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Advanced Time Series Analysis MCQ's

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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