Next Reaction Method for Solving Dynamic Macroeconomic Models: A Growth Regressions Simulation
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.
Growth regressions, Macroeconomic models, Monte Carlo algorithms, Next Reaction Method, Solow model
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