This chapter describes a method for constructing confidence intervals for general equilibrium model results using the covariance matrices routinely computed in the course of estimating the model's parameters. The method is then applied to IGEM to produce confidence intervals for several types of results: (i) the levels of variables in IGEM's base case, (ii) deviations in variables resulting from a policy experiment consisting of a carbon tax used to reduce the rate of tax on capital income, and (iii) the effects of the carbon tax swap on the components of social welfare. For each case, a decomposition analysis is used to identify the key uncertainties underlying the confidence interval. The carbon tax swap is shown to produce statistically significant gains in key variables, including social welfare, along with significant reductions in pollution. The method is also shown to have important advantages over alternative approaches. The confidence intervals it produces are very similar to those generated by Monte Carlo simulation but are much faster to compute for large models. In addition, the decomposition analysis shows that off-diagonal terms in the parameter covariance matrices often contribute significantly to the confidence intervals for key results. By capturing these effects, the method provides better characterization of uncertainty than alternative methodologies that rely on independent perturbations of parameters.
Keywords: confidence intervals, delta method, parameter estimates, covariance matrix, statistical significance, carbon taxes, revenue recycling, capital taxes, macroeconomic performance, Monte Carlo analysis, systematic sensitivity analysis, equivalent variation, efficiency, equity, social welfare, decomposition of uncertainty
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