Applied Econometrics Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

The Applied Econometrics - Essentials training course, focuses on the development and use of single-equation econometric models that enable analysts to better understand their economic/business landscape and to improve their ability to make sound economic/business forecasts. Through hands-on exercises, you will gain knowledge of the practical elements of applied econometric analysis. The overall aims are to sharpen the quantitative, statistical, and analytical skills of dealing with problems and issues related to business and economics as well as to improve communication skills in reporting findings to decision makers.

The Applied Econometrics - Advanced training course will enable analysts to better understand their economic/business landscape and to improve their ability to make sound forecasts. Through applications, you will gain knowledge of the practical elements of applied econometric analysis. The overall aims are to sharpen the quantitative, statistical, and analytical skills in dealing with problems and issues related to business and economics as well as to improve communication skills in reporting findings to decision-makers.

By attending Applied Econometrics - Essentials workshop, delegates will learn to:

Develop and use single-equation econometric models
Improve your ability to make sound forecasts
Sharpen your quantitative, statistical, and analytical skills
Improve the effectiveness of how you translate technical information for key decision-makers
Use SAS software to conduct econometric analyses

By attending Applied Econometrics - Advanced workshop, delegates will learn to:

Detect and circumvent collinearity and ill-conditioning problems in econometric models
Detect and assess data outliers and leverage points
Detect structural change and test the stability of structural coefficients
Incorporate dynamic elements in econometric models principally through the use of distributed lags
Use Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized ARCH (GARCH) models
Use qualitative choice models and censored response models
Use simultaneous-equation models
Use seemingly unrelated regression models

For Applied Econometrics - Essentials:

Basic knowledge of SAS software, including SAS procedures such as PROC UNIVARIATE and PROC REG
Basic knowledge of statistics, especially in conjunction with tests of hypotheses (t tests and F tests) and regression analysis
Familiar with regression analysis
Basics of economic and business concepts

For Applied Econometrics - Advanced:

Basic knowledge of SAS software, including SAS procedures such as PROC REG, PROC AUTOREG, and PROC MODEL
Know the equivalent of the material covered in the Applied Econometrics - Essentials course, specifically data issues inherent with econometric models, the development and estimation of single-equation econometric models, hypothesis testing associated with these models, the construction and interpretation of dummy variables, and the detection and circumvention of serial correlation (autocorrelation) and heteroscedasticity
Some knowledge pertaining to developing and evaluating ex-post and ex-ante forecasts

Academicians, economists, forecasters, and government and business analysts

The Nature of Applied Econometrics

What is applied econometrics?
Course of action - development of formal quantitative models
Disciplines in applied econometrics
Empirical models and modeling approaches
Components of applied econometrics
Products of applied econometrics
Generic Multiple Regression Model
Software considerations
Communication and aims for the analyst

Data Considerations and Ordinary Least Squares Estimation of Single-Equation Econometric Models

Data
Getting a feel for the data
Massaging the data
Estimation of the Simple Linear Regression Model
SAS output of the simple linear regression of total personal bankruptcy (TPB) on real gros domestic product (RGDP)
Estimation of the Multiple Regression Model - the Generic Single-Equation Econometric Model
Example: SAS output of the demand function for shrimp

Interpretation and Use of Estimated Coefficients and Forecasting with Single-Equation Econometric Models

Mathematical and statistical considerations in applied econometrics
Interpretation of estimated coefficients
Partial correlation coefficients
Alternatives to least squares estimation
Criteria for estimators
Interval estimation and confidence intervals
Forecasting with single-equation econometric models
Forecast evaluation
Illustration of out-of-sample forecasting witht he demand curve for shrimp

Common Tests of Hypotheses in Applied Econometrics

Introduction: preliminary statistical elements
Basics of hypothesis testing
Tests of hypotheses regarding structural parameters of econometric models
Tests of normality of residuals
Tests of hypotheses associated with the specification of econometric models

Use of Dummy (Indicator) Variables in Applied Econometrics

Intercept shifters
Slope shifters
intercept shifters and slope shifters
Final thoughts about the use of dummy (indicator) variables
Additional readings

