CRADF
C-RAD AB UPPSALA B
stock OTC

I need to choose only one exam between this two:
Time series econometrics
Time series models, non-stationarity
\- Some hypotheses and definitions
Moments of a stochastic process and stationarity concepts
\- Modelling stationary stochastic processes
The White Noise process
Wald decomposition theorem and moving average (MA) representations
Stationary autoregressive (AR) processes
Mixed processes: ARMA models, maximum likelihood (ML) estimation, specification and forecasting
\- Modelling non-stationary stochastic processes
Sources of non-stationarity in first and second moments
Testing for non-stationarity (and stationarity): Augmented Dickey-Fuller, Phillips-Perron and Ng-Perron tests (ADF-PP-NP)
Filtering and modelling non-stationary processes: Hodrick-Prescott (HP) filter and ARIMA modelling
Beveridge-Nelson trend-cycle decomposition (BN) - Hamilton's filter (HTCF)
\- Non-stationarity and cointegration
Spurious relations: properties of estimators under stationarity, near-stationarity and non-stationarity
Concept of co-integration (CI)
Granger representation theorem and error-correction (EC) representation
Engle and Granger's and Autoregressive Distributed Lag (ARDL)-based EC models (ECMs)
(Non) CI tests: Cointegration Rank ADF (CRADF), loading coefficients-based and F-based tests
\- Applications
Multivariate time series modelling
\- The Vector-autoregressive model (VAR)
\- The VAR with exogenous variables (VAR-X) and the Vector-autoregressive Distributed Lag model (VARDL)
\- Identification issues: order and rank conditions for identification
\- Estimation and model evaluation
\- Forecasting and policy simulation
\- Applications
Structural VARs
\- Vector Moving Average (VMA) representation of a VAR
Impulse Response Function (IRF)
Forecast Error Variance Decomposition (FEVD)
Historical Decomposition (HD)
\- Identification strategies for structural VARs
Cholesky decomposition
A-B model
Long-run exclusion restrictions
Set-identification, i.e., sign restrictions
The structural Vector-Error-correction model (SVEC): Common Trends approach to identification
VAR identification and instrumental variables
Proxy (IV)-VARs
Applications
Alternative methods for structural time series modelling
\- Local Projection methods (LP) and VARs
Identification of LPs through IV-GMM
Identification of LPs through controls and exclusion restrictions
Applications
\- Machine Learning methods (ML) for prediction and structural modelling
Basic ML estimators: Ridge, Lasso and Elastic Net
Predictive performances of ML methods in low and high-dimensional settings
Lasso-VARs, forecasting, nowcasting
Causal ML: LP identified through double-Post-Lasso/Elastic Net
Causal ML: LP identified through Double/Debiased ML (DML)
Applications

I need to choose only one exam between this two:
Time series econometrics
Time series models, non-stationarity
\- Some hypotheses and definitions
Moments of a stochastic process and stationarity concepts
\- Modelling stationary stochastic processes
The White Noise process
Wald decomposition theorem and moving average (MA) representations
Stationary autoregressive (AR) processes
Mixed processes: ARMA models, maximum likelihood (ML) estimation, specification and forecasting
\- Modelling non-stationary stochastic processes
Sources of non-stationarity in first and second moments
Testing for non-stationarity (and stationarity): Augmented Dickey-Fuller, Phillips-Perron and Ng-Perron tests (ADF-PP-NP)
Filtering and modelling non-stationary processes: Hodrick-Prescott (HP) filter and ARIMA modelling
Beveridge-Nelson trend-cycle decomposition (BN) - Hamilton's filter (HTCF)
\- Non-stationarity and cointegration
Spurious relations: properties of estimators under stationarity, near-stationarity and non-stationarity
Concept of co-integration (CI)
Granger representation theorem and error-correction (EC) representation
Engle and Granger's and Autoregressive Distributed Lag (ARDL)-based EC models (ECMs)
(Non) CI tests: Cointegration Rank ADF (CRADF), loading coefficients-based and F-based tests
\- Applications
Multivariate time series modelling
\- The Vector-autoregressive model (VAR)
\- The VAR with exogenous variables (VAR-X) and the Vector-autoregressive Distributed Lag model (VARDL)
\- Identification issues: order and rank conditions for identification
\- Estimation and model evaluation
\- Forecasting and policy simulation
\- Applications
Structural VARs
\- Vector Moving Average (VMA) representation of a VAR
Impulse Response Function (IRF)
Forecast Error Variance Decomposition (FEVD)
Historical Decomposition (HD)
\- Identification strategies for structural VARs
Cholesky decomposition
A-B model
Long-run exclusion restrictions
Set-identification, i.e., sign restrictions
The structural Vector-Error-correction model (SVEC): Common Trends approach to identification
VAR identification and instrumental variables
Proxy (IV)-VARs
Applications
Alternative methods for structural time series modelling
\- Local Projection methods (LP) and VARs
Identification of LPs through IV-GMM
Identification of LPs through controls and exclusion restrictions
Applications
\- Machine Learning methods (ML) for prediction and structural modelling
Basic ML estimators: Ridge, Lasso and Elastic Net
Predictive performances of ML methods in low and high-dimensional settings
Lasso-VARs, forecasting, nowcasting
Causal ML: LP identified through double-Post-Lasso/Elastic Net
Causal ML: LP identified through Double/Debiased ML (DML)
Applications