Bongers, Anelí and Molinari, Benedetto and Torres, José L. (2022): Computers, Programming and Dynamic General Equilibrium Macroeconomic Modeling.
Preview |
PDF
MPRA_paper_112505.pdf Download (278kB) | Preview |
Abstract
Dynamic stochastic general equilibrium (DSGE) models nowadays undertake the bulk of macroeconomic analysis. Their widespread use during the last 40 years reflects their usefulness as a scientific laboratory in which to study the aggregate economy and its responses to different shocks, to carry out counterfactual experiments and to perform policy evaluation. A key characteristic of DSGE models is that their computation is numerical and requires intensive computational power and the handling of numerical methods. In fact, the main advances in macroeconomic modeling since the 1980s have been possible only because of the increasing computational power of computers, which has supported the expansion of DSGE models as more and more accurate reproductions of the actual economy, thus becoming the prevailing modeling strategy and the dominant paradigm in contemporaneous macroeconomics. Along with DSGE models, specific computer languages have been developed to facilitate simulations, estimations and comparisons of the aggregate economies represented by DSGE models. Knowledge of these languages, together with expertise in programming and computers, has become an essential part of the profession for macroeconomists at both the academic and the professional level.
Item Type: | MPRA Paper |
---|---|
Original Title: | Computers, Programming and Dynamic General Equilibrium Macroeconomic Modeling |
English Title: | Computers, Programming and Dynamic General Equilibrium Macroeconomic Modeling |
Language: | English |
Keywords: | Dynamic stochastic general equilibrium models; Computers; Programming languages; Codes; Computational economics; Dynare. |
Subjects: | C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C88 - Other Computer Software E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications |
Item ID: | 112505 |
Depositing User: | Dr. Jose L. Torres |
Date Deposited: | 22 Mar 2022 15:03 |
Last Modified: | 22 Mar 2022 15:03 |
References: | Adelman, I. and Adelman, F. L. (1959). The Dynamic Properties of the Klein–Goldberger Model. Econometrica, 27(4): 596–625. Aldrich, E. M., J. Fernández-Villaverde, R. A. Gallant and Rubio-Ramírez, J. F. (2011). Tapping the Supercomputer under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors. Journal of Economic Dynamics and Control, 35(3): 386–393. Altug, S. (1989). Time-to-Build and Aggregate Fluctuations: Some New Evidence. International Economic Review, 30: 889–920. Auroba, S. B. and Fernández-Villaverde, J. (2015). A Comparison of Programming Languages in Economics. Journal of Economic Dynamics and Control, 58: 265–273. Bachmann, S. and Strulik, H. (1992). SoWhat 1.6. A Tool for Easy Simulation of Dynamical System. Manuscript. Bencivenga, V. R. (1992). An Econometric Study of Hours and Output Variation with Preference Shocks. International Economic Review, 33: 449–471. Blake, A. P. (2012). DSGE Modeling on an iPhone/iPad Using SpaceTime. Computational Economics, 40: 313–332. Blanchard, O. J. and Kahn, C. H. (1980). The Solution of Linear Difference Models under Rational Expectations. Econometrica, 48: 1305–1311. Blanchard, O. J. and Kiyotaki, N. (1987). Monopolistic Competition and the Effects of Aggregate Demand. American Economic Review, 77(4): 647–666. Bollard, A. E. (2011). Man, Money and Machines: The Contributions of A. W. Phillips. Economica, 78(309): 1–9. Bongers, A., Gómez, T. and Torres, J. L. (2020). Teaching Dynamic General Equilibrium Models to Undergraduates Using a Spreadsheet. International Review of Economic Education, 35(4): 1–11. Bongers, A., Gómez, T. and Torres, J. L. (2021). Dynamic Macroeconomic Models with Excel. Journal of Economic Education, 52(4): 372-372. Brainard, W. C. and Scarf, H. E. (2005). How To Compute Equilibrium Prices in 1891. American Journal of Economics and Sociology, 61(1): 57–83. Brock, W. and