Econometrics A Z Hands On With Advance Econometric Analysis

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Tags: Econometrics

Hypothesis Testing, Regression, Correlation, Eviews, Predictive and Econometric Modeling, and Descriptive Statistics

Last updated 2022-01-10 | 4.5

- The linear and non-linear regression models
- Time series data
- models and autocorrelation
- EViews for econometrics

What you'll learn

The linear and non-linear regression models
Time series data
models and autocorrelation
EViews for econometrics
The regression model with several explanatory variable
Probability theory
variables and distributions
Endogeneity and instrumental variables
Binary choice models
Non-stationary time series models
cointegration
Panel data(pooled OLS
random effects and fixed effects models)
Hypothesis testing
Moments of different types of variables
Distributions (Chi-square
T-distribution and F-distribution)
Multicollinearity and forecasting
Heteroscedasticity
OLS

* Requirements

* MS Office desired

Description

  • The linear and non-linear regression models
  • Time series data, models and autocorrelation
  • EViews for econometrics
  • The regression model with several explanatory variable
  • Probability theory, variables and distributions
  • Endogeneity and instrumental variables
  • Binary choice models
  • Non-stationary time series models, cointegration
  • Panel data(pooled OLS, random effects and fixed effects models)
  • Hypothesis testing
  • Moments of different types of variables
  • Distributions (Chi-square, T-distribution and F-distribution)
  • Multicollinearity and forecasting
  • Heteroscedasticity
  • OLS

Course content

12 sections • 100 lectures

Sample moments and Software 2 lectures

Sample Moments Preview 35:11

Getting started with Eviews Preview 10:29

Least squares principle 8 lectures

The OLS Formula Preview 18:38

Residuals and fitted values Preview 19:34

The least squares principle Preview 17:10

Trendline with no intercept Preview 08:39

Deriving the OLS Formula Preview 23:31

Global minimum of RSS Preview 12:52

Some OLS results Preview 18:21

Measure of fit Preview 20:34

Introduction to probability theory 11 lectures

Random variables Preview 11:24

Distribution functions Preview 17:42

Standard Normal Preview 14:51

Expected value discrete random variable Preview 06:42

Expected value continues random variable Preview 07:08

The variance of a random variable Preview 20:24

The expected value and variance of a linear function Preview 12:27

The normal distribution Preview 15:48

Covariance, correlation and independence Preview 26:34

Conditional expectation and conditional variance Preview 22:07

Sample as a sequence of random variables Preview 11:32

The linear regression model with one explanatory variable 9 lectures

The linear regression model(LRM) Preview 12:34

LRM with an exogenous explanatory variable Preview 21:37

The OLS estimator Preview 37:18

When are the OLS estimators unbiased and consistent Preview 24:51

Homoscedasticity, heteroscedasticity and the GM Preview 11:04

The variance of the OLS estimators Preview 14:02

Estimating sigma2 Preview 16:31

Estimating the variance of the OLS estimators Preview 09:39

The Gauss-Markov theorem Preview 11:15

Inference in the linear regression model with one explanatory variable 7 lectures

The chi-square distribution Preview 16:27

The t-distribution Preview 11:28

The F-distribution Preview 07:47

Critical values Preview 13:21

Introduction to hypothesis testing Preview 26:06

Hypothesis testing in the LRM The t-test Preview 38:19

Confidence intervals in the LRM Preview 14:19

The linear regression model with several explanatory variable 8 lectures

Linear regression with several explanatory variables Preview 25:17

OLS Preview 27:23

The properties of the OLS estimator Preview 17:42

Hypothesis testing, one restriction – the t-test Preview 26:25

Hypothesis testing, several restrictions – the F-test Preview 12:42

Confidence intervals in the LRM Preview 06:35

Multicollinearity Preview 33:45

Forecast in the LRM Preview 15:38

Nonlinear and logarithmic regression model 6 lectures

Linear in parameters and/or linear in data Preview 22:32

Linear regression models which are nonlinear in data Preview 26:28

The log-log model Preview 26:39

The log-linear model Preview 17:02

Logging an x-variable Preview 10:43

Ramsey’s RESET test Preview 12:35

Dummy variables 6 lectures

The LRM with a dummy variable Preview 18:48

Dummy variables handling more than two categories Preview 11:31

Interactive dummy variables Preview 14:18

The Chow test Preview 23:34

Redundant variables Preview 19:32

Missing variables Preview 24:39

Heteroscedasticity 4 lectures

Heteroscedasticity Preview 15:18

Test for heteroscedasticity using squared residuals Preview 22:37

Robust standard errors with heteroscedasticity Preview 05:17

Weighted least squares Preview 15:55

Endogeneity and instrumental variables 12 lectures

Measurement errors Preview 07:37

A simple model of measurement errors in a LRM Preview 18:09

Simultaneous equations Preview 18:42

Simultaneous equation bias Preview 07:35

Endogenous variables Preview 07:07

Instrumental variables, one explanatory variable Preview 20:23

Instrumental variables, several explanatory variables Preview 10:22

Generalized IV Preview 06:57

Hausman test for defective variables Preview 08:24

The linear probability model Preview 26:30

Binary choice models Preview 14:18

Binary choice models, inference Preview 23:53

Time series models 20 lectures

Time series data Preview 10:56

Stationarity Preview 13:26

LRM with time series data – the static model Preview 18:14

The properties of the OLS estimator in the static model Preview 10:23

ADL(p,q) model Preview 21:14

The AR(1) process Preview 32:51

The AR(p) process Preview 05:36

Estimating ADL(p,q) models Preview 06:27

Long run and short run effects in ADL models Preview 15:31

The partial adjustment model Preview 08:41

The error correction model Preview 11:22

Autocorrelation Preview 07:35

Test for autocorrelation, Breusch-Godfrey test Preview 18:49

Robust standard errors with autocorrelation Preview 04:32

Efficient estimation with AR(1) errors Preview 14:26

The problem with nonstationary data Preview 13:27

Nonstationary models Preview 16:24

Test for unit root Preview 20:47

Cointegration Preview 19:29

Panel data Preview 09:23

Models based on panel data 7 lectures

Pooled OLS Preview 05:24

Error component model Preview 13:30

Random effects model Preview 15:53

Fixed effects model Preview 12:17

Random effects versus fixed effects Preview 07:36

The Hausman test for random effects Preview 06:37

Final Quiz