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Lecture
Model Selection in Time Series Analysis
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Related lectures (33)
Time Series: Common Models
Covers common time series models, trend removal, and seasonality adjustment techniques.
Time Series: Stochastic Properties and Modelling
Explores the stochastic properties and modelling of time series, covering autocovariance, stationarity, spectral density, estimation, forecasting, ARCH models, and multivariate modelling.
Box-Jenkins Methodology: Building Time Series Models
Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.
Univariate time series: Analysis & Modeling
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Covers the analysis and modeling of univariate time series, focusing on stationarity, ARMA processes, and forecasting.
Time Series Analysis: ARMA Models
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Explores ARMA models in time series analysis, covering model selection, forecasting, and precision assessment.
Multivariate Time Series: Cointegration & Forecasting
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Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.
Count Data Models & Univariate Time Series Analysis
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Covers count data models and Poisson regression, then transitions to univariate time series analysis for forecasting economic variables.
Binary Choice Models and Time Series Analysis
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Explores binary choice models like probit and logit, as well as univariate time series analysis with ARIMA models for forecasting economic variables.
Time Series: Autoregressive Models
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Explores autoregressive models for time series analysis, covering AR(1), AR(2), identification, and MA models.
Time Series Models: Autoregressive Processes
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Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.
Univariate Time Series Analysis
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Explores univariate time series analysis, covering stationarity, ARMA processes, model selection, and unit root tests.
Parametric Signal Models: Matlab Practice
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Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.
Confidence Intervals and T-Test
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Explores confidence intervals, T-test, and hypothesis testing, including assumptions and critical regions.
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