Recent studies apply the Monte Carlo method to try to solve multiple data problems for dynamic macroeconomic models such as measurement errors, residue correlation, and omitted variables. This paper evaluates the estimate of economic growth regressions from the Solow model by applying the Next Reaction Method, similar to the Monte Carlo kinetic methods. Our results indicate that with the said algorithm the estimation of these models improves since they increase the levels of precision of the existing models simulated with Monte Carlo, achieving faster the convergence of the coefficients of the variables reduces the possible measurement errors and the level of deviations. These results can be very useful in their application in dynamic macroeconomic models, which help the estimation challenges of policymakers and other related stakeholders.

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动态宏观经济模型的下一反应方法：增长回归模拟

最近的研究应用蒙特卡洛方法尝试解决动态宏观经济模型的多个数据问题，例如测量误差，残差相关性和遗漏变量。本文通过应用类似于蒙特卡洛动力学方法的下一步反应方法，评估了Solow模型对经济增长回归的估计。我们的结果表明，使用上述算法，由于这些模型提高了用蒙特卡洛模拟的现有模型的精度水平，因此对这些模型的估计有所改善，更快地实现了变量系数的收敛，从而减少了可能的测量误差和偏差水平。这些结果在动态宏观经济模型中的应用中可能非常有用，这有助于决策者和其他相关利益相关者的估计挑战。