ECONOMETRIA II
1 Preface
- 1.1 Welcome
- 1.2 Context
I Overview
2 What is Econometrics
3 This Course
II Logistic Regression
4 Introduction
5 Binary Logistic Regression
6 Applications
7 Inference
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
13 Examples
14 Descriptive Analysis
15 Deterministic Models
16 Smoothing Methods
- 16.1 Moving-average models
  - 16.1.1 Non-centered moving averages
- 16.2 Exponential smoothing models
17 Stationary Stochastic Processes
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
20 ARMA Identification
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
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

19 ARMA Models

A model for a stochastic process is any set of well-defined assumptions about the statistical properties of that process.