Abstract
Good economic theory should hold surprise predictions or outcomes that have not been observed before or not been thought about. This normally means complex models.
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Notes
- 1.
If we are only concerned with the national income or growth effects of manipulating the policy parameters, “opportunity costs ” equal the shadow prices. More complicated objectives, such as adding income distribution consequences or social benefits with no extra value creation for society at large, will not only complicate the political decision. Considering the state of the art of setting up large-scale economic systems models empirically, which we are addressing, complicating the objective function of the policy maker with further detail is likely to reduce the credibility of the analysis.
- 2.
Note however that such computer-based measurement and modeling are practiced in computer-based trading in financial markets, where transactions take place in a millionth of a second.
- 3.
They are not a one to one correspondence mapping (a bijective function). Hence the Micro to Macro model cannot be derived one to one from a CGE model.
- 4.
I won’t say “disequilibrium state.” The disequilibrium conceptualized in neoclassical economics is something very specific, which has no meaning in the type of evolutionary models I am discussing (Eliasson 2014d).
- 5.
If you have a stochastic model, the “empirical” situation is again different. If you start model simulations with identical initial conditions (whether a linear or nonlinear model), you will obtain different simulation outcomes that in the case of nonlinear models may be radically different, for no other reason than a random factor. Stochastic models are thus in general to be avoided and especially so in cost-benefit calculations. (Still the preferred Micro to Macro model that I keep referring to includes some minor stochastic elements (see Eliasson 2014d) that thus make the model open to the same criticism.) So any cost-benefit analysis on that same model has to be controlled for the sensitivity of model system dynamics to these stochastic elements through Monte Carlo experiments.
- 6.
I should add here that the survey of econometric practice conducted in this book, and summarized in sections 13.7 and 13.8, in retrospect, makes me conclude that our primitive calibration at the time, as well as the case aggregation method of estimating spillovers, under the limitations of the Micro to Macro model, do not warrant the cautions with which the results were hedged at the time. Still, however, strong political statements, to be empirically credible have to be based on strict observance of proper statistical protocol.
Bibliography
Carlsson, B. (1983a). Industrial subsidies in Sweden: Macro-economic effects and an international comparison. Journal of Industrial Economics, XXXII(1), 9–14.
Carlsson, B. (1983b). Industrial subsidies in Sweden: Simulations on a micro-to-macro model. In Microeconometrics, IUI yearbook 1982–1983. IUI: Stockholm.
Carlsson, B., Bergholm, F., & Lindberg, T. (1981). Industristödspolitiken och dess inverkan på samhällsekonomin (Industry subsidy policy and its macroeconomic impact). Stockholm: IUI.
Carlsson, B., Eliasson, G., & Sjöö, K. (2014). The Swedish industrial support program of the 1970s revisited – A study in micro to macro analytical method, Paper presented to the 15th International Joseph A. Schumpeter Conference, Jena Germany, July 2014.
Eliasson, G. (1978a). Micro-to-macro model of the Swedish economy. Conference Reports, 1978:1. Stockholm: IUI.
Eliasson, G. (1985). The firm and financial markets in the Swedish micro-to-macro model – Theory, model and verification. Stockholm: IUI.
Eliasson, G. (2010a). Advanced public procurement as industrial policy – Aircraft industry as a technical university. New York/Dordrecht/Heidelberg/London: Springer.
Eliasson, G. (2014b). Generalizing from cases to macro – On model based dynamic aggregation. Revised version of paper presented at the Jena Economic Research Seminar, Jena, May 28. 2014.
Eliasson, G. (2014c). Från Krut, Vapen och Missiler till Civil Industry. Linköping: Saab.
Eliasson, G. (2015a). The incomplete Austrian Swedish school connection. Journal of Evolutionary Economics, 25, 45–56.
Eliasson, G., & Olavi, G. (1978). Stepwise parameter estimation on a micro simulation model. In G. Eliasson (Ed.), A micro-to-macro model of the Swedish economy. IUI Conference Reports, 1978:1. Stockholm: IUI.
Johnston, J. (1963). Econometric methods. New York/San Francisco/Toronto/London: McGraw-Hill.
Stiglitz, J. E. (1980). Some further remarks on cost-benefit analysis. NBER Reprint Nr 78, Cambridge, MA: NBER (Also published in Schwartz & Berney (Eds.), 1977, Social and economic dimensions of project evaluation, Washington DC: Inter-American Development Bank).
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Eliasson, G. (2017). Economy-Wide, Long-Run Model-Based Social Cost-Benefit Calculations. In: Visible Costs and Invisible Benefits. Economics of Science, Technology and Innovation. Springer, Cham. https://doi.org/10.1007/978-3-319-66993-9_14
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