ECONOMETRIA II
1
Preface
1.1
Welcome
1.2
Context
I Overview
2
What is Econometrics
3
This Course
3.1
Virtual Campus
3.1.1
Contents
3.1.2
Calendar
3.1.3
Grading
3.2
Bibliography
3.2.1
En la Biblioteca
3.2.2
Links Útiles:
3.2.3
Software
3.3
Gretl
Ingredients
Recommendations
II Logistic Regression
4
Introduction
5
Binary Logistic Regression
5.1
Assumptions for logistic regression:
5.2
Interpretation of Parameter Estimates
5.3
Probabilities
6
Applications
6.1
Coronary Heart Disease (CHD)
6.1.1
Interpreting Model Coefficients
6.2
Car Sales
6.3
Credit Cards
7
Inference
7.1
Dependent Variable
7.2
Odds and Log-Odd
7.3
Parameter Estimation
7.4
Likelihood Ratio
7.5
Significancia de Parámetros
8
Goodness of Fit
8.1
Pseudo
\(R^2\)
8.2
AIC y BIC
9
Classification
9.1
Confusion Matrix
10
Diagnosis
11
En Gretl
III Time Series
12
Introduction
12.1
Objectives
12.2
Applications
Economic Forecasting
Demand Forecasting
Anomaly Detection
13
Examples
14
Descriptive Analysis
14.1
Trend
14.2
Seasonality
14.3
Cyclical
14.4
Residuals
14.5
Examples
15
Deterministic Models
15.1
Time Series Components
15.2
Modeling the trend
15.3
Modeling Seasonality
16
Smoothing Methods
16.1
Moving-average models
16.1.1
Non-centered moving averages
16.2
Exponential smoothing models
17
Stationary Stochastic Processes
17.1
Stochastic processes
17.2
Stationarity
17.3
ACF and PACF
17.4
Operator
\(B\)
17.4.1
Lag operator
17.4.2
Difference operator
17.4.3
Forward operator
17.4.4
Properties of
\(B\)
17.4.5
Equations in Difference
17.4.6
Example:
18
AR(1) Model
18.1
AR(1)
18.1.1
Residual Analysis
18.1.2
Sample Autocorrelation Function (ACF)
18.2
AR(1) properties
18.2.1
Properties of
AR(1)
18.2.2
AR(1)
- ACF
19
ARMA Models
19.1
Ruido Blanco
19.2
Random walk
19.3
AR(p)
19.4
AR(1)
19.5
AR(2)
19.6
MA(q)
19.7
MA(1)
19.8
MA(2)
19.9
ARMA(p,q)
19.9.1
ARMA(1,1)
20
ARMA Identification
20.1
Box-Jenkins Method
20.1.1
Identification
20.1.2
Estimation
20.1.3
Diagnostic Checking
20.2
ACF and PACF for ARMA models
20.2.1
AR y MA
20.2.2
ARMA y ARMA Seasonal
20.3
Examples
20.4
En Gretl
21
Non-Stationary Stochastic Processes
21.0.1
Non-stationarity in Variance
21.0.2
Non-stationarity in Mean
22
ARIMA Models
23
Forecast Error
IV Appendix
24
Students’t Distribution
24.1
Degrees of Freedom (df)
24.2
Area under the curve
24.3
The t-table
24.4
Acceptance/Rejection Region
25
Gretl: Quick Intro
25.1
References
25.1.1
Tutorial 1
25.1.2
Tutorial 2
26
About me
Universidad Antonio de Nebrija.
Publicado with bookdown
Econometrics II | Class Notes
7
Inference
We will use this example to discuss:
how to estimate the
parameters
of a logistic regression,
how to
use
the fitted model and
how to evaluate the quality or
goodness of fit
of the model.
