Regression Analysis is a Statistical technique that actually explains the change in dependent variable due to movement in other independent variables. It is a technique of predicting the unknown variable through the known variables. A dependent variable is the variable which is dependent over the actions of the independent variable. Any changes made in the independent variable will lead to a change in the dependent variable.
This is a cause and effect relationship between the variables. To understand the relationship of dependent and independent variable in regression analysis : Let us explain it by an example:. This equations explains that Quantity demanded is a function of all these variables. Any change in these variables will change the Quantity Demanded keeping other things constant. The reason of using the word of keeping other things constant is because we will not be able to understand the effect of a single independent variable over the independent variable if all the variables and their effects are checked simultaneously.
That is why , we estimate the variable in a such a way that it keeps other things constant while analyzing the effect of a single variable over the dependent variable.
Notice that much of the econometric analysis is concerned with cause and effect relations. Additional variables such as the market capitalization of a stock, valuation ratios, and recent returns can be added to the CAPM model to get better estimates for returns.
These additional factors are known as the Fama-French factors, named after the professors who developed the multiple linear regression model to better explain asset returns.
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We and our partners process data to: Actively scan device characteristics for identification. I Accept Show Purposes. Your Money. Personal Finance. Your Practice. Popular Courses. Economics Macroeconomics. What Is Regression? Key Takeaways Regression helps investment and financial managers to value assets and understand the relationships between variables Regression can help finance and investment professionals as well as professionals in other businesses.
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Create a free Team What is Teams? Learn more. Why do researchers in economics use linear regression for binary response variables? Ask Question. Asked 4 years, 1 month ago. Active 2 years, 11 months ago. Viewed 2k times. My question is therefore: Why is linear regression favoured over for instance logistic regression in the field of economics?
Improve this question. Nick Cox Economics and econometrics also have a vast literature on logit and probit and related models. I am an outsider too and I can't easily quantify relative use, but the literature is large enough to refute "ubiquitous" meaning, everywhere! There is a question here about why the so-called linear probability model is used at all and I don't think the explanation need be deep or hard to find: it is simple to understand and sometimes it works adequately.
I wouldn't worry too much about it. Certainly, stating that "You wouldn't worry too much about it" is irresponsible to the question. Depending on sub-field, Economics can have very strong relationship with mathematics and statistics.
It's just that Economists are often concerned with causal inference while happened to also have to deal with observational data like a lot of social sciences do. This makes it extremely hard to establish strong mathematical rigor without bringing in some economic intuition. Show 1 more comment. Active Oldest Votes. However , he does include a short list of reasons why researchers choose to use it: It's computationally simpler.
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