Time Series Analysis Spectral Analysis and Advanced Topics 2

Question 1

What is ARCH (Autoregressive Conditional Heteroscedasticity) in time series modeling?

Accepted Answer

A model where the variance of the error term depends on past squared errors

Answer

ARCH models the time-varying variance by making it a function of past squared residuals, capturing volatility clustering in financial data.

Question 2

What does GARCH add beyond the basic ARCH model?

Accepted Answer

Lagged conditional variance terms in addition to lagged squared errors

Answer

GARCH includes past conditional variances as predictors, allowing the model to capture longer-memory volatility persistence with fewer parameters than high-order ARCH.

Question 3

What is a 'long memory' process in time series analysis?

Accepted Answer

A process where autocorrelations decay slowly (hyperbolically) rather than exponentially

Answer

Long memory (fractional integration) is characterized by hyperbolic rather than geometric autocorrelation decay, often modeled with ARFIMA models.

Question 4

What is the purpose of an Intervention Analysis in time series?

Accepted Answer

To model the effect of known external events (interventions) on a time series

Answer

Intervention analysis explicitly models the impact of known events (policy changes, promotions, disasters) using indicator variables within the ARIMA framework.

Question 5

What is a Transfer Function model?

Accepted Answer

An ARIMA model extended with lagged values of one or more input (predictor) variables

Answer

Transfer function (or dynamic regression) models add filtered versions of external predictors to a noise model, capturing both input-output dynamics and autocorrelated errors.

Time Series Analysis Practice Test

Time Series Analysis Practice Test

Time Series Analysis Spectral Analysis and Advanced Topics 2