Econometrics Tutors in Delhi

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What is Econometrics

Econometrics Tutors in Delhi

Econometrics is the application of statistical to economic data in order to give empirical content to economic relationships. More precisely, it is “the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference” An introductory economics textbook describes econometrics as allowing economists “to sift through mountains of data to extract simple relationships”. Jan Tinbergen is one of the two founding fathers of econometrics.The other, Ragnar Frisch, also coined the term in the sense in which it is used today(Econometrics Tutors in Delhi)

A basic tool for econometrics is the multiple linear regression model. Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods. Econometrics try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency. Applied econometrics uses theoretical econometrics and real-world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting.
The details of the Syllabus, Topic-wise Reading list, recommended text books and are attached.

SYLLABUS

I. Nature and scope of Econometrics
II. Statistical Inference
i. Normal distribution; chi-sq, t- and F-distributions
ii. Estimation of parameters
iii. Testing of hypotheses
iv. Defining statistical hypotheses
v. Distributions of test statistics
vi. Testing hypotheses related to population parameters
vii. Type-I and Type-II errors; Power of a test
viii. Tests for comparing parameters from two samples.
III. Simple Linear Regression Model: Two Variable Case
i. Estimation of model by method of ordinary least squares
ii. Properties of estimators
iii. Goodness of fit
iv. Testing of Hypotheses
v. Scaling and units of measurement
vi. Confidence intervals
vii. Gauss Markov Theorem
viii. Forecasting
IV. Multiple Linear Regression Model
i. Estimation of parameters
ii. Properties of OLS estimators
iii. Goodness of fit- R2 and Adjusted R2
iv. Partial regression coefficients
v. Testing Hypotheses: Individual and Joint
vi. Functional Forms of Regression Models
vii. Qualitative (dummy) independent variables
V. Violations of Classical Assumptions: Consequences, Detection and Remedies
- Multi collinearity
- Heterosexuality
- Serial Correlation
- Specification Analysis
- Omission of a relevant variable
- Inclusion of irrelevant variable
- Tests of specification