Econometrics Archives - Page 2 of 2

What is meant by multicollinearity? What are its consequences on estimates? What remedial measures do you suggest for the problem?

Introduction Multicollinearity is a common problem encountered in multiple regression analysis. It occurs when two or more independent variables in a regression model are highly linearly related. This leads to complications in estimating the individual effect of each explanatory variable on the dependent variable. While multicollinearity does not violate the assumptions of the classical linear […]

What is meant by multicollinearity? What are its consequences on estimates? What remedial measures do you suggest for the problem? Read More »

What is meant by identification in a simultaneous equation model? Check the identification status of the equations in the following model: Demand function: 𝑄t = 𝛼0+ 𝛼1𝑃t + 𝛼2𝑋t + 𝑢1t Supply function: 𝑄t = 𝛽0 + 𝛽1𝑃t + 𝑢2t

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Introduction In econometrics, simultaneous equation models are used when two or more endogenous variables are determined together, influencing each other. Unlike single-equation models where one variable is dependent and the others are independent, simultaneous equation models have multiple equations with interdependent variables. One crucial concept in dealing with such models is identification. What is Meant

What is meant by identification in a simultaneous equation model? Check the identification status of the equations in the following model: Demand function: 𝑄t = 𝛼0+ 𝛼1𝑃t + 𝛼2𝑋t + 𝑢1t Supply function: 𝑄t = 𝛽0 + 𝛽1𝑃t + 𝑢2t Read More »

Consider the regression equation 𝑌i = 𝛼 + 𝛽𝑋i + 𝑢i where 𝑢i is a stochastic error term. a) Explain how estimators of 𝛼 and 𝛽 can be obtained. b) What algebraic properties do the estimators fulfil?

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Introduction Regression analysis is a powerful statistical technique used to determine the relationship between a dependent variable (Y) and an independent variable (X). One of the simplest forms of regression is the simple linear regression, represented as: Yi = α + βXi + ui Where: Yi: Dependent variable for observation i Xi: Independent variable for

Consider the regression equation 𝑌i = 𝛼 + 𝛽𝑋i + 𝑢i where 𝑢i is a stochastic error term. a) Explain how estimators of 𝛼 and 𝛽 can be obtained. b) What algebraic properties do the estimators fulfil? Read More »

Explain why an error term is added to the regression model. What assumptions are made about the error term? What are the implications of such assumptions? What will happen to the estimators of the parameters of the regression model, if these assumptions are violated?

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Introduction In econometric modeling, a regression equation is used to express the relationship between a dependent variable and one or more independent variables. However, this relationship is not always perfect. There are many factors that influence the dependent variable which are either unobserved or not included in the model. To account for these unknown influences,

Explain why an error term is added to the regression model. What assumptions are made about the error term? What are the implications of such assumptions? What will happen to the estimators of the parameters of the regression model, if these assumptions are violated? Read More »

What is meant by heteroscedasticity? What are its consequences? How do you detect the presence of heteroscedasticity in a data set?

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Introduction In econometrics, the reliability of regression results depends heavily on assumptions about the error term. One such assumption is homoscedasticity, which implies that the variance of the error term remains constant across all levels of the explanatory variable. When this assumption is violated, we face the problem of heteroscedasticity. It is a common issue

What is meant by heteroscedasticity? What are its consequences? How do you detect the presence of heteroscedasticity in a data set? Read More »

Explain the various functional forms of regression model. From the imaginary data for 46 Districts in UP related to the year 2020, the following regression results are given: Log C= 4.30-1.34 log P +0.17 log Y Se= (0.91) (0.32) (0.20) R² = 0.27 Where C= consumption of Cigarette packs per year P= real price per pack Y= real disposable income per capita. i. What is the elasticity of demand for cigarettes with respect to price? ii. What is the income elasticity of demand for cigarettes? Is it statistically significant? iii. How would you retrieve R² from the adjusted R² given above.