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Econometrics 1

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Econometrics 1. Lecture 10 Autocorrelation-2. Remedial Measure when is Known. Remedy for Autocorrelation: GLS Estimator. Variance and Covariance of Errors in Case of Autocorrelation Compared to the Variance of a Normal Error Term. Cochrane-Orcutt iterative procedure.

Econometrics 1

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Econometrics 1 Lecture 10 Autocorrelation-2
Remedial Measure when is Known
Remedy for Autocorrelation: GLS Estimator
Variance and Covariance of Errors in Case of Autocorrelation Compared to the Variance of a Normal Error Term
Cochrane-Orcutt iterative procedure

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Econometrics 1. Lecture 15 Simultaneous Equation Models – Reduced Form and Simultaneity Bias. Main Feature of Simultaneous Equation System. Main Feature of Simultaneous Equation System-2. Simultaneity-an Example. Example of an Identified Model. Example of an Identified Model.

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Econometrics 1. Lecture 11 Stationarity and AR MA and ARMA Processes. Stationary and Nonstationary Series. Spurious Regression. Four ways of checking stationarity. Partial autocorrelation function and Ljung and Box statistics. Unit Root and Non-stationarity. Time Dependent Variance.

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Econometrics 1. Lecture 5 Multiple Regression -derivation. Multiple Regression Analysis. Errors Terms in a Multiple Regression Equation. Derivation of Normal Equations in a Multiple Regression Analysis. Main Issue in the Multiple Regression Analysis. Algebraic Method.

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Econometrics 1. Lecture 22 Dummy Dependent Variables Models. Dummy Variables. There are mainly three roles for dummy variables as a reggressor. Use of Dummy in House Price Model. Impact of Location in House Price. Slope Dummy. Applications and Dummy Variable Trap. Chow Test.

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Econometrics 1. Lecture 23 Article and Report Writing Using Econometrics. Report or Essay Writing . Introduction to the Issue Specification and justification of the Model Mathematical derivations and proof of key equations Assessment of properties of the Key parameters of the Model

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Econometrics 1. Lecture 22 Dummy Dependent Variables Models. Probability models/Dummy dependent variables. Four types of Probability models/Dummy dependent variable Models. Linear Probability Model. Limitations of a linear probability model. Probit model. Steps for a probit model.

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Econometrics 1. Lecture 21 Polynomial Distributed (Dynamic) Models. Nature of Dynamic Lag Models. Distributed Lag Model:Koyck Procedure. Koyck Trasformation Procedure. Choice of Length of Lag in Koyck Model. Problems with Koyck Model. Almon’s polynomial lag model.

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Econometrics 1. Lecture 17 Pooling Cross Section and Time Series. Need For Pooling Time Series and Cross Section data. Two Methods of combining Time Series and Cross Section: Panel Model and Pooling Regression. A Glimpse of Panel Method.

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Econometrics 1. Lecture 8 Heteroscedasticity. Causes of Heteroskedasticity. Nature and Causes. Detection of Heteroscedasticity: Informal (Graphical) method. Consequence of Heteroscedasticity. Consequence of Heteroscedasticity. Tests for Heteroscedasticity. Tests for Heteroscedasticity.

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Econometrics 1. Lecture 16 Simultaneous Equation Models – Identification and Estimation. Recursive triangular system. Rank and Order Conditions of Identification. Order and rank conditions of identification. Steps for rank condition. Macroeconometric Model. Reduced form of the Macro Model.

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Econometrics 1. Lecture 20 Random Regressor and Large Sample Theory. Random Regressor Models. Random Regressor Model. Reasons for Random Regressor. Simultaneity and Random Regressor. Measurement Error. Measurement Error. Hausemen Test. Hausman specification test Shazam Programme.

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Econometrics 1. Lecture 18 Restrictions in Multiple Regression. F-test for Restrictions in Multiple Regression. Estimates of the Model. Variance of Error and the Parameters. Test of Restrictions. Shazam Program to Test of Restrictions - from HGJ 10.2.

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Econometrics 1. Lecture 6 Multiple Regression -tests. Testing Restrictions in a Multiple Regression Model. F-test in the Aggregate Demand Model. Prediction and Forecasts. Variance of the Forecast Error. Decomposition of the Forecast Error. Forecasting and Decomposition of Forecast Variance.

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