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Research Articles

Dynamic stochastic general equilibrium (DSGE) modelling in practice: identification, estimation and evaluation

Pages 107-134 | Received 30 Jun 2016, Accepted 13 Jul 2016, Published online: 24 Aug 2016

References

  • Adamic, L. 2011. Complex Systems: Unzipping Zipf’s Law. Nature 474: 164–165. doi:10.1038/474164a.
  • Altug, S. 1989. Time-to-Build and Aggregate Fluctuations: Some New Evidence. International Economic Review 30, no. 4: 889–920. doi:10.2307/2526758.
  • Alvarez-Lois, P., R. Harrison, L. Piscitelli, and A. Scott. 2008. On the Application and Use of DSGE Models. Journal of Economic Dynamics and Control 32: 2428–2452. doi:10.1016/j.jedc.2007.09.003.
  • Andrews, I., and A. Mikusheva. 2011. Maximum Likelihood Inference in Weakly Identified DSGE Models. MIT Department of Economics Working Paper No. 11-03.
  • Andrle, M. 2010. A Note on Identification Patterns in DSGE Models. European Central Bank Working Paper Series No. 1235 August.
  • Aoki, M., and H. Yoshikawa. 2006. A Reconstruction of Macro: A Perspective from Statistical Physics and Combinatorial Stochastic Processes. Cambridge: Cambridge University Press.
  • Axtell, R.L. 2001. Zipf Distribution of U.S. Firm Sizes. Science 293, no. 5536: 1818–1820. doi:10.1126/science.1062081.
  • Bartlett, M.S. 1950. Periodogram Analysis and Continuous Spectra. Biometrika 37: 1–16. doi:10.1093/biomet/37.1-2.1.
  • Besag, J. 1986. On Statistical Analysis of Dirty Pictures (with Discussion). Journal of the Royal Statistical Society, Ser. B 48: 259–302.
  • Blanchard, O.J., and C.M. Kahn. 1980. The Solution of Linear Difference Models under Rational Expectations. Econometrica 48, no. 5: 1305–1311. doi:10.2307/1912186.
  • Brillinger, D. 1981. Time Series: Data Analysis and Theory. San Francisco, CA: Holden Day.
  • Brush, S.G. 1967. History of the Lenz-Ising Model. Reviews of Modern Physics (American Physical Society) 39: 883–893. doi:10.1103/RevModPhys.39.883.
  • Campbell, J.Y. 1994. Inspecting the Mechanism: An Analytical Approach to the Stochastic Growth Model. Journal of Monetary Economics 33: 463–506. doi:10.1016/0304-3932(94)90040-X.
  • Canova, F. 1995. Sensitivity Analysis and Model Evaluation in Simulated Dynamic General Equilibrium Economies. International Economic Review 36, no. 2: 477–501. doi:10.2307/2527207.
  • Canova, F., and L. Sala. 2009. Back to Square One: Identification Issues in DSGE Models. Journal of Monetary Economics 56, no. 4: 431–449. doi:10.1016/j.jmoneco.2009.03.014.
  • Canova, F., F. Ferroni, and C. Matthes. 2014. Choosing the Variables to Estimate Singular DSGE Models. Journal of Applied Econometrics 29, no. 7: 1099–1117. November. doi:10.1002/jae.2414.
  • Casella, G., and E.I. George. 1992. Explaining the Gibbs Sampler. The American Statistician 46: 167–174.
  • Chib, S., and E. Greenberg. 1995. Understanding the Metroplois-Hastings algorithm. American Statistician 49: 327–35.
  • Christiano, L.J., and M. Eichenbaum. 1992. Current Real Business Cycle Theories and Aggregate Labour Market Fluctuations. American Economic Review 82: 430–450.
  • Christiano, L.J., R. Motto, and M. Rostagno. 2008. Shocks, Structures or Monetary Policies? The Euro Area and US after 2001. Journal of Economic Dynamics and Control 32, no. 8: 2476–2506. doi:10.1016/j.jedc.2007.09.014.
