Concept

Unit root

Related lectures (24)

Univariate time series: Analysis & Modeling

Covers the analysis and modeling of univariate time series, focusing on stationarity, ARMA processes, and forecasting.

Explores univariate time series analysis, covering stationarity, ARMA processes, model selection, and unit root tests.

Count Data Models & Univariate Time Series Analysis

Covers count data models and Poisson regression, then transitions to univariate time series analysis for forecasting economic variables.

Multivariate Time Series: Cointegration & Forecasting

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Parametric Signal Models: Matlab Practice

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Time Series Models: Autoregressive Processes

Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.

Forecasting & Long Memory: Time Series

Explores forecasting methods and long memory in time series analysis.

Long Memory and ARCH: Time Series

Explores long memory in time series and ARCH models for financial volatility.

Time Series: Structural Modelling and Kalman Filter

Covers structural modelling, Kalman Filter, stationarity, estimation methods, forecasting, and ARCH models in time series.

Integrated and Seasonal Processes: Time Series

Explores parametric estimation, integrated processes, seasonal modeling, and ARIMA model building in time series analysis.

Long Memory and ARCH: Time Series Math 342

Explores long memory in time series and Autoregressive Conditional Heteroskedasticity processes in financial data.

Spectral Analysis: Time Series

Explores spectral analysis in time series, focusing on spectral density functions and integrated spectra.

Signal Models and Methods: Parametric vs Nonparametric

Provides an overview of signal models and methods in statistical signal processing.

Linear Estimation and Prediction: Part 2

Covers the estimation and prediction of random signals in linear systems.

Model Choice and Prediction

Explores model choice, prediction, and forecasting techniques in time series analysis.

Time Series: Linear Filtering and Spectral Estimation

Explores linear filtering, spectral estimation, and second-order stationarity in time series analysis.

Vector Autoregression

Explores Vector Autoregression for modeling vector-valued time series, covering stability, Yule-Walker equations, and spectral representation.

Extreme Value Time Series: Modelling and Dependence

Explores extremal limit theorems, point processes, and multivariate extremes in extreme value time series modelling, emphasizing the effect of local dependence on extreme values.

Spectral Analysis: Integrated Spectrum and Autocovariance

Explores spectral analysis, integrated spectrum, autocovariance, estimation, and convergence in time series models.

Time Series: Autoregressive Models

Explores autoregressive models for time series analysis, covering AR(1), AR(2), identification, and MA models.