All the modules for econometrics and the detailed contents of each module can be found below. Classes focus on both theory and application of models, whereas, workshops and webinars are focused more on application aspects of econometric models.
List of Modules for Econometrics
- One on one classes
- Group classes
- Workshops
- Webinars
For prices and detailed contents of each module, see below.
Contents of all Modules for Econometrics
Module 1: Ordinary least squares and Goodness of fit
Ordinary least squares (OLS) is the basic starting point of econometric models. In this module, you will learn about the logic behind OLS, its inner workings, application and interpretation.
You will learn to deal with different situations where OLS can be used and different ways to adapt the models based on your needs.
In the section related to goodness of fit, you will master the application of basic and advanced statistical methods to assess the reliability of applied models, to choose among different independent variables or models.
The detailed contents of this module include:
Ordinary Least Squares (OLS)
- OLS estimation
- Construction of matrices for OLS using real data (how various software do it)
- Estimation of residuals or error terms
- Implementation and interpretation of OLS
- Log transformation and its significane in economics
Problems associated with OLS
- Heteroscedasticity: testing for heteroscedasticity and its solutions
- Autocorrelation: detection and remedies
- Multicollinearity: detection and possible solutions
- Theory and real life applications of tests and solutions of the above problems
Goodness of fit
- R-square and Adjusted R-square
- Model selection: Akaike and Schwarz Information Criteria
- Standard error of estimate, confidence intervals and P-value
- Theory, implementation and interpretation of the above
Pricing (Contact for exact Quote)
One on one classes
INR 5000 – 8000 (approx. USD 61 – 98)
Group classes
INR 3000 – 4500 (approx. USD 37 – 55) per student
Workshops and webinars
INR 800 (approx. USD 10) per student
Module 2: Time series analysis
The study of time series holds a special place in economics and econometrics. Time series models and techniques are some of the most widely used analytical tools.
In this module, we will focus on important concepts related to time series data. You will learn about stationarity and its meaning, unit root processes, tests of stationarity and order of integration.
You will learn about “Cointegration” and its meaning. We will discuss the theory, application and interpretation of VAR (Vector Autoregressive Model) and VECM (Vector Error Correction Mechanism) on real data.
The next section of the module will focus on various other time series models including ARDL (Autoregressive and Distributed Lag Models), ARMA, ARIMA models, ARCH and GARCH models. Along with theoretical background, we will focus on the application and interpretation of these models.
The detailed contents of this module include:
Stationarity
- Meaning of stationarity
- Stationary, non-stationary and unit root processes
- Random walk with and without drift, Difference and trend stationary processes
- Tests of stationarity: Autocorrelation Function, Correlogram, Q and LB statistics
- Unit root based tests: Dickey Fuller Test and Augmented Dickey Fuller Test
- Order of integration
Cointegration
- Meaning of cointegration and its significance in economics
- Tests of cointegration: Engle-Granger Test, Johansen's Test, ARDL for cointegration
- VAR (Vector Autoregressive Model): theory, implementation and interpretation
- Error correction mechanism: VECM model, its implementation and interpretation
- VAR vs VECM: when to use them
Other time series models and techniques
- ARDL (Autoregressive and Distributed Lag Models): Koyck transformation, Partial Adjustment Model and Adaptive Expectations Model
- Granger Causality
- ARIMA and Box-Jenkins Methodology
- Volatility Forecasting: ARCH and GARCH
Pricing (Contact for exact Quote)
One on one classes
INR 6000 – 9500 (approx. USD 73 – 116)
Group classes
INR 4000 – 5500 (approx. USD 49 – 67) per student
Workshops and webinars
INR 1000 (approx. USD 12) per student
Module 3: Panel data models
Panel data is a combination of cross sectional and time series elements which presents a different set of challenges for analysis and prediction.
This module will focus on the application of models specific to panel data. You will learn about the theory and application of Fixed Effects, Random Effects and Mixed models. Moreover, we will discuss when to use Panel data models instead of Pooled OLS and how to choose between fixed and random effects models.
The detailed contents of this module include:
Fixed effects and Random effects
- Fixed Effects Model: theory, estimation and interpretation
- Random Effects Model: theory, estimation and interpretation
- Random Coefficients Model
Choosing between Fixed and Random Effects Models
- Wu-Hausman Test
- Breusch-Pagan Lagrange Multiplier Test
More Applications on Real Data
- Checking for cross-sectional and time specific effects
- Pooled OLS vs Fixed/Random Effects
- Mixed Models: combination of fixed and random effects in a model
Pricing (Contact for exact Quote)
One on one classes
INR 4000 – 6000 (approx. USD 49 – 73)
Group classes
INR 2500 – 3500 (approx. USD 30 – 43) per student
Workshops and webinars
INR 800 (approx. USD 10) per student
Module 4: Simultaneous Equation Models
Economic relationships are complex and the use of single equation models is not always appropriate or reliable. In such situations, simultaneous equation models are employed to capture interactions among economic variables.
You will learn about the situations where simultaneous equation models are needed. We will discuss about the meaning of simultaneous equation bias and problem of identification.
The module will focus on theory and application of Indirect Least Squares, Two-stage Least Squares (2SLS) and Three-stage Least Squares (3SLS).
The detailed contents of this module include:
Need for simultaneous equation models
- Simultaneous equation bias
- Problem of identification
- Determining under-identified, just-identified and over-identified models
- When to use simultaneous equation models: Test of Endogeneity or Simultaneity
Simultaneous Equation Models
- Indirect Least Squares: deriving reduced form equations and coefficients
- Indirect Least Squares: application and interpretation
- Two-stage Least Squares (2SLS): theory, implementation and interpretation
- Three-stage Least Squares (3SLS): underlying reasoning and implementation
Pricing (Contact for exact Quote)
One on one classes
INR 4000 – 6000 (approx. USD 49 – 73)
Group classes
INR 2500 – 3500 (approx. USD 30 – 43) per student
Workshops and webinars
INR 800 (approx. USD 10) per student
Module 5: Qualitative response
Qualitative response models are ones with categorical or qualitative dependent variables, the categories or choices may be binary or more than two.
In this module, we will discuss why usual models such as OLS are inappropriate in case of qualitative dependent variables. You will learn to apply models that are more suitable in these cases, which include Logit Model, Probit Model and Tobit Model.
We will further discuss goodness of fit measures for such models with special focus on classification tables and how to adapt them to different situations.
The detailed contents of this module include:
Qualitative response
- OLS estimation and its problems with qualitative dependent variable
- Logit Model: theory, implementation and interpretation
- Probit Model: theory, implementation and interpretation
- Estimation of marginal effects and their interpretation
- Censored dependent variable: Tobit Model
Goodness of fit
- Classification table and its uses
- Count R-square, Pseudo R-square, McFadden R-square and Likelihood-Ratio test
Other applcations
- Multiple categories: Multinomial Logit Model
- Ordered choice: Ordered Logit Model
- Application and interpretation of the above models
Pricing (Contact for exact Quote)
One on one classes
INR 4000 – 6000 (approx. USD 49 – 73)
Group classes
INR 2500 – 3500 (approx. USD 30 – 43) per student
Workshops and webinars
INR 800 (approx. USD 10) per student