  • Colander, D. 2000. New Millennium Economics: How Did It Get This Way and What Way Is It? Journal of Economic Perspectives 14, no. 1: 121–132. Winter. doi:10.1257/jep.14.1.121.
  • Cowles, M.K., and B.P. Carlin. 1996. Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review. Journal of the American Statistical Association 91: 883–904. doi:10.1080/01621459.1996.10476956.
  • Cox, D.R., and H.D. Miller. 2001. The Theory of Stochastic Processes. London: Chapman & Hall.
  • David, C., eds. 2006. Post Walrasian Macroeconomics: Beyond the Dynamic Stochastic General Equilibrium Model. Cambridge: Cambridge University Press.
  • DeJong, D.N., B.F. Ingram, and C.H. Whiteman. 1996. A Bayesian Approach to Calibration. Journal of Business & Economic Statistics 14, no. 1: 1–9.
  • DeJong, D.N., B.F. Ingram, and C.H. Whiteman. 2000. A Bayesian Approach to Dynamic Macroeconomics. Journal of Econometrics 98: 203–223. doi:10.1016/S0304-4076(00)00019-1.
  • Del Negro, M., and F. Schorfheide. 2004. Priors from General Equilibrium Models for VARs. International Economic Review 45, no. 2: 643–673. doi:10.1111/iere.2004.45.issue-2.
  • Del Negro, M., and F. Schorfheide. 2006. How Good Is What You Have Got? DSGE-VAR as a Toolkit for Evaluating DSGE Models. FRB of Atlanta: 21–37. Second Quarter.
  • Del Negro, M., F. Schorfheide, F. Smets, and R. Wouters. 2007. On the Fit of New Keynesian Models. Journal of Business & Economic Statistics 25, no. 2: 123–143. doi:10.1198/073500107000000016.
  • Doob, J.L. 1953. Stochastic Processes. New York, NY: Wiley & Sons.
  • Duffie, D., and K.J. Singleton. 1993. Simulated Moments Estimation of Markov Models of Asset Prices. Econometrica 61: 929–952. doi:10.2307/2951768.
  • El Adlouni, S., A. Favre, and B. Bobee. 2006. Comparison of methodologies to assess the convergence of Markov Chain Monte Carlo methods. Computational Statistics and Data Analysis 50, no. 1: 2685–701.
  • Epstein, J.M., and R.L. Axtell. 1996. Growing Artificial Societies: Social Science from the Bottom Up. Cambridge, MA: MIT Press.
  • Farmer, D., L. Gillemot, G. Iori, S. Krishnamurthy, E. Smith, and M. Daniels. 1988. A Random Order Placement Model of Price Formation in the Continuous Double Auction. In The Economy as an Evolving Complex System III, ed. L. Blume and S. Durlauf, Oxford: Oxford University Press.
  • Fernandez-Villaverde, J., J. Rubio-Ramirez, T.J. Sargent, and M.W. Watson. 2007. ABCs (and Ds) of understanding VARs. American Economic Review 97: 1021–26.
  • Franchi, M., and K. Jusélius. 2007. Taking a DSGE Model to the Data Meaningfully. Kiel Institute for the World Economy (IfW) Economics Discussion Papers No. 2007-6.
  • Galí, J., F. Smets, and R. Wouters. 2012. Unemployment in an estimated new Keynesian model. NBER Macroeconomics Annual 26, no. 1: 329–60. doi:10.1086/663994.
  • Gamerman, D. 1997. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. London: Chapman & Hall.
  • Gelfand, A.E., and A.F.M. Smith. 1990. Sampling-based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association 85: 398–409. doi:10.1080/01621459.1990.10476213.
  • Gelman, A. 1996. Inference and Monitoring Convergence. In Markov Chain Monte Carlo in Practice, ed. W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, 131–143. London: Chapman & Hall.
  • Gelman, A., and D.B. Rubin. 1992. Inference from Iterative Simulation Using Multiple Sequences (with Discussion). Statistical Science 7: 457–472. doi:10.1214/ss/1177011136.
  • Geman, S., and D. Geman. 1984. Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 721–741. doi:10.1109/TPAMI.1984.4767596.
