MECE-101

MECE-101: INTRODUCTORY ECONOMETRIC METHODS – Assignment Answer Index (2024-25)

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MECE-101: INTRODUCTORY ECONOMETRIC METHODS Tutor Marked Assignment (2024-25) Course Code: MECE-101 Assignment Code: MECE-101/AST/2024-25 Total Marks: 100 Section A – (20 marks each) What is meant by heteroscedasticity? What are its consequences? How do you detect the presence of heteroscedasticity in a data set? Explain why an error term is added to the regression model. […]

MECE-101: INTRODUCTORY ECONOMETRIC METHODS – Assignment Answer Index (2024-25) Read More »

Write short notes on the following: a) Dummy variable trap b) Coefficient of Determination

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Introduction In econometrics, understanding the behavior of regression models and their components is crucial. Two important concepts often encountered in practical modeling are the dummy variable trap and the coefficient of determination (R²). These help in model specification and result interpretation, especially in multiple regression analysis. a) Dummy Variable Trap A dummy variable is a

Write short notes on the following: a) Dummy variable trap b) Coefficient of Determination Read More »

While estimating a regression model you found that the explanatory variable is measured with certain error. Specify the model. What are its consequences on the parameters?

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Introduction In regression analysis, we assume that the explanatory variables are measured accurately. However, in many real-life applications, the independent variables may suffer from measurement errors due to instrument limitations, reporting mistakes, or data entry errors. This issue leads to what is known as the problem of errors-in-variables, which can severely affect the estimation of

While estimating a regression model you found that the explanatory variable is measured with certain error. Specify the model. What are its consequences on the parameters? Read More »

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

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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 »

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