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Statistical Methods for Social Sciences: Prevision Techniques
History, Politics & International Studies Program
Madrid, Spain
Dates: 1/19/23 - 6/3/23
Statistical Methods for Social Sciences: Prevision Techniques
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Other Subject Area: International Relations
Instruction in: English
Course Code: 16630
Transcript Source: Partner Institution
Course Details: Level 200
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Previous courses on Statistics and Econometrics.
DESCRIPTION
Chapter 1. TIME SERIES ECONOMETRICS. PROPERTIES AND STATISTICAL CONTEXT
1.1 Quantitative methods and socioeconomic analysis.
1.2 Random samples and time series characteristics. Evolution of the level and stationary oscillations.
1.3 Time series decomposition
1.3.1 Classical decomposition: trend, seasonality and short term disturbances.
1.3.2. Time series decomposition and econometric modelling
1.4 Trend and seasonality in time series. Transformations of stationarity.
1.4.1 The model of linear trend and deterministic seasonality
1.4.2 Trend segmentation.
1.4.3 Stochastic seasonality and trends
Chapter 2. UNIVARIATE LINEAR MODELS
2.1 Stationary stochastic processes. Univariate models: autocorrelation function and correlogram
2.2 White noise process
2.3 First-order Autoregressive model AR (1)
2.4 Generalization to the AR (p)
2.5 Integrated models: ARI (1, p)
2.6 ARMA and ARIMA models
Chapter 3 SPECIFICATION, ESTIMATION AND VALIDATION OF ARIMA MODELS
3.1 The Box-Jenkins Methodology
3.2 Initial Specification
3.2.1 Unit root test
3.2.2 Information criteria for temporal dependence
3.2.3 Seasonal unit root test
3.3 Estimation
3.4 Validation of ARIMA models
3.4.1 Residual Analysis
3.4.2 Alternative models
Chapter 4 STATIONARY MULTIVARIATE MODELS
4.1. Stationary VAR(p) Model. Specification. Temporal Dependence.
4.2. Granger Causality. Contemporaneous Dependence
4.3. VAR model estimation
4.4. VAR model with exogenous variables. Recursive VAR models
4.5. Uniequational Dynamic Models. Autoregressive Distributed Lag models (ADL).
4.6. Impact and long run multipliers
1.1 Quantitative methods and socioeconomic analysis.
1.2 Random samples and time series characteristics. Evolution of the level and stationary oscillations.
1.3 Time series decomposition
1.3.1 Classical decomposition: trend, seasonality and short term disturbances.
1.3.2. Time series decomposition and econometric modelling
1.4 Trend and seasonality in time series. Transformations of stationarity.
1.4.1 The model of linear trend and deterministic seasonality
1.4.2 Trend segmentation.
1.4.3 Stochastic seasonality and trends
Chapter 2. UNIVARIATE LINEAR MODELS
2.1 Stationary stochastic processes. Univariate models: autocorrelation function and correlogram
2.2 White noise process
2.3 First-order Autoregressive model AR (1)
2.4 Generalization to the AR (p)
2.5 Integrated models: ARI (1, p)
2.6 ARMA and ARIMA models
Chapter 3 SPECIFICATION, ESTIMATION AND VALIDATION OF ARIMA MODELS
3.1 The Box-Jenkins Methodology
3.2 Initial Specification
3.2.1 Unit root test
3.2.2 Information criteria for temporal dependence
3.2.3 Seasonal unit root test
3.3 Estimation
3.4 Validation of ARIMA models
3.4.1 Residual Analysis
3.4.2 Alternative models
Chapter 4 STATIONARY MULTIVARIATE MODELS
4.1. Stationary VAR(p) Model. Specification. Temporal Dependence.
4.2. Granger Causality. Contemporaneous Dependence
4.3. VAR model estimation
4.4. VAR model with exogenous variables. Recursive VAR models
4.5. Uniequational Dynamic Models. Autoregressive Distributed Lag models (ADL).
4.6. Impact and long run multipliers