  • Geweke, J. 1989. Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica 57: 1317–1339. doi:10.2307/1913710.
  • Geweke, J. 1992. Evaluating the Accuracy of Sampling Based Approaches to the Calculation of Posterior Moments. In Bayesian Statistics 4, ed. J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, 169–193. Oxford: Oxford University Press.
  • Geweke, J. 1997. Posterior Simulators in Econometrics. In Advances in Economics and Econometrics: Theory and Applications, ed. D. Kreps and K.F. Wallis, 128–165. vol. 3. Cambridge: Cambridge University Press.
  • Geweke, J. 1998. Using Simulation Methods in Bayesian Econometric Models: Inference, Development and Communication. Research Department Staff Report No. 249, FRB of Minneapolis.
  • Geyer, C.J. 1992. Practical Markov Chain Monte Carlo (with Discussion). Statistical Science 7: 473–483. doi:10.1214/ss/1177011137.
  • Gregory, A., and G. Smith. 1991. Calibration as Testing: Inference in Simulated Macroeconomic Models. Journal of Business & Economic Statistics 9: 297–304.
  • Hamilton, J.D. 1994. Time Series Analysis. Princeton, NJ: Princeton University Press.
  • Hammersley, J.M., and D.C. Handscomb. 1964. Monte Carlo Methods. London: Mithuen.
  • Hansen, G.D. 1985. Indivisible Labor and the Business Cycle. Journal of Monetary Economics 16: 309–327. doi:10.1016/0304-3932(85)90039-X.
  • Hansen, L.P. 1982. Large Sample Properties of Generalized Method of Moments Estimators. Econometrica 50: 1029–1954. doi:10.2307/1912775.
  • Harrison, R., K. Nikolov, M. Quinn, G. Ramsey, A. Scott, and R. Thomas. 2005. The Bank of England Quarterly Model. London: Bank of England.
  • Hastings, W.K. 1970. Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika 57: 97–109. doi:10.1093/biomet/57.1.97.
  • Heidelberger, P., and P.D. Welch. 1983. Simulation Run Length Control in the Presence of an Initial Transient. Operations Research 31: 1109–1144. doi:10.1287/opre.31.6.1109.
  • Heyde, C.C., and I.M. Johnstone. 1979. On Asymptotic Posterior Normality for Stochastic Processes. Journal of the Royal Statistical Society, Ser. B. 41, no. 2: 184–189.
  • Hoover, K., S. Johansen, and K. Juselius. 2008. Allowing the Data to Speak Freely: The Macroeconometrics of the Cointegrated Vector Autoregression. American Economic Review 98: 251–255. doi:10.1257/aer.98.2.251.
  • Ireland, P.N. 2004. A Method for Taking Models to the Data. Journal of Economic Dynamics & Control 28: 1205–1226. doi:10.1016/S0165-1889(03)00080-0.
  • Johansen, S. 1996. Likelihood-based Inference in Cointegrated Vector Autoregressive Models. Oxford: Oxford University Press.
  • Jorgensen, B. 1997. The Theory of Dispersion Models. London: Chapman and Hall.
  • Juselius, K. 2006. The Cointegrated VAR Model: Econometric Methodology and Empirical Applications. Oxford: Oxford University Press.
  • Juselius, K., and M. Franchi. 2007. Taking a DSGE Model to the Data Meaningfully. Economics-The Open Access, Open-Assessment E-Journal 2007-4: 2–38.
  • Komunjer, I., and S. Ng. 2011. Dynamic Identification of DSGE Models. Econometrica 79: 1995–2032. doi:10.3982/ECTA8916.
  • Kwan, Y.K. 1991. Bayesian Calibration with an Application to a Nonlinear Rational Expectations Two Country Model. Chicago, IL: University of Chicago Business School (Manuscript).
  • Kydland, F., and E. Prescott. 1982. Time to Build and Aggregate Fluctuations. Econometrica 50: 1345–1370. doi:10.2307/1913386.
  • Lambert, D., and K. Roeder. 1995. Overdispersion Diagnostics for Generalized Linear Models. Journal of the American Statistical Association 90: 1225–1236. doi:10.1080/01621459.1995.10476627.
