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o determine an appropriate time-series model to analyze a given investment problem, and justify that choice. Flashcards Preview
L2 13 Time-Series Analysis
> o determine an appropriate time-series model to analyze a given investment problem, and justify that choice. > Flashcards
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o determine an appropriate time-series model to analyze a given investment problem, and justify that choice.
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L2 13 Time-Series Analysis
Class (15):
A Calculate And Evaluate The Predicted Trend Value For A Time Series, Modeled As Either A Linear Trend Or A Log Linear Trend, Given The Estimated Trend Coefficients;
B Describe Factors That Determine Whether A Linear Or A Log Linear Trend Should Be Used With A Particular Time Series, And Evaluate Limitations Of Trend Models;
C Explain The Requirement For A Time Series To Be Covariance Stationary, And Describe The Significance Of A Series That Is Not Stationary;
D Describe The Structure Of An Autoregressive (Ar) Model Of Order P, And Calculate One And Two Period Ahead Forecasts Given The Estimated Coefficients;
E Explain How Autocorrelations Of The Residuals Can Be Used To Test Whether The Autoregressive Model Fits The Time Series;
F Explain Mean Reversion, And Calculate A Mean Reverting Level;
G Contrast In Sample And Out Of Sample Forecasts, And Compare The Forecasting Accuracy Of Different Time Series Models Based On The Root Mean Squared Error Criterion;
H Explain The Instability Of Coefficients Of Time Series Models;
I Describe Characteristics Of Random Walk Processes, And Contrast Them To Covariance Stationary Processes;
J Describe Implications Of Unit Roots For Time Series Analysis, Explain When Unit Roots Are Likely To Occur And How To Test For Them, And Demonstrate How A Time Series With A Unit Root Can Be Transformed So It Can Be Analyzed With An Ar Model;
K Describe The Steps Of The Unit Root Test For Nonstationarity, And Explain The Relation Of The Test To Autoregressive Time Series Models;
L Explain How To Test And Correct For Seasonality In A Time Series Model, And Calculate And Interpret A Forecasted Value Using An Ar Model With A Seasonal Lag;
M Explain Autoregressive Conditional Heteroskedasticity (Arch), And Describe How Arch Models Can Be Applied To Predict The Variance Of A Time Series;
N Explain How Time Series Variables Should Be Analyzed For Nonstationarity And/Or Cointegration Before Use In A Linear Regression;
O Determine An Appropriate Time Series Model To Analyze A Given Investment Problem, And Justify That Choice.