monetary Economics 45(2):329-359.
59. Kim, J., Kim, S. H. (2003). Spurious welfare reversal in international business cycle models. Journal of International Economics 60(2):471-500.
60. Kim, J., Kim, S. H., Schaumburg, E., Sims, C. A. (2005). Calculating and using second order accurate solutions of discrete time dynamic equilibirum models. Manuscript, Princeton University.
61. Kim, J.-Y. (1998). Large sample properties of posterior densities, bayesian information criterion, and the likelihood principle in non-stationary time series models. Econometrica 66(2):359-380.
62. Kim, K. Pagan, A. (1995). Econometric analysis of calibrated macroeconomic models. In: Pesaran, H., Wickens, M., eds. Handbook of Applied Econometrics: Macroeconomics. Oxford: Basil Blackwell, pp. 356-390.
63. Kim, S., Shephard, N., Chib, S. (1998). Stochastic volatility: likelihood inference and comparison with ARCH Models. Review of Economic Studies 65(3):361-393.
64. King, R. G., Watson, M. W. (1998). The solution of singluar linear difference systems under rational expectations. International Economic. Review 39(4):1015-1026.
65. Kitagawa, G. (1996). Monte Carlo filter and smoother for non-gaussian non-linear state space models. Journal of Computational and Graphical Statistics 5(1):1-25.
66. Klein, P. (2000). Using the generalized Schur form to solve a multivariate linear rational expectations model. Journal of Economic Dynamics and Control 24(10):1405-1423.
67. Kydland, F. E., Prescott, E. C. (1982). Time to build and aggregate fluctuations. Econometrica 50 (6): 1345-1370.
68. Kydland, F. E., Prescott, E. C. (1996). The computational experiment: an econometric tool. Journal of Economic Perspectives 10(1):69-85.
69. Laforte, J.-P. (2004). Comparing monetary policy rules in an estimated equilibrium model of the US economy. Manuscript, Federal Reserve Board of Governors.
70. Lancaster, A. (2004). An Introduction to Modern Bayesian Econometrics. Oxford: Blackwell Publishing.
71. Landon-Lane, J. (1998). Bayesian Comparison of Dynamic Macroeconomic Models. Ph.D. dissertation, University of Minnesota.
72. Leeper, E. M., Sims, C. A. (1994). Toward a modern macroeconomic model usable for policy analysis. In: Stanley, F., Rotemberg, J. J., eds. NBER MAcroeconomics Annual 1994. Cambridge, MA: MIT Press, pp. 81-118.
73. Levin, A. T., Alexei O., Williams, J. C., Williams, N. (2006). Monetary policy under uncertainty in micro-founded macroeconometric models. In: Mark G., Rogoff, K., eds. NBER Macroeconomics Annual 2005. Cambridge, MA: MIT Press, pp. 229-287.
74. Lubik, T. A., Schorfheide, F. (2003). Do central banks respond to exchange rate fluctuation - a structural investigation. Manuscript, University of Pennsylvania.
75. Lubik, T. A., Schorfheide, F. (2004). Testing for indeterminacy: an application to US monetary policy. American Economic Review 94(1):190-217.
76. Lubik, T. A., Schorfheide, F. (2006). A Bayesian look at new open economy macroeconomics. In: Mark, G., Rogoff, K., eds. NBER Macroeconomics Annual 2005. Cambridge, MA: MIT Press, pp. 313-366.
77. MATLAB (2005). Version 7. Software available from http://www.mathworks. com/.
78. McGrattan, E. R. (1994). The macroeconomic effects of distortionary taxation. Journal of Monetary Economics 33(3):573-601.
79. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics 21(6):1087-1092.
80. Onatski, A., Williams, N. (2004). Empirical and policy performance of a forward-looking monetary model. Manuscript, Princeton University.