  • Lange, K. 1999. Numerical Analysis for Statisticians. Heidelberg: Springer.
  • LeBaron, B., and L. Tesfatsion. 2008. Modeling Macroeconomies as Open-Ended Dynamic Systems of Interacting Agents. American Economic Review: Papers & Proceedings 98, no. 2: 246–250. doi:10.1257/aer.98.2.246.
  • Lucas, R.E. 1972. Expectations and the Neutrality of Money. Journal of Economic Theory 4, no. 2: 103–124. doi:10.1016/0022-0531(72)90142-1.
  • Mankiw, N.G. 1989. Real Business Cycles: A New Keynesian Perspective. Journal of Economic Perspectives 3, no. 3: 79–90. doi:10.1257/jep.3.3.79.
  • McGrattan, E., R. Rogerson, and R. Wright. 1997. An Equilibrium Model of the Business Cycle with Household Production and Fiscal Policy. International Economic Review 38, no. 2: 267–290. doi:10.2307/2527375.
  • Mehra, R., and E. Prescott. 1985. The Equity Premium: A Puzzle. Journal of Monetary Economics 15: 145–161. doi:10.1016/0304-3932(85)90061-3.
  • Metropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller. 1953. Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 21: 1087–1091. doi:10.1063/1.1699114.
  • Morris, S. 2014. Identification of dynamic stochastic general equilibrium models. PhD Thesis, University of California, San Diego.
  • Muller, P. 1991. Monte Carlo Integration in General Dynamic Models. Contemporary Mathematics 115: 145–163.
  • Nachane, D.M. 2016a. Dynamic Stochastic General Equilibrium (DSGE) Modelling: Theory and Practice. IGIDR Working Paper No. 2016-004. Mumbai.
  • Nachane, D.M. 2016b. Dynamic Stochastic General Equilibrium Modelling: A Sisyphean Toil? Economic and Political Weekly 51, no. 12: 68–77. 19 March.
  • Newey, W., and K. West. 1987. Hypothesis Testing with Efficient Method of Moments Estimation. International Economic Review 28: 777–787. doi:10.2307/2526578.
  • Niederreiter, H. 1988. Quasi Monte Carlo Methods for Multidimensional Numerical Integration. International Series of Numerical Mathematics 85: 157–171.
  • Norris, J.R. 1997. Markov Chains. Cambridge: Cambridge University Press.
  • Pagan, A. 2005. Addendum to Report on Modelling and Forecasting at the Bank of England. Bank of England Quarterly Bulletin (Summer). 190–193
  • Polson, N. 1992. [Practical Markov Chain Monte Carlo]: Comment. Statistical Science 7: 490–491. doi:10.1214/ss/1177011141.
  • Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling. 1992. Numerical Recipes in Fortran: The Art of Scientific Computing. 2nded. Cambridge: Cambridge University Press.
  • Qu, Z., and D. Tkachenko. 2012. Identification and Frequency Domain Quasi-Maximum Likelihood Estimation of Linearized Dynamic Stochastic General Equilibrium Models. Quantitative Economics 3: 95–132. doi:10.3982/QE126.
  • Priestley, M.B. 1981. Spectral Analysis and Time Series. London: Academic Press.
  • Raftery, A.E., and S. Lewis. 1992. How Many Iterations in the Gibbs Sampler? In Bayesian Statistics 4, ed. J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, 763–773. Oxford: Oxford University Press.
  • Richard, J.-F., and W. Zhang. 2007. Efficient High-Dimensional Importance Sampling. Journal of Econometrics 141: 1385–1411. doi:10.1016/j.jeconom.2007.02.007.
  • Ritter, C., and M.A. Tanner. 1992. Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy Gibbs Sampler. Journal of the American Statistical Association 87: 861–868. doi:10.1080/01621459.1992.10475289.
  • Robert, C.P. 1994. The Bayesian Choice: A Decision Theoretic Motivation. New York: Springer Verlag.