Diagnostic Checks - Autocorrelation or Serial Correlation

Autocorrelation or serial correlation
Tests for serial correlation
Sample problem: the demand for shrimp
Sample problem: the demand for gasoline
Test for serial correlation in the presence of a lagged dependent variable
Summary remarks about the issue of serial correlation

Diagnostic Checks - Heteroscedasticity

Weighted least squares (WLS)
Example of econometric analysis with heteroscedasticity
Multiplicative and additive heteroscedasticity
Common tests of heteroscedasticity
Maximum likelihood (ML) as opposed to weighted least squares (WLS)
Recommended procedures to combat heteroscedasticity

Detecting and Circumventing Collinearity or Ill-Conditioning Problems

Introduction
Collinearity diagnostics
Solutions to the collinearity problem

Detecting and Assessing Data Outliers and Leverage Points

Background
Influence diagnostics
Solutions to the problem of influential observations
Robust regression techniques

Detecting Structural Change and Testing for the Stability of Structural Coefficients

Diagnostic tests for structural change
Illustration of sequential Chow tests
Illustration of the Farley, Hinrich, and McGuire test
Illustration of recursive coefficients, recursive residuals, CUSUM, and CUSUMSQ tests

Incorporating Dynamics through the Use of Distributed Lags

Approaches to distributed lag models
Sample problem: free-form lag
Sample problem: geometric lag
Sample problem: polynomial distributed lag
Partial adjustment model
Sample problem: partial adjustment model

Autoregressive Conditional Heteroscedasticy (ARCH) and Generalized ARCH (GARCH) Models

ARCH(q) model
Sample problem: ARCH(q) model
GARCH model
GARCH-M model variations (linear, square root, and log)
GARCH model with autoregressive errors
Example: ARCH/GARCH models for three month returns of SP500
Threshold GARCH model
Exponential GARCH (EGARCH) model
Example: exponential GARCH (EGARCH) model
Estimation of GARCH models

Qualitative Choice and Censored Response Models

Limited dependent variables
Probit/logit models
Computational methods and statistical considerations for empirical analysis
Sample problem: use of probit analysis
Sample problem: use of logit analysis
Censored response models
Censored samples: use of the Tobit model
Sample problem with the Tobit model
Heckman sample selection procedure
Sample problem with the Heckman sample selection procedure

Simultaneous Equation Models

Modeling approaches
Simultaneous systems
Simultaneous structural models
Simultaneous structural models
Identification issues
Example of order and rank conditions
Common methods of estimation
Simultaneous equation model of demand and supply relationships
Analytically derived reduced forms
Microeconomics specification of simultaneous equation models
Final form of the system
Determining whether a system is stable
Example of stability condition: Klein Model
Specification, estimation, and simulation of a dynamic macroeconomic simultaneous equation model

Seemingly Unrelated Regression Models

Seemingly unrelated regression models
Example of seemingly unrelated regression models
Example: demand for a cereal product from five retailers: HEB, Publix, Food Lion, Fred Myer, and Meijer
Seemingly unrelated regression models with restrictions
Rotterdam Model
Linear approximate almost ideal demand system (LA/AIDS) model
Example: demand interrelationships for spaghetti sauces: LA/AIDS model
Demand for spaghetti sauce example: use of PROC MODEL

Pooling of Time-Series and Cross-Sectional Data

To pool or not to pool
Single-Equation model specification associated with the pooling of time-series and cross-sectional data or the use of panel data
Typical assumptions when dealing with the pooling of time-series and cross-sectional data: Parks model
Typical assumptions when dealing with the pooling of time-series and cross-sectional data: error components model
Sample problem: the use of pooled OLS, Parks procedure, and error components procedure
Typical assumptions when dealing with the pooling of time-series and cross-sectional data: covariance model
Fixed and random effects
Sample problem: the use of the ANACOVA or LSDV model and the use of the one-way random effects model
Seemingly unrelated regression model (fixed and random effects)
Sample problem: the use of seemingly unrelated regression

SAS Procedures Associated with Advanced Topics in Applied Econometrics

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Applied Econometrics

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