Besides maximum likelihood estimation of the logit and probit model, your textbook mentions that the model can also be estimated by nonlinear least squares. Construct the sum of squared prediction mistakes and suggest how computer algorithms go about finding the coefficient values that minimize the function. You may want to use an analogy where you place yourself into a mountain range at night with a flashlight shining at your feet. Your task is to find the lowest point in the valley. You have two choices to make: the direction you are walking in and the step length. Describe how you will proceed to find the bottom of the valley. Once you find the lowest point, is there any guarantee that this is the lowest point of all valleys? What should you do to assure this?

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The Answer of Besides maximum likelihood estimation of the logit and probit model, your textbook mentions that the model can also be estimated by nonlinear least squares. Construct the sum of squared prediction mistakes and suggest how computer algorithms go about finding the coefficient values that minimize the function. You may want to use an analogy where you place yourself into a mountain range at night with a flashlight shining at your feet. Your task is to find the lowest point in the valley. You have two choices to make: the direction you are walking in and the step length. Describe how you will proceed to find the bottom of the valley. Once you find the lowest point, is there any guarantee that this is the lowest point of all valleys? What should you do to assure this?

Besides Maximum Likelihood Estimation of the Logit and Probit Model