Time Series
Preliminary remarks
1
Introduction
1.1
Exploratory Data Analysis
1.1.1
Libraries
1.1.2
Loading datasets
1.1.3
Time series objects and basic plots
1.2
Introduction to the basic time series functions
1.2.1
Exercise 1: Beaver temperature
1.3
Second order stationarity
1.3.1
Exercise 2: SP500 daily returns
1.4
Simulations
1.4.1
Exercise 3: Simulated data
1.5
Spectral analysis
1.6
Smoothing and detrending
1.6.1
Exercise 4: Mauna Loa Atmospheric CO
2
Concentration
1.7
Solutions to Exercises
1.7.1
Solutions 1: Beaver temperature
1.7.2
Solutions 2: SP500 daily returns
1.7.3
Solutions 3: Simulated data
1.7.4
Solutions 4: Mauna Loa Atmospheric CO
2
Concentration
2
Likelihood estimation and the Box–Jenkins method
2.1
Manual maximum likelihood estimation
2.1.1
Exercise 1: UBS stock returns
2.2
Box–Jenkins methodology for ARMA models
2.2.1
Exercise 2: Simulated series
2.3
Information criteria, model selection and profile likelihood
2.3.1
Exercise 3: Lake Erie height
2.4
Solutions to Exercises
2.4.1
Exercise 1: UBS stock returns
2.4.2
Exercise 2: Simulated series
2.4.3
Exercise 3: Lake Erie height
3
Seasonal ARIMA and GARCH models
3.1
SARIMA models: estimation and forecasting
3.2
An aside on models with regressors (optional)
3.2.1
Mauna Loa CO
2
dataset
3.2.2
Exercice 1: Nottingham average monthly temperature and Hong Kong monthly exports
3.3
Boostrap methods for time series
3.3.1
Bootstrapping a linear model
3.3.2
Testing for heteroscedasticity
3.3.3
AR-sieve bootstrap
3.3.4
Boostrap models for uncertainty assessment
3.3.5
Exercice 2: Lake Erie and Lake Huron levels
3.4
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and extensions
3.4.1
Predictions
3.4.2
Exercice 3: International Business Machines (IBM) stock
3.5
Solutions to Exercises
3.5.1
Exercice 1: Nottingham average monthly temperature and Hong Kong monthly exports
3.5.2
Exercice 2: Lake Erie and Lake Huron levels
3.5.3
Exercice 3: International Business Machines (IBM) stock
4
Spectral analysis and filtering
4.1
Nonparametric spectral estimation
4.1.1
Tapering
4.1.2
A data example
4.1.3
Smoothing
4.2
Summary of nonparametric spectral estimation
4.3
Spectral estimation in R
4.3.1
Smoothing and seasonally adjusted values
4.3.2
Exercise 1: Southern oscillation index (SOI) and fish recruitement
5
Covariates and dynamic linear models
5.1
Simulation-based prediction intervals for ARIMA-GARCH models
5.2
State-space models and the Kalman filter
5.2.1
Exercise 1: Dynamic linear model for the Nile river dataset
6
Notes on irregular time series and missing values"
6.1
Irregular time series
6.1.1
Exercise 1: Jussy air temperature
6.2
Imputation of missing values
6.3
Diagnostics for missing values and smoothing
6.3.1
Exercise 2: Tyne river flow
6.4
Solutions to Exercises
6.4.1
Exercise 1: Jussy air temperature
6.4.2
Exercise 2: Tyne river flow
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timeseRies
6
Notes on irregular time series and missing values"