MY-ASSIGNMENTEXPERT™可以为您提供 sydney ECON2300 Econometrics计量经济学的代写代考和辅导服务！
这是悉尼大学计量经济学课程的代写成功案例。
ECMT1020课程简介
This unit introduces econometrics: the application of economic theory and statistical methods to quantify economic phenomena and relationships using observational economic data. The unit starts with a review of probability and statistics, followed by an in-depth analysis of classical linear regression. Advanced topics including nonlinear models, dummy variables, model specification, heteroskedasticity, measurement errors, and time series are discussed in the second half of the unit. The aim is to develop a deep understanding of the statistical properties of modern econometrics techniques, and to provide the practical skills, such as using statistical software, to fit models to data.
Prerequisites
At the completion of this unit, you should be able to:
- LO1. understand the process of econometric model building and testing
- LO2. critically evaluate underlying theories, concepts, assumptions and arguments in econometrics and other related fields
- LO3. develop coherent arguments when recommending solutions
- LO4. manage, analyse, evaluate and use information efficiently and effectively
- LO5. demonstrate an increased range of econometric techniques for use in applied work and be able to implement this knowledge for modelling data in different situations
- LO6. demonstrate proficiency in carrying out applied work in econometrics and in the use of an econometric software package
- LO7. confidently and coherently communicate, orally and in writing, to a professional standard
ECMT1020 Econometrics HELP（EXAM HELP， ONLINE TUTOR）
David Neumark and William Wascher published a study in 1992 of the effect of minimum wages on teenage employment using a U.S. state panel. The paper used annual observations for the years 1977-1989 and included all 50 states plus the District of Columbia. The estimated equation is of the following type:
$$
E_{i t}=\beta_0+\beta_1\left(M_{i t} / W_{i t}\right)+\gamma_2 D 2 i+\cdots+\gamma_n D 51_i+\delta_2 B 2_t+\cdots+\delta_{\mathrm{TB}} 13_{\mathrm{t}}+y_{i t}
$$
where $E$ is the employment to population ratio of teenagers, $M$ is the nominal minimum wage, and $W$ is average wage in the state. In addition, other explanatory variables, such as the prime-age male unemployment rate, and the teenage population share were included. D2-D51 are the state dummies, B2-B13 are the time dummies.
(a) Briefly discuss the advantage of using panel data in this situation rather than pure cross-sections or time series.
There are likely to be omitted variables in the above regression. On way to deal with some of these is to introduce state and time dummy variables to capture the fixed component of the error term. State dummy variables will capture the influence of omitted variables that are state specific and do not vary over time, while the time dummies will capture any country-wide variables that are common to all states at a point in time. (Furthermore, there are also more observations when using panel data, resulting in more variation.)
(b) Estimating the model by OLS but including only time dummy variables results in the following output
where $S H Y$ is the proportion of teenagers in the population, and uram is the prime-age male unemployment rate. Coefficients for the time fixed effects are not reported. Numbers in parentheses are homoskedasticity-only standard errors.
Comment on the above results. Are the coefficients statistically significant? Since these are level regressions, how would you calculate elasticities?
There is a negative relationship between minimum wages and the employment to population ratio. Increases in the share of teenagers in the population result in higher employment to population ratio, and increases in the prime-age male unemployment rate lower the employment to population ratio. 20 percent of employment to population of teenagers variation is explained by the above regression. The relative minimum wage and the prime-age male unemployment rate are significant using a $1 \%$ significance level, while the proportion of teenagers in the population is not. The important point is to note that elasticities vary with levels. One possibility is to report elasticities at the sample means.
(c) Adding state fixed effects changes the above equation as follows:
$$
\begin{aligned}
& (0.10) \
&
\end{aligned}
$$
Compare the two results. Why would the inclusion of state dummy variables change the coefficients in this way?
The parameter of interest here is the coefficient on the relative minimum wage. While it was highly significant in the previous regression, it has now changed signs and is statistically insignificant. The explanatory power of the equation has increased substantially. The size of the other two coefficients has also decreased. The results suggest that omitted variables, which are now captured by state dummy variables, were correlated with the regressors and caused omitted variable bias.
The significance of each coefficient decreased, yet $\bar{R}^2$ increased. How is that possible? What does this result tell you about testing the hypothesis that all of the state fixed effects can be restricted to have the same coefficient? How would you test for such a hypothesis?
The influence of the state dummy variables is large. These are bound to be statistically significant and the hypothesis to restrict these coefficients is zero is bound to fail. Since these are linear hypothesis that are supposed to hold simultaneously, an F-test is appropriate here.
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