Mirman, L. (1972). Optimal Economic Growth and Uncertainty: The Discounted Case. Journal of Economic Theory, 4(3): 479–513. Caraiani, P. (2019). Introduction to Quantitative Macroeconomics Using Julia. Academic Press, Elsevier. Cass, D. (1965). Optimum Growth in an Aggregative Model of Capital Accumulation. Review of Economic Studies, 32: 233–240. Christiano, L. J. (1988). Why Does Inventory Investment Fluctuate So Much? Journal of Monetary Economics, 21: 247–280. Chu, A. C. (2018). From Solow to Romer: Teaching Endogenous Technological Change in Undergraduate Economics. International Review of Economics Education, 27(c): 10–15. DeJong, D. N., Ingram, B. F., and Whiteman, C. H. (2000). A Bayesian Approach to Dynamic Macroeconomics. Journal of Econometrics, 98(2), 203-223. Dasgupta, P. and Heal, G. (1974). The Optimal Depletion of Exhaustible Resources. Symposium on the Economics of Exhaustible Resources. Review of Economic Studies, 41: 3–28. Enke, S. (1951). Equilibrium among Spatially Separated Markets: Solution by Electric Analogue. Econometrica, 19(1): 40–47. Fernández-Villaverde, J. (2010). The Econometrics of DSGE Models. SERIEs, 1(1–2): 3–49. Forrester, J. W. (1958). Industrial Dynamics—A Major Breakthrough for Decision Makers. Harvard Business Review, 36(4): 37–66. Goodwin, R. M. (1951). The Nonlinear Accelerator and the Persistence of Business Cycles. Econometrica, 19: 1–17. Hansen, G. D. (1985). Indivisible Labor and the Business Cycle. Journal of Monetary Economics, 16: 309–327. Hobbs, P. and Judge, G. (1992). Computers as a Tool for Teaching Economics. Computers and Education, 19 (1-2), 67-72. Ireland, P. N. (1997). A Small, Structural, Quarterly Model for Monetary Policy Evaluation. Carnegie-Rochester Conference Series on Public Policy, 47, 83-108. Ireland, P. N. (2001a). Technology Shocks and the Business Cycle. Journal of Economic Dynamics and Control, 25, 703-719. Ireland, P. N. (2001b). Sticky-price Models of the Business Cycle. Specification and Stability. Journal of Monetary Economics, 47, 3-18. Ireland, P. N. (2004). A Method for Taking Models to the Data. Journal of Economic Dynamics and Control, 28: 1205–1226. Jenkins, B. C. (2022). A Python-Based Undergraduate Course in Computational Macroeconomics. Journal of Economic Education, forthcoming. Judd, K. L. (1997). Computational Economics and Economic Theory: Substitutes or Complements? Journal of Economic Dynamic and Control, 21: 907–922. Kendrick, D. A. and Amman, H. M. (1999). Programming Languages in Economics. Computational Economics, 14: 151–181. Kim, J. (2000). Constructing and Estimating a Realistic Optimizing Model of Monetary Policy. Journal of Monetary Economics, 45(2): 329–359. King, R. G., Plosser, C. I. and Rebelo, S. T. (1987). Production, Growth, and Business Cycles: Technical Appendix. Manuscript. University of Rochester. Klein, P. (2000). Using the Generalized Schur Form to Solve a Multivariate Linear Rational Expectations Model. Journal of Economic Dynamic and Control, 24: 405–423. Kocherlakota, N. R. (2009). Modern Macroeconomic Models as Tools for Economy Policy. The Region: 5–21. Koopmans, T. C. (1965). On the Concept of Optimal Growth, The Econometric Approach to Development Planning. Rand McNally. Kowal, P. (2005). GEMLLIB-Matlab Code for Specifying and Solving DSGE Models. Computer Programs 0504007. University Library of Munich. Kydland, F. and Prescott, E. (1982a). Time To Build and Aggregate Fluctuations. Econometrica, 50: 1350–1372. Kydland, F. and Prescott, E. (1982b). Executable Program for Time To Build and Aggregate Fluctuations. QM&RBC Codes 4, Quantitative Macroeconomics & Real Business Cycles. Long, J. B. and Plosser, C. I. (1983). Real Business Cycles. Journal of Political Economy, 91(1): 39–69. Lucas, R. E. (1976). Econometric Policy Evaluation: A Critique. In K. Brunner and A. Meltzer (eds), The Phillips Curve and Labor Markets. Carnegie-Rochester Conference Series on Public Policy 1, New York. Lucas, R. E. (1980). Methods and Problem in Business Cycle Theory. Journal of Money, Credit, and