1. An Ideal ACF plot will increase exponentially ?
View Answer
False
2.The Partial Auto Correlation Function is useful in detecting the Order of AR process.
View Answer
True
3. Non Stationary Time series will have a declining effect ?
View Answer
False
4. What is the process followed to make a specific metric unitless known as ?
View Answer
Normalization
5. _______________________ is said to occur when the variance of the unobservable error , conditional on independent variables, is not Constant.
View Answer
Heteroskedasticity
6. You take a time series and determine the characteristic equation. You find the roots and conclude that they lie within the unit circle. What can you say about the Time Series ?
View Answer
It is Stationary
7. What is the term used to quantify the relationship between the current value and previous values in a time series known as ?
View Answer
Auto Correlation Function
8. What is the property of White Noise in an Auto Regression Equation ?
View Answer
It has zero mean and unit standard deviation.
9. What is the outcome of Model Fitting process for Auto Regression ?
View Answer
Determining the Coefficient.
9. The Auto Regression model can be represented as a Moving Average infinity model.
View Answer
True.
10. ARIMA (0,0,1) is equivalent to _____________.
View Answer
MA Model.
11. Time series is a linear combination of white noise process. This is a representation of _____________.
View Answer
Moving Average Model.
12. What is the mechanism used to choose optimal p and q for an ARMA model ?
View Answer
Residual Sum of Squares.
13. ARIMA (1,0,0) is equivalent to _____________
View Answer
AR Model.
14. If the ACF follows a geometric decay and the PACF is significant till lag (p) what process does the time series follow ?
View Answer
AR(p).
15. How will you make a non-stationary time series to stationary ?
View Answer
Taking Difference between time Series and its Lag.
16. AR , MA and ARMA models can handle non-stationary time series data ?
View Answer
False
17. For a moving average model , the expectation of the dependent variable is ______________ .
View Answer
Constant
18. If there is no decay in the ACF values for any number of lags , what can you say about the time series .
View Answer
Non stationary
19. Structural Models have a time component.
View Answer
False
20. How will you make a non-stationary time series to stationary ?
View Answer
Taking Difference between time Series and its Lag
21. Time series is a linear combination of white noise process. This is a representation of _____________
View Answer
Moving Average Model
22. ARIMA (1,0,1) is equivalent to _____________
View Answer
ARMA Model
23. What are serially uncorrelated vectors which have variance between 0 and a finite value ?
View Answer
white noise innovations
24. My time series model is predicting well for the available data but not predicting accurately for new data. What problem might I have encountered ?
View Answer
OverFitting
25. In an ARMA(p,q) series , what do p and q represent ?
View Answer
Lag Terms
26. ___________________________ is a multivariate generalization of a uni-variate auto regressive time series model.
View Answer
Vector Auto Regression
27. In exponential smoothing , the weights assigned to lag values should __________________ over time.
View Answer
Decline
28. In a time series , the rate of decay will decide the value of the coefficient terms.
View Answer
True
29. A model that is efficient and simple is known as ?
View Answer
Parsimonious Model
30. A model that is efficient and simple is known as ?
View Answer
Parsimonious Model
31. ARIMA (0,1,0) is equivalent to _____
View Answer
Random Walk Model
32. What is the range of smoothing constant alpha ?
View Answer
0 to 1
33. What is the process followed to make a specific metric unitless known as ?
View Answer
Normalization
34. What can we say about the time series when the inverse of the lag function converges to zero ?
View Answer
It is Stationary
35. Partial Auto Correlation is also known as _____________
View Answer
Conditional coreelation
36. What do you get when you divide Auto Covariace of a Time Series by the Variance value ?
View Answer
Auto Correlation Function
37. In vector auto regression , the estimation by ordinary least squares is equivalent to generalized least squares.
View Answer
True
38. Exponential smoothing models can be considered as ARIMA models ?
View Answer
TRUE
39. The Auto Correlation Function is Unitless.
View Answer
TRUE
40. ACF and PACF for Time Series
View Answer
Continue
41. The coefficient for the residual error terms can be negative for a time series.
View Answer
True
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