This paper evaluates the effectiveness of simulated annealing as an optimization algorithm for statistical functions, comparing it to conventional algorithms on four econometric problems. Simulated annealing is shown to find the global optimum more reliably and robustly than conventional algorithms, which can sometimes fail or converge to local optima. The study uses four different models: a nonlinear least squares problem with multiple minima, a rational expectations exchange rate model, a translog cost frontier model, and a neural network fitting a chaotic time series. Despite requiring more computational resources, simulated annealing demonstrates superior performance in finding the global optimum, especially in more complex and challenging functions. The paper also introduces extensions to simulated annealing to improve its robustness and efficiency, further enhancing its potential for practical applications in econometrics.This paper evaluates the effectiveness of simulated annealing as an optimization algorithm for statistical functions, comparing it to conventional algorithms on four econometric problems. Simulated annealing is shown to find the global optimum more reliably and robustly than conventional algorithms, which can sometimes fail or converge to local optima. The study uses four different models: a nonlinear least squares problem with multiple minima, a rational expectations exchange rate model, a translog cost frontier model, and a neural network fitting a chaotic time series. Despite requiring more computational resources, simulated annealing demonstrates superior performance in finding the global optimum, especially in more complex and challenging functions. The paper also introduces extensions to simulated annealing to improve its robustness and efficiency, further enhancing its potential for practical applications in econometrics.