Questions from Econometrics

Q: Consider estimation of a Poisson regression model for yi | xi.

Consider estimation of a Poisson regression model for yi | xi. The data are truncated on the left—these are on-site observations at a recreation site, so zeros do not appear in the data set. The data...

Q: The following 20 observations are drawn from a censored normal distribution:

The following 20 observations are drawn from a censored normal distribution: The applicable model is Exercises 1 through 4 in this section are based on the preceding information. The OLs estimato...

Q: We now consider the tobit model that applies to the full data

We now consider the tobit model that applies to the full data set. a. Formulate the log likelihood for this very simple tobit model. b. Reformulate the log likelihood in terms of Then derive the neces...

Q: This application is based on the following data set.

This application is based on the following data set. a. compute the OLS regression of y on a constant, x1, and x2. Be sure to compute the conventional estimator of the asymptotic covariance matrix o...

Q: Using only the no limit observations, repeat Exercise 2 in the

Using only the no limit observations, repeat Exercise 2 in the context of the truncated regression model. Estimate / by using the method of moments estimator outlined in Example 19.2. Compare your r...

Q: Continuing to use the data in Exercise 1, consider once again

Continuing to use the data in Exercise 1, consider once again only the nonzero observations. Suppose that the sampling mechanism is as follows: y* and another normally distributed random variable z ha...

Q: Derive the partial effects for the tobit model with heteroscedasticity that is

Derive the partial effects for the tobit model with heteroscedasticity that is described in section 19.3.5.b.

Q: Prove that the Hessian for the tobit model in (19-

Prove that the Hessian for the tobit model in (19-14) is negative definite after Olsen’s transformation is applied to the parameters.

Q: Does first differencing reduce autocorrelation? Consider the models /

Does first differencing reduce autocorrelation? Consider the models where / Compare the autocorrelation of t in the original model with that of vt in / where

Q: Derive the disturbance covariance matrix for the model / What

Derive the disturbance covariance matrix for the model What parameter is estimated by the regression of the OLS residuals on their lagged values?