Summary: Time Series Lecture Slides

Read the summary and the most important questions on Time Series Lecture slides

• 1 Basic properties of Time Series

• 1.1 Basic Definitions

  This is a preview. There are 2 more flashcards available for chapter 1.1
  Show more cards here

• What is the definition of a Time-series model (for observed data {xt})?

  Its a specification of the joint distributions of a sequence of random variables {Xt} ordered in time.

• What is the specific difference between a Time series, and a Time Series Model?

  I would say that because the definition of the model contains the joint distribution, its probably when you fuse the probability distributions together into one probability distribution for the entire series (? Double check)

• 1.2 IID Sequences

  This is a preview. There are 2 more flashcards available for chapter 1.2
  Show more cards here

• What do identical distributions (the id in iid), mean for the probability of extreme event?

  Because with Identical distributions the mean, variance, and other moments do not change over time, it automatically means that the probability of extreme events do not change over time.

• 1.4.2 Stationarity

• In layman's terms: what is the definition of  weak stationarity?

  WS = mean, variance, and covariance are time-invariant. 
  if 2 covariances are different it can only be due to the relative difference between the variables:
  (cov(day3, day5) -> diff of 2
  vs. 
  Cov(day4, day7) -> diff of 3

• 1.4.2.1 Properties of Stationary Process

  This is a preview. There are 8 more flashcards available for chapter 1.4.2.1
  Show more cards here

• True or falseEvery weakly stationary process has well-defined mean µX, variance σ 2 X and covariance function γX(h).

  True (see slide 39)

• 1.5.1 Conditional vs Unconditional

  This is a preview. There are 4 more flashcards available for chapter 1.5.1
  Show more cards here

• Context: slide 38: Conditional moments with IID DataWhat is the difference between IID and stationary data with respect to conditional and unconditional moments?

  
  With IID: unconditional and conditional moments are time invariant
  With stationarity: only unconditional moments are time invariant...conditional moments can change

• 1.8.1 White Noise

  This is a preview. There are 1 more flashcards available for chapter 1.8.1
  Show more cards here

• What does White noise say about higher-order moments?

  It is silent about that

• What does the autocovariance function equal to for a white noise process for- h = 0- h > 0  ?

  For h = 0, the autocovariance is equal to the variance (which is constant)
  For h > 0, the autocovariance is equal to 0

• 1.9 Sources of Non-Stationary

  This is a preview. There are 3 more flashcards available for chapter 1.9
  Show more cards here

• What are the 2 sources of non-stationarity? And how do they differ?

  2 sources:
  1. Stochastic trends
  2. Deterministic trends
  
  How do they differ?
  Dont know, have to find out.

• What are the 2 sources of non-stationarity?

  1. Stochastic trends
  2. Deterministic trends