Book contents
- Frontmatter
- Contents
- List of contributors
- Acknowledgments
- Introduction
- Part I The SEMTSA approach
- Part II Selected applications
- Part III Macroeconomic forecasting and modeling
- Part IV Disaggregation, forecasting, and modeling
- 20 Using Bayesian techniques for data pooling in regional payroll forecasting (1990)
- 21 Forecasting turning points in metropolitan employment growth rates using Bayesian techniques (1990)
- 22 A note on aggregation, disaggregation, and forecasting performance (2000)
- 23 The Marshallian macroeconomic model (2000)
- 24 Bayesian modeling of economies and data requirements (2000)
- Subject index
- Author index
- References
24 - Bayesian modeling of economies and data requirements (2000)
Published online by Cambridge University Press: 24 October 2009
Book contents
- Frontmatter
- Contents
- List of contributors
- Acknowledgments
- Introduction
- Part I The SEMTSA approach
- Part II Selected applications
- Part III Macroeconomic forecasting and modeling
- Part IV Disaggregation, forecasting, and modeling
- 20 Using Bayesian techniques for data pooling in regional payroll forecasting (1990)
- 21 Forecasting turning points in metropolitan employment growth rates using Bayesian techniques (1990)
- 22 A note on aggregation, disaggregation, and forecasting performance (2000)
- 23 The Marshallian macroeconomic model (2000)
- 24 Bayesian modeling of economies and data requirements (2000)
- Subject index
- Author index
- References
Summary
Introduction
For many years, theoretical and empirical workers have tried to model national economies in order to (1) understand how they operate, (2) forecast future outcomes, and (3) evaluate alternative economic policies. While much progress has been made in the decades since Tinbergen's pioneering work [1940], it is the case that no generally accepted model has as yet appeared. On the theoretical side, there are monetary, neo-monetary, Keynesian, neo-Keynesian, real business cycle, generalized real business cycle, and other theoretical models (see, Belongia and Garfinkel 1992 for an excellent review of many of these models and Min 1992 for a description of a generalized real business cycle model). Some empirical testing of alternative models has appeared in the literature. However, in Fair (1992) and Zellner (1992) (invited contributions to a St. Louis Federal Reserve Bank conference on alternative macroeconomic models), it was concluded that there is a great need for additional empirical testing of alternative macroeconomic models and production of improved models.
Over the years many structural econometric and empirical statistical models have been constructed and used. These include large structural econometric models (e.g. the Tinbergen, Klein, Brookings–SSRC, Federal Reserve–MIT–PENN, OECD, Project Link, and other models). While progress has been made, there does not yet appear to be a structural model that performs satisfactorily in point and turning point forecasting.
- Type
- Chapter
- Information
- The Structural Econometric Time Series Analysis Approach , pp. 677 - 706Publisher: Cambridge University PressPrint publication year: 2004
References
and (1959), “The dynamic properties of the Klein–Goldberger model,” Econometrica 27, 569–625CrossRefGoogle Scholar
Belongia, M. and M. Garfinkel (eds.) (1992), “The business cycle: theories and evidence,” Proceedings of the 16th Annual Economic Policy Conference of the Federal Reserve Bank of St. Louis (Boston, Kluwer Academic),
Cooper, R. (1972), “The predictive performance of quarterly econometric models of the United States,” in B. Hickman (ed.), Econometric Models of Cyclical Behavior 2 (New York, Columbia University Press), 813–936
Currie, J. (1996), “The geographic extent of the market: theory and application to the US petroleum markets,” PhD thesis, Department of Economics, University of Chicago
de Alba, E. and A. Zellner (1991), “Aggregation, disaggregation, predictive precision and modeling,” H. G. B. Alexander Research Foundation, Graduate School of Business, University of Chicago, manuscript
Espasa, A. and L. Matea (1990), “Underlying inflation in the Spanish economy: estimation and methodology,” Working Paper, Bank of Spain
Fair, R. (1992), “How might the debate be resolved?,” in M. Belongia and M. Garfinkel (eds.), “The business cycle: theories and evidence,” Proceedings of the 16th Annual Economic Policy Conference of the Federal Reserve Bank of St. Louis (Boston, Kluwer),
Gao, C. and K. Lahiri (1999), “A comparison of some recent Bayesian and non-Bayesian procedures for limited information simultaneous equations models,” Paper presented at the American Statistical Association's meeting, Baltimore, MD, August
, , and (1987), “Macroeconomic forecasting using pooled international data,” Journal of Business and Economic Statistics 5(1), 53–67; chapter 13 in this volumeGoogle Scholar
Hong, C. (1989), “Forecasting real output growth rates and cyclical properties of models: a Bayesian approach,” PhD thesis, Department of Economics, University of Chicago
Judge, G., W. Griffiths, R. Hill, H. Lütkepohl, and T. Lee (1987), The Theory and Practice of Econometrics (New York, John Wiley)
(1987), “Predicting the turning points of business and economic time series,” Journal of Business 60, 201–38CrossRefGoogle Scholar
(1986), “Forecasting with Bayesian vector autoregressions: five years of experience,” Journal of Business and Economic Statistics 4, 25–38Google Scholar
