This paper compares simulated annealing to conventional optimization algorithms on four econometric problems. Simulated annealing is shown to find the global optimum more reliably than conventional algorithms, which often get stuck in local optima. The study tests simulated annealing on four models: a nonlinear least squares model with multiple minima, a rational expectations exchange rate model, a translog cost frontier model, and a neural network fitted to a chaotic time series. Conventional algorithms like the simplex, conjugate gradient, and quasi-Newton methods often fail or converge to local optima, while simulated annealing consistently finds the global optimum. Simulated annealing is also more robust, handling functions with ridges, plateaus, and discontinuities better than conventional methods. The paper introduces extensions to the Corana algorithm, allowing for better control over the optimization process. Simulated annealing requires more computational time than conventional algorithms but is competitive when compared to multiple runs of conventional algorithms. The study concludes that simulated annealing is a powerful and reliable method for global optimization, especially for difficult functions. The paper also notes that computational resources are becoming more available, making simulated annealing increasingly practical for complex problems.This paper compares simulated annealing to conventional optimization algorithms on four econometric problems. Simulated annealing is shown to find the global optimum more reliably than conventional algorithms, which often get stuck in local optima. The study tests simulated annealing on four models: a nonlinear least squares model with multiple minima, a rational expectations exchange rate model, a translog cost frontier model, and a neural network fitted to a chaotic time series. Conventional algorithms like the simplex, conjugate gradient, and quasi-Newton methods often fail or converge to local optima, while simulated annealing consistently finds the global optimum. Simulated annealing is also more robust, handling functions with ridges, plateaus, and discontinuities better than conventional methods. The paper introduces extensions to the Corana algorithm, allowing for better control over the optimization process. Simulated annealing requires more computational time than conventional algorithms but is competitive when compared to multiple runs of conventional algorithms. The study concludes that simulated annealing is a powerful and reliable method for global optimization, especially for difficult functions. The paper also notes that computational resources are becoming more available, making simulated annealing increasingly practical for complex problems.