Banking, 12(4): 696–715. Mankiw, N. G. (1985). Small Menu Costs and Large Business Cycles: A Macroeconomic Model of Monopoly. Quarterly Journal of Economics, 100: 529–539. McGrattan, E. (1994). The Macroeconomic Effects of Distortionary Taxation. Journal of Monetary Economics, 33(3): 573–601. McGrattan, E., Rogerson, R. and Wright, R. (1997). An Equilibrium Model of the Business Cycle with Household Production and Fiscal Policy. International Economic Review, 38(2): 267–290. Morehouse, N. F., Strotz, R. H. and Horwitz, S. J. (1950). An Electro-analog Method for Investigating Problems in Economic Dynamics: Inventory Oscillations. Econometrica, 18(4): 313–328. Nerlove, M. (2004). Programming Languages: A Short History for Economists. Journal of Economic and Social Measurement, 29: 189–203. Neumuller, S., Rothschild, C. and Weerapana, A. (2018). The Macro Pedagogy Debate: Teaching DSGE to Undergraduates Symposium. Journal of Economic Education, 49(3): 242–251. Perla, J., Sargent, T. J. and Stachurski, J. (2022). Quantitative Economics with Julia. Manuscript. Phillips, A. W. H. (1950). Mechanical Models in Economic Dynamics. Economica, 17(67): 283–305. Pierse, R. G. (2000). WinSolve Version 3: An Introductory Guide. Department of Economics, University of Surrey. Rabanal, P. and Rubio-Ramirez, J. F. (2003). Inflation Persistence: How Much Can We Explain? Economic Review, Federal Reserve Bank of Atlanta, Q2: 43–55. Ramsey, F. (1928). A Mathematical Theory of Saving. Economic Journal, 37: 543–559. Raybaut, A. (2020). Analog Computing Simulations and the Production of Theoretical Evidence in Economic Dynamics. Œconomia, 10(2): 309–329. Rotemberg, J. (1982). Monopolistic Price Adjustment and Aggregate Output. Review of Economic Studies, 49(4): 517–531. Sargent, T. J. and Stachurski, J. (2015). Quantitative Economics with Julia. Manuscript. Sergi, F. (2018). DSGE Models and the Lucas Critique. A Historical Appraisal. Working Paper 20181806. Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol. Sims, C. (2001). Solving Linear Rational Expectations Models. Computational Economics, 10: 1–20. Smets, F. and Wouters, R. (2003). An Estimated Dynamic Stochastic General Equilibrium Model for the Euro Area. Journal of the European Economic Association, 1: 1123–1175. Solis-García, M. (2018). The Macro Pedagogy Debate: Teaching DSGE to Undergraduates Symposium: Yes We Can! Teaching DSGE Models to Undergraduate Students. Journal of Economic Education, 49(3): 226–236. Strotz, R. H., Calvert, J. F. and Morehouse, N. F. (1951). Analogue Computing Techniques Applied to Economics. IEEE Xplore, 70: 557–563. Strotz, R. H., McAnulty, J. C. and Naines, J. B. (1953). Goodwin’s Nonlinear Theory of the Business Cycle: An Electro-analog Solution. Econometrica, 21(3): 390–411. Strulik, H. (1992). SoWhat for Windows 1.6. QM&RBC Codes 96, Quantitative Macroeconomics & Real Business Cycles. Svensson, L. (1986). Sticky Goods Prices, Flexible Asset Prices, Monopolistic Competition and Monetary Policy. Review of Economic Studies, 53(3): 385–405. Taylor, J. B. and Uhlig, H. (1990). Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods. Journal of Business and Economic Statistics, 8: 1–17. Uhlig, H. (1999). A Toolkit for Analyzing Nonlinear Dynamic Model Easily. In R. Marimon and A. Scott (eds), Computational Methods for the Study of Dynamic Economies. New York: Oxford University Press. Uzawa, H. (1965). Optimum Technical Change in an Aggregative Model of Economic Growth. International Economic Review, 6: 18–31. Warne, A. (2022). YADA Manual—Computational Details. http://www.texlips.net/yada/ Wieland, V., Afanasyeva, E., Kuete M. and Yoo, J. (2016). New Methods for Macro-Financial Model Comparison and Policy Analysis. Handbook of Macroeconomics, 2: 1241–1319. Wieland, V., Cwik, T., Müller, G. J., Schmidt, S. and Wolters, M. (2012). A New Comparative Approach to Macroeconomic Modeling and Policy Analysis. Journal of Economic Behavior and Organization, 83: 523–541. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/112505 |