81. Otrok, C. (2001). On measuring the welfare cost of business cycles. Journal of Monetary Economics 47(1):61-92.
82. Phillips, P. C. B. (1996). Econometric model determination. Econometrica 64(4):763-812.
83. Pitt, M. K., Shephard, N. (1999). Filtering via simulation: Auxiliary particle filter's. Journal of the American Statistical Association 94(446):590-599.
84. Poirier, D. (1998). Revising beliefs in nonidentified models. Econometric Theory 14(4):483-509.
85. Rabanal, P., Rubio-Ramírez, J. F. (2005a). Comparing new keynesian models in the Euro area: A bayesian approach. Federal Reserve Bank of Atlanta Working Paper 2005-8.
86. Rabanal, P., Rubio-Ramírez, J. F. (2005b). Comparing new keynesian models of the business cycle: A Bayesian approach. Journal of Monetary Economics 52(6):1152-1166.
87. Rabanal, P., Tuesta, V. (2006). Euro-Dollar real exchange rate dynamics in an estimated two-country model: What is important and what is not. CEPR Discussion Paper No. 5957.
88. Robert, C., Casella, G. (1999). Monte Carlo Statistical Methods. New York: Springer Verlag.
89. Roternberg, J. J., Woodford, M. (1997). An optimization-based econometric framework for the evaluation of monetary policy. In: Bernanke, B. S., Rotemberg, J. J., eds. NBER Macroeconomics Annual 1997. Cambridge, MA: MIT Press, pp. 297-346.
90. Sargent, T. J. (1989). Two models of measurements and the investment accelerator. Journal of Political Economy 97(2):251-287.
91. Schmitt-Grohé, S., Uribe, M. (2006). Optimal simple and implementable monetary and fiscal rules. Journal of Monetray Economics. Duke University.
92. Schorfheide, F. (2000). Loss function-based evaluation of DSGE models. Journal of Applied Econometrics 15(6):645-670.
93. Schorfheide, F. (2005). Learning and monetary policy shifts. Review of Economic Dynamics 8(2):392-419.
94. Sims, C. A. (1996). Macroeconomics and methodology. Journal of Economic Perspectives 10(1):105-120.
95. Sims, C. A. (2002). Solving linear rational expectations models. Computational Economics 20(1-2):1-20.
96. Sims, C. A. (2003). Probability models for monetary policy decisions. Manuscript, Princeton University.
97. Smets, F., Wouters, R. (2003). An estimated dynamic stochastic general equilibrium model of the Euro area. Journal of European Economic Association 1(5):1123-1175.
98. Smets, F., Wouters, R. (2005). Comparing shocks and frictions in US and Euro area business cycles: a Bayesian DSGE Approach. Journal of Applied Econometrics 20(2):161-183.
99. Smith, A. A. (1993). Estimating non-linear time-series models using simulated vector autoregressions. Journal of Applied Econometrics 8:S63-S84.
100. Swanson, E., Anderson, G., Levin, A. (2005). Higher-Order perturbation solutions to dynamic, discrete-time rational expectations models. Manuscript, Federal Reserve Bank of San Fransisco.
101. Taylor, J. B., Uhlig, H. (1990). Solving non-linear stochastic growth models: a comparison of alternative solution methods. Journal of Business and Economic Statistics 8(1):1-17.
102. Uhlig, H. (1999). A toolkit for analyzing non-linear dynamic stochastic models easily. In: Marimón, R., Scott, A., eds. Computational Methods for the Study of Dynamic Economies. Oxford, UK: Oxford University Press, pp. 30-61.
103. Walker, A. M. (1969). On the asymptotic behaviour of posterior distributions. Journal of the Royal Statistical Society Series B 31(1):80-88.
104. Watson, M. W. (1993). Measures of fit for calibrated models. Journal of Political Economy 101(6):1011-1041.
105. White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica 50(1):1-25.
106. Woodford, M. (2003). Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton, NJ: Princeton University Press.
107. Zeller, A. (1971). Introduction to Bayesian Inference in Econometrics. New York: John Wiley & Sons.