Times-series Analysis (2024 Level II CFA® Exam -Quantitative Methods-Module 5)

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Topic 1 - Quantitative Methods
Module 4 - Times-series Analysis
0:00 Introduction and Learning Outcome Statements
1:24 LOS: 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
5:45 LOS: 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
7:24 LOS: Explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary
8:45 LOS: Describe the structure of an autoregressive (AR) model of order p and calculate one- and two period-ahead forecasts given the estimated coefficients
14:07 LOS: Explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series
18:58 LOS: Explain mean reversion and calculate a mean-reverting level
21:06 LOS: 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
25:01 LOS: Explain the instability of coefficients of time-series models
27:30 LOS: Describe characteristics of random walk processes and contrast them to covariance stationary processes.
31:24 LOS: 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
33:25 LOS: Describe the steps of the unit root test for non-stationary and explain the relation of the test to autoregressive time-series models
36:49 LOS: 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
42:35 LOS: Explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series
46:59 LOS: Explain how time-series variables should be analyzed for nonstationary and/or cointegration before use in linear regression
53:27 LOS: Determine an appropriate time-series model to analyze a given investment problem and justify that choice

[@caroljynx6776]

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[@kevinnguyen7093]

@@analystprep 😊

[@ayushdarda9963]

So grateful to this channel. Extremely helpful and the way concepts are explained is amazing. Thank you james sir

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[@ValueInvestments]

Great Video Professor, make it super easy to understand.

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[@LL-fl3pz]

in Dickey-fuller test, step 5, if g1 is not significantly different from 0, then b1=1, not b1=0?

[@brandonfronczak1008]

One over the square root of observations not time

[@MrTheVakman]

In this case time is the number of observation

[@thehardlife5588]

This formula for durbin watson statistic is different from the one i have seen before 16:16

[@user-oi8zk9vs4e]

you cannot use DW for serial correlation in time series, that is not DW

[@thehardlife5588]

@@user-oi8zk9vs4e oh right, thanks