(1986), “Forecasting accuracy of alternative techniques: a comparison of US macroeconomic forecasts,” Journal of Business and Economic Statistics 4, 5–23Google Scholar
Min, C. (1992), “Economic analysis and forecasting of international growth rates using Bayesian techniques,” PhD thesis, Department of Economics, University of Chicago
and (1993), “Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates,” Journal of Econometrics, Annals 56, 89–118; chapter 17 in this volumeCrossRefGoogle Scholar
and (1982), “Trends and random walks in macroeconomic time series,” Journal of Monetary Economics 10, 139–62CrossRefGoogle Scholar
Orcutt, G., M. Greenberger, J. Korbel, and A. Rivlin (1961), Microanalysis of Socioeconomic Systems (New York, Harper)
Pagan, A. (1979), “Some consequences of viewing LIML as an iterated Aitken estimator,” Working Paper 18, Australian National University Faculty of Economics and Research School of Social Sciences
(1976), “Testing the dynamic specification of an econometric model with an application to Belgian data,” European Economic Review 8, 269–89CrossRefGoogle Scholar
(1977), “On univariate time series methods and simultaneous equation econometric models,” Journal of Econometrics 5, 379–88CrossRefGoogle Scholar
Palm, F. C. (1983), “Structural econometric modeling and time series analysis: an integrated approach,” in A. Zellner (ed.), Applied Time Series Analysis of Economic Data (Washington, DC, US Department of Commerce, Bureau of the Census, 199–233; chapter 3 in this volume
(1982), “Some sampling properties of minimum expected loss (MELO) estimators of structural coefficients,” Journal of Econometrics 18, 295–311CrossRefGoogle Scholar
Tsurumi, H. (1990), “Comparing Bayesian and non-Bayesian limited information estimators,” in S. Geisser, J. S. Hodges, J. Press, and A. Zellner (eds.), Bayesian and Likelihood Methods in Statistics and Econometrics: Essays in Honor of George A. Barnard (Amsterdam, North-Holland), 179–202
and (1985), “Entry and empirical demand and supply analysis for competitive industries,” Journal of Econometrics 30, 459–71CrossRefGoogle Scholar
(1979), “Predicting the turning points of a time series,” Journal of Business 52, 35–50CrossRefGoogle Scholar
(1986), “The record and improvability of economic forecasting,” Economic Forecasts 3, 22–31Google Scholar
(1979), “Statistical analysis of econometric models,” invited paper, with discussion, Journal of the American Statistical Association 74, 628–51; chapter 2 in this volumeCrossRefGoogle Scholar
Zellner, A. (1986), “Further results on Bayesian minimum expected loss (MELO) estimates and posterior distributions for structural coefficients,” in D. Slottje (ed.), Advances in Econometrics 5, 171–82
Zellner, A. (1992), “Comment on Ray C. Fair's thoughts on ‘How might the debate be resolved?’,” in M. Belongia and M. Garfinkel (eds.), “The business cycle: theories and evidence,” Proceedings of the 16th Annual Economic Policy Conference of the Federal Bank of St. Louis (Boston, Kluwer), 148–57
(1994), “Time series analysis, forecasting, and econometric modeling: the structural econometric modeling, time series analysis (SEMTSA) approach,” Journal of Forecasting 13, 215–33, invited paper with discussion; chapter 4 in this volumeCrossRefGoogle Scholar
Zellner, A. (1997a), “The Bayesian method of moments (BMOM): theory and applications,” Advances in Econometrics 12, T. Fomby and R. Hill (eds.), 85–105
Zellner, A. (1997b), Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers (Cheltenham, Edward Elgar)
Zellner, A. (1998), “The finite sample properties of simultaneous equations' estimates and estimators: Bayesian and non-Bayesian approaches,” Annals Issue of Journal of Econometrics 83, L. Klein (ed.), 185–212CrossRef
Zellner, A. (1999), “Bayesian and non-Bayesian approaches to scientific modeling and inference in economics and econometrics,” invited keynote paper presented at the Ajou University Research Conference in honor of Professor Tong Hun Lee, South Korea, August; [published in Special Issue of the Korean Journal of Money and Finance 5(2) (November 2000)]
and (1989), “Forecasting international growth rates using Bayesian shrinkage and other procedure,” Journal of Econometrics, Annals 40, 183–202; chapter 14 in this volumeCrossRefGoogle Scholar
and (1999), “Forecasting turning points in countries' growth rates: a response to Milton Friedman,” Journal of Econometrics 88, 203–6; chapter 19 in this volumeCrossRefGoogle Scholar
Zellner, A., C. Min, D. Dallaire, and J. Currie (1994), “Bayesian analysis of simultaneous equation, asset pricing and related models using Markov chain Monte Carlo techniques,” H. G. B. Alexander Research Foundation, Graduate School of Business, University of Chicago, manuscript
and (1974), “Time series analysis and simultaneous equation econometric models,” Journal of Econometrics 2, 17–54; chapter 1 in this volumeCrossRefGoogle Scholar
and (1975), “Time series and structural analysis of monetary models of the US economy,” Sankyā: The Indian Journal of Statistics, Series C 37, 12–56; chapter 6 in this volumeGoogle Scholar
Zellner, A. and S. Peck (1973), “Simulation experiments with a quarterly macroeconometric model of the US economy,” in A. Powell and R. Williams (eds.), Econometric Studies of Macro and Monetary Relations (Amsterdam, North-Holland), 149–68
Zellner, A., J. Tobias, and H. Ryu (1999), “Bayesian method of moments analysis of time series models with an application to forecasting turning points in output growth rates”; [published in Estadistica 49–51 (152–157), 3–63, with discussion]
- 1
- Cited by