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MECE-102: Advanced Econometric Methods – Assignment Answer Key 2024-25

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MECE-102: ADVANCED ECONOMETRIC METHODS Tutor Marked Assignment Course Code: MECE-102 Asst. Code: MECE-102/AST/2024-25 Maximum Marks: 100 Note: Answer all the questions. While questions in Section A carry 20 marks each, those in Section B carry 12 marks each. Section A a) What is simultaneity bias? Explain the conditions required for identification of parameters in a […]

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Write short notes on the following: a) ARCH model b) Granger-causality

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a) ARCH Model (Autoregressive Conditional Heteroskedasticity) The ARCH model, introduced by Robert Engle in 1982, is used to model and forecast time-varying volatility in time series data, especially in financial markets where periods of high and low volatility alternate. Key Features: Conditional heteroskedasticity: The variance of the error term depends on past squared errors. Captures

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What are the advantages of panel data models? Specify the fixed effects model and explain how it can be estimated.

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Introduction Panel data models have gained significant importance in empirical economic research due to their ability to control for unobserved heterogeneity and improve the reliability of estimates. Panel data refers to multi-dimensional data involving observations over time for the same individuals, households, firms, or countries. Advantages of Panel Data Models Controls for Unobserved Heterogeneity: Panel

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Explain the central idea behind the multinomial logit model. What the underlying assumptions in this model?

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Introduction The Multinomial Logit Model (MNL) is an extension of the binary logit model used in econometrics when the dependent variable has more than two unordered categories. It is frequently applied in modeling individual choices among multiple discrete alternatives, such as choosing between different brands, modes of transportation, or political parties. Central Idea Behind the

Explain the central idea behind the multinomial logit model. What the underlying assumptions in this model? Read More »

What is meant by dynamic model? Explain how the following model can be estimated? 𝑦𝑡 =∝ +𝛽𝑥𝑡 + 𝛾𝑦𝑡−1 + 𝑢𝑡 where |𝛾| < 1 and 𝑢𝑡 = 𝜌 𝑢𝑡−1+ 𝜀𝑡

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Introduction In econometrics, a dynamic model is one that includes lagged values of the dependent or independent variables. These models are particularly useful when analyzing time series data where past events influence current outcomes. Dynamic models are essential for studying the adjustment process and persistence over time. What is a Dynamic Model? A dynamic model

What is meant by dynamic model? Explain how the following model can be estimated? 𝑦𝑡 =∝ +𝛽𝑥𝑡 + 𝛾𝑦𝑡−1 + 𝑢𝑡 where |𝛾| < 1 and 𝑢𝑡 = 𝜌 𝑢𝑡−1+ 𝜀𝑡 Read More »

What is the underlying idea behind the probit model? Explain how parameters are estimated in the probit model.

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Introduction In econometrics, many real-world situations involve binary outcomes — for example, whether a person purchases a product (yes or no), passes an exam (pass/fail), or defaults on a loan (default/no default). These binary dependent variable models require special treatment. One of the most widely used models for such data is the probit model. What

What is the underlying idea behind the probit model? Explain how parameters are estimated in the probit model. Read More »

Distinguish between weak stationarity and strong stationarity. Explain the methods of testing for stationarity in a univariate time series model.

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Introduction Stationarity is a fundamental concept in time series analysis. A stationary time series is one whose properties do not depend on the time at which the series is observed. In econometrics, stationarity ensures that the statistical inferences made about the model are valid. There are two main types of stationarity: weak stationarity and strong

Distinguish between weak stationarity and strong stationarity. Explain the methods of testing for stationarity in a univariate time series model. Read More »

a) What is simultaneity bias? Explain the conditions required for identification of parameters in a simultaneous equation model. b) In the following two-equation system check the identification status of both the equations. 𝑌1 =∝1+∝2 𝑌2 + 𝑢1 𝑌2 = 𝛽1 + 𝛽2𝑌1 + 𝛽3𝑍1 + 𝛽4𝑍2 + 𝑢2

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Introduction Simultaneity bias and identification are two fundamental concepts in econometrics, particularly when dealing with simultaneous equation models (SEMs). SEMs occur when more than one endogenous variable is determined within a system of equations, causing problems in estimation using ordinary least squares (OLS). a) What is Simultaneity Bias? Simultaneity bias occurs when an explanatory variable

a) What is simultaneity bias? Explain the conditions required for identification of parameters in a simultaneous equation model. b) In the following two-equation system check the identification status of both the equations. 𝑌1 =∝1+∝2 𝑌2 + 𝑢1 𝑌2 = 𝛽1 + 𝛽2𝑌1 + 𝛽3𝑍1 + 𝛽4𝑍2 + 𝑢2 Read More »

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.

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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?