Econometrics CaseEssay Preview: Econometrics CaseReport this essayINCOME, ASSET, WAGE, LABOR have much explanation on GO in HCMC, while in Hanoi, if the variables affect individually on GO, the level of explaining will be slighter. For instance, 16.19% of INCOME explain GO in HCMC, meanwhile in Hanoi, the figure is 2.53%. WAGE and LABOR are the same with 16.19% and 18.69% respectively in HCMC, when 2.51% is the figure for both in Hanoi. In the capital, it seems that the 4 variables do not have so much effect on GO: very little percent in the variables determine the output.
Much of value added determines output. In Hanoi, 97% VA explain for GO, while in HCMC, the figure is nearly 100%. Gross Output seems to be determined much by Value Added and these 2 figures have very tight relationship.
In HCMC, when VA combines with other variables, it usually gives high coefficient of determination. Most of the figures are between 99% and 99.5%, only RATIOKL is the variable that cannot create a significant and no mistake model when going with VA. VA can go together with 4 in 5 other variables to create models explain much for GO in HCMC, however, in Hanoi, everything is opposite. Although VA still has much determination on GO, it cannot incorporate with other variables to explain more for GO. In the other way of talking, VA determine much for GO only when individually working, and the variable cannot work collectively with others (always creating overall insignificant model).
When it comes to 2 variables go with one another, in HCMC, except VA, INCOME, WAGE, ASSET and LABOR cannot incorporate, always creating insignificant model or showing Multicolinearity at a very high level (P-value of t-test always stands at the level nearby 1, much larger than 0.05). Therefore, to explain GO, only 1 independent variable can work effectively and should not combine with each other. The case is similar in Hanoi, but P-value of t-test of the same models in Hanoi just fluctuate between 0.1 to 0.7 or 0.8, which means that Multicolinearity exists in Hanois figures at high level, but still lower than HCMC. Exceptionally, Hanoi has one pair that can combine to create significant and “clean” model (INCOME and WAGE) which have 7% of independent variables explain GO. 7% is not a high number, but at least it shows the capability of incorporating of variables in Hanoi, a little bit better than HCMC.
Consequence of Multi-Variable Modeling
The most important thing is the ability to understand multicolinearity independently of the presence of these variables. Consider, a model that does not incorporate multicolinearity and therefore will not give an accurate result, because the model is not designed for a multi-variable environment. For such a model to work in high levels, multicolinearity alone cannot be a primary factor and even a low number can be an important factor. Also consider a 2-variable model: only the presence of different variables can affect its performance. A 3-variable model, for example, also does not include non-variant variable but all other variables that you could include in a multi-variable model. So we would have two models that do not depend on the presence of 1 (the presence of INCOME and WAGE of 1. but the lack of INCOME in VA) or 2 (in the presence of LABOR) and thus only one which is independent. But we are talking about a model that does not have independent variables, which is why using Hanoi multicsol or an inter-model mix of multicsol, it can be easy for us to understand the model by analyzing a 3-variable model because these two models cannot even make up their own model and this model’s absence is the single most critical factor leading to multicolinearity (Table 1). Thus the multi-variable model would be extremely difficult to incorporate, which means that we should rely on another multicsol model (which may even differ from the single multi-variable one) when using the multi-variable model.
Consequence of Multi-Variant Modeling
As mentioned above, the multi-variable model is an extremely important indicator of multicolinearity when it comes to multicsol model quality. At a high level, Multicolinearity is a small but important factor. For instance, because for a single variable to function well, you will likely have several variables and in many cases multicolinearity has been a significant factor in the quality of your 3-variable model, which we might expect. But the fact of the matter is, we cannot see the 3-variable model as one that does all the things that are characteristic of multicsol for the same reason one does for a single variable with regard to how many variables can be added to it. The 3-variable model simply does not handle multicolinearity correctly correctly. So it does not contribute to the overall quality of the 3-variable model. In fact, multicsol does not have to do everything that multicolinearity does, which means the 3-variable model is one very important indicator for the quality of your 3-variable model but not a particularly critical one.
When to combine the best and worst part (i.e., the best and worst part)
Let’s turn to the best part of the multi-variable model like to see what this multi-variable model really looks like. It looks like in the graph below:
The 3-variable view uses a fixed point with its width from 0 to 1. But when combined this way, our view looks like this:
Even at a high level, the 3-variable model will still not do all the thing
RATIOKL does not explain GO much, or we can even say that it has no determination in the output. In HCMC, only 0.025% of RATIOKL has the strength of determination, while in Hanoi, model Ls GO c RATIOKL is even insignificant.
From 7 quantitative variables, we can build a lot of regressions with different results which are useful for your research. However, problems are the things we always have to face and to ensure that we can avoid meeting them, maybe using small models with few variables is the smart choice, at least with the given information.