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4

Depends on a trend. Trends come generally in two categories: Deterministic trend - this can be controlled for using various methods. For example, in panel regression you could include time fixed effects that would correct for an effect of each time period on all firms and hence should control for a trend in data. This would look like this: $$y_{it} = \beta_{...


0

As mentioned, if you can identify the sources used for the reported figures, that should solve the problem. Art is correct in terms of the the unit of time with Q4/Q4 comparing growth from the same quarter from the prior year, and that the Y/Y based on the according calendar year. His calculations appear to be correct as well, and my guess is that it was ...


1

If you could include the source(s) you were talking about that would be great. Without further info, my initial guess is that the Q4/Q4 means you compare GDP in 2018Q4 to GDP in 2017Q4. Y/Y means you compare GDP in the whole year of 2018 to that of 2017. Upon further inspection, if you take a look at the US's real GDP, you'd get the following: Growth in ...


0

Friend, To find data related to real GDP per capita of a country go to the World Bank Group economic database (https://data.worldbank.org/) World Bank Open Data portal. Type in GDP per capita then scroll down and select a country. To measure economic output growth you should use Real GDP. GDP per capita shows how much economic production value can be ...


1

Well first of all the story why we only record final consumption is not completely straight - it’s not because we only care about activity of “real person” or consumer. But because counting it all the way would mean we double count the value of the good. For example imagine the following situation. A wood is produced and sold for 15\$ to furniture company ...


3

The difference is that the first regression is unbiased only if you can assume that high school GPA and ACT score are orthogonal on each other $cov(x,z)=0$ where $x$ is shortcut for high school GPA and $z$ for ACT score. Or if you can assume the second variable ATC score does not affect the dependent variable at all $\beta_2=0$. This is because in simple ...


2

I have spent three days thinking about how to answer your question. I still haven't really decided how it should be answered, so this answer is more in the way of a set of observations. I can readily imagine a formal treatment of general linear models or limited dependent variables models could do better than this answer. The first thing I would observe ...


0

You should definitely adjust for inflation, as you should with all monetary variables. I wouldn't worry using the CPI, you could use an alternative indicator as a robustness check to see whether the choice of indicator affects your main conclusions.


3

Actually there is no single agreed upon definition of low middle and high class. For example, Pew research center uses the following definitions: “Pew Research defines middle-income Americans as those whose annual household income is two-thirds to double the national median. For a family of three, that ranges from \$42,000 to \$126,000 in 2014 dollars. ...


-2

Large consumer good companies not using advertising because now a day every people using Network marketing.QNet is also done Network marketing business.


1

It is allowed in a sense it does not violate any assumption of the OLS estimator but it is also unlikely that you would need such a specification for a model. If you include $log(x_2)$ in model specification that means you are trying to control for the fact that there is some exponential relationship between $y$ and $x$ like $y=x^b$ and taking logs of $x^b$ ...


4

Yes it is "allowed". Econometrically it is not a problem. The real question is how useful such a model is for your setting and that will depend on your exact research question, variables and data.


0

It might be too long for a comment so I write it as an answer. Apologies if I missed any ongoing discussions. Let me say the general background first and then my thoughts about OP's question. The model $y=a+bx+e$ as such tells us nothing about what the true $b$ is. We need more restrictions to identify $b$. There can be many ways of making identifying ...


1

a) not sure what exactly you mean here by probability distribution but if you mean the distribution of potential Y given the distribution of estimated coefficient b and a it would be correct, although usually we look at expectation to get single number than on distribution in which case it would be $E(Y|X)$ b) is correct


0

There seem to be (at least) two ways of interpreting the concept of a 'data generating process': The data generating process is a correct description of the causal process that generates the values of the dependent variable $Y$. The data generating process specifies the expected value of the dependent variable $Y$ given the values taken the independent ...


0

I think that the wikipedia steps are not completely correct. When you perform Johansen cointegration test you first have to pretest the data to find if they have the same order of integration. That is all variables have to be either $I(1)$ or $I(2)$ etc. (see Verbeek, 2008). There are some cointegration tests and models that relax this assumption but ...


0

A OP's second expression corresponds to $$\Delta p = P(Y=1|D=1,X=\bar{X}) - P(Y=1|D=0,X=\bar{X}),$$ which is $$ \Delta p = \Lambda(\beta_0 + \beta_1 + \beta_2 \bar{X}) - \Lambda(\beta_0 + \beta_2 \bar{X}), $$ where $\Lambda(z) = e^z/(1+e^z)$. Rationale for dividing by p OP's first expression corresponds to $(\Delta p)/p$, where $p$ is the proportion of 1'...


0

This is not about causality. Causality is a statement that $x$ causes $y$ but it is not the value of the coefficient ($a$ in your model). Even if you care just about coefficient estimates you will get wrong answer. This is because the formula for estimate of the coefficient $a$, that is $\hat{a}$, in simple OLS can be expressed as: $$\hat{a} = a + \beta \...


1

The word "noise" implies that there is error in the measurement of household debt, but there is no good reason to believe this is so. In fact, I argue that a finer time period would give you more detail, and a more accurate measurement of the correlation between two variables. You don't want to find a moving average because that throws away valuable ...


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