  • Roberts, G.O., and A.F.M. Smith. 1994. Simple Conditions for the Convergence of the Gibbs Sampler and Metropolis-Hastings Algorithms. Stochastic Processes and Their Applications 49: 207–216. doi:10.1016/0304-4149(94)90134-1.
  • Rosenblatt, M. 1971. Markov Processes: Structure and Asymptotic Behaviour. Berlin: Springer.
  • Ruge-Murcia, F. 2007. Methods to Estimate Dynamic Stochastic General Equilibrium Models. Journal of Economic Dynamics and Control 31: 2599–2636. doi:10.1016/j.jedc.2006.09.005.
  • Sahlin, K. 2011. Estimating Convergence of Markov Chain Monte Carlo Simulations. Stockholm University, Mathematisk Statistik, Masteruppsats 2011: 2.
  • Sargent, T.J. 1979. Macroeconomic Theory. New York, NY: Academic Press.
  • Sargent, T.J. 1989. Two Models of Measurements and the Investment Accelerator. Journal of Political Economy 97, no. 2: 251–287. doi:10.1086/261603.
  • Sborodone, A., A. Tambalotti, K. Rao, and K. Walsh. 2010. Policy analysis using DSGE models: An introduction, 23–43. Economic Policy Review. New York: Federal Reserve Bank of New York. (October).
  • Schorfheide, F. 2000. Loss Function Based Evaluation of DSGE Models. Journal of Applied Econometrics 15, no. 6: 645–670. doi:10.1002/(ISSN)1099-1255.
  • Schwarz, G. 1978. Estimating the Dimension of a Model. The Annals of Statistics 6: 461–464. doi:10.1214/aos/1176344136.
  • Sims, C.A. 2002. Solving Linear Rational Expectations Models. Computational Economics 20: 1–20. doi:10.1023/A:1020517101123.
  • Smets, F., and R. Wouters. 2003. An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area. Journal of the European Economic Association 1, no. 5: 1123–1175. doi:10.1162/jeea.2003.1.issue-5.
  • Smets, F., and R. Wouters. 2004. Forecasting with a Bayesian DSGE Model: An Application to the Euro Area. JCMS: Journal of Common Market Studies 42, no. 4: 841–867. doi:10.1111/jcms.2004.42.issue-4.
  • Smith, A.A. 1993. Estimating Nonlinear Time Series Models Using Simulated Vector Autoregressions. Journal of Applied Econometrics 8: S63–84. doi:10.1002/(ISSN)1099-1255.
  • Solow, R. 1997. How Did Economics Get That Way and What Way Did It Get? Daedalus 126, no. 1: 39–58. Winter.
  • Taylor, J.B. 1980. Aggregate Dynamics and Staggered Contracts. Journal of Political Economy 88, no. 1: 1–23. doi:10.1086/260845.
  • Tesfatsion, L., and K.L. Judd, eds. 2006. Handbook of Computational Economics Vol. 2. Amsterdam: Elsevier.
  • Tierney, L. 1994. Markov Chains for Exploring Posterior Distributions (with Discussion). The Annals of Statistics 22: 1701–1728. doi:10.1214/aos/1176325750.
  • Tovar, C.E. 2008. DSGE Models and Central Banks. BIS Working Papers No. 258
  • Uhlig, H. 1999. A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily. In Computational Methods for the Study of Dynamic Economics, ed. R. Marimon and A. Scott, 30–61. London: Oxford University Press.
  • Watson, M. 1993. Measures of Fit for Calibrated Models. Journal of Political Economy 101: 1011–1041. doi:10.1086/261913.
  • Weinberg, G., and R. Kyprianou. 2005. Approximation of integrals via Monte Carlo methods with an application to calculating radar detection probabilities. Canberra: Department of Defence, Australian Government. DSTO-TR-1692.
  • Whittle, P. 1962. Gaussian Estimation in Stationary Time Series. Bulletin of the Institute of International Statistics 39: 105–129.
  • Woodford, M. 1986. Stationary sunspot equilibria: The case of small fluctuations around a deterministic steady state. Chicago, IL: University of Chicago (Manuscript).

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