MY-ASSIGNMENTEXPERT™可以为您提供 montana.edu ECNS562 Econometrics计量经济学的代写代考和辅导服务！
ECNS562课程简介
Lectures
Instrumental Variable Estimation
olsIVSimulation.R
Discrete Dependent Variables
optim.R
binary_response.R
discrete_response.R
McFadden, D. (2001) “Economic Choices.” The American Economic Review. 91(3): 351-378.
Train, K.E. (1998) “Recreation Demand Models with Taste Differences over People.” Land Economics. 74(2): 230-239.
Train, K. (2009) Discrete Choice Methods with Simulation (Cambridge University Press) – chapters 2,3
Small, K.A. and H.S. Rosen. 1981. “Applied Welfare Economics with Discrete Choice Models.” Econometrics. 49(1): 105-130.
Prerequisites
Censoring and Truncation
censoring.R
Amemiya, T. (1984) “Tobit Models: A Survey.” Journal of Econometrics. 24: 3-61.
Jones, A.M. (1989) “A Double-Hurdle Model of Cigarette Consumption.” Journal of Applied Econometrics. 4(1): 23-39.
Gurmu, S. and P.K. Trivedi (1996). “Excess Zeros in Count Models for Recreation Trips.” Journal of Business and Economic Statistics. 14(4): 469-477.
Systems of Equations
simultaneous.R
systems.R
Zellner, A. (1962). “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias.” Journal of the American Statistical Association. 57: 742-749.
Belasco, E.J., S.K. Ghosh, and B.K. Goodwin (2009) “A Multivariate Evaluation of Ex ante Risks Associated with Fed Cattle Production.” American Journal of Agricultural Economics. 91(2): 431-443.
ECNS562 Econometrics HELP（EXAM HELP， ONLINE TUTOR）
From Wool C5.3 Suppose you are interested in the effect of parental education on child birthweights.
Your initial model is
bwght = b0 + b1motheduc+u
Suppose that mothereduc was a binary variable that was equal to 1 if a mother had completed high school and 0 if a mother had not completed high school. Interpret the coefficients b0andb1. I’m not asking you to run this regression, just explain how to interpret coefficients on binary variables.
b0 is the mean (average) birthweight for children whose mother’s did not complete high school. b1 is the difference in mean (average) birthweights between children whose mothers’ did and di not complete high school.
Your next model is
$$
\text { bwght }=\beta_0+\beta_1 \text { cigs }+\beta_2 \text { parity }+\beta_3 \text { faminc }+\beta_4 \text { motheduc }+\beta_5 \text { fatheduc }+u
$$
If income is measured in logs, a $1 \%$ increase in income is associated with a $\beta_1$ increase in ounces of birthweight. For example, if $\hat{\beta}_1=.3$, a $1 \%$ increase in income is associated with a .3 ounce increase in birthweight.
Your next model is
$$
b w g h t=\beta_0+\beta_1 \text { cigs }+\beta_2 \text { arity }+\beta_3 \text { faminc }+\beta_4 \text { faminc }^2+\beta_5 \text { motheduc }+\beta_6 \text { fatheduc }+u
$$
How would you explain the effect of family income? I’m not asking you to run this regression, just explain how to interpret coefficients in quadratic models.
To interpret quadratic specifications, take a partial derivative. The effect of family income on birthweight $=\partial$ birthweight $/ \partial$ family income $=\beta_3+\beta_4$ faminc. . You will need to choose a level of family income to report a specific marginal effect. Often this is reported at the mean.
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