ECOM20001 Econometrics Unimelb Assignment Answer
This is the assignment sample of ECOM20001 Econometrics Unimelb.
This is a course about econometrics. You will learn how to understand probabilities and statistics, single linear regression, multiple linear regression, and non-linear regression models. This class is a pass/fail class.
The overall aim of the course is to teach you the concepts and hands-on skills that are necessary for conducting an econometric analysis using STATA software.
The course will focus mainly on applied statistical techniques rather than theoretical modeling, although lectures will provide background theory where relevant.
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Assignment Solutions Of ECOM20001 Econometrics Unimelb.
Assignment Activity 1. Apply the least-squares method of estimation and inference to the context of single and multiple linear regression.
The least-squares method of estimation and inference is one of the most commonly used for linear models (such as those encountered when estimating blood pressure from height.)
There are two equations to estimate a line, either equation will provide the same result. The first uses minimizing the sum of squared residuals, and the second by minimizing sum absolute residuals.
However, these two methods should not be treated as the same. If you use them together and there are a lot of noises in your data, then it might lead to over-fitting.
This means that using equation 1 or 2 could introduce more bias into the model. Models with many parameters also have a higher probability of under-fitting.
When drawing conclusions from a regression analysis result on estimates with other unknowns, it should be stressed that the least-square estimator is only a best linear unbiased estimate (BLUE).
The problem of identifying the correct model in which the parameters are all identified property is often referred to as “over-identification” and if there isn’t enough data then it can never be done.
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Assignment Activity 2. Use the statistical programs to estimate econometric models, test hypotheses, and undertake forecasting exercises.
A good statistical software program is necessary to estimate econometric models.
Many types of statistical programs are available for different applications and some websites provide customized packages (especially for uni students).
So it’s important to choose one that is not only sophisticated enough to provide the statistical functionalities you need but also easy-to-use.
Besides, modern computer systems have more power than they did a decade ago, so many low-end programs will work sufficiently well if your data isn’t excessively complex.
In summary, understand your budget and what features you require in order to be satisfied with the outcome of your econometric models’ estimation.
Give an example of a forecasting exercise undertaken without using econometric models.
An editorial calendar is a list of articles and their publication dates. The editorial calendar also includes past performances to help you decide which article will do well in the future.
Or it may list some ideas for stories that may be worth moving up on the timeline if there are open slots for future articles; this can form a hybrid between test hypotheses and forecasting exercises.
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Assignment Task 3. Understand and explain various problems that arise in the development and interpretation of econometric models
There are several problems that arise in the development and interpretation of econometric models.
Firstly, there is a problem of parameter instability, whereby parameters taking on different values at one point in time may take on different values at another point in time due to external (or internal) factors.
The second problem is that we only have data for models and causal mechanisms in the range where they are used. So if you use this knowledge, your estimates will be biased.
If you have only observed someone up to the age of 20 then more information from 10-30 years old will be missing. Thirdly, Kelly’s theorem in math says that the results of any model will look different depending on how you sort your data.
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