Time series analysis. - One Line Questions

1. What is 'white noise' in the context of time series residuals? — A series with a constant mean, constant variance, and no autocorrelation
2. Vector Autoregression (VAR) models are used to model: — The interdependencies among multiple time series
3. In the Box-Jenkins methodology, the identification stage typically involves examining: — ACF and PACF plots
4. A time series that is not stationary can often be made stationary by: — Differencing
5. Autocorrelation refers to the correlation of a time series with: — Its own past values
6. The Dickey-Fuller test is a statistical test used to check for: — Stationarity (specifically, the presence of a unit root)
7. In a SARIMA model, (P,D,Q) represent the orders of the seasonal: — Autoregressive, Differencing, and Moving Average components
8. The Box-Jenkins methodology is a systematic approach for: — Identifying, estimating, and checking ARIMA models
9. What is a common issue when forecasting far into the future using ARIMA models? — Forecast accuracy tends to decrease
10. Which of the following is a common application of GARCH models in econometrics? — Modeling asset price volatility
11. A key indicator of a spurious regression is: — High R-squared and significant t-statistics, despite non-significant Durbin-Watson statistic
12. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models extend ARCH models by: — Including MA terms for the variance
13. The 'I' in ARIMA stands for: — Integrated
14. Which component of a time series represents fluctuations that are not due to trend, seasonality, or cyclical patterns? — Irregular component
15. A Moving Average (MA) model relates the current value of a variable to: — Past error terms
16. In a VAR(p) model, each variable is modeled as a linear function of: — The past values of all variables in the system
17. What does it mean for a time series to be stationary? — Its statistical properties (mean, variance, autocorrelation) do not change over time
18. If a time series has a unit root, it is likely to be: — Explosive or trending
19. Which model is a simple model where the current value of a variable is a function of its past values and a random error term? — Autoregressive (AR) model
20. The additive model for time series decomposition assumes that the components are: — Added together
21. The Augmented Dickey-Fuller (ADF) test is an extension of the Dickey-Fuller test that accounts for: — Autocorrelation in the error terms
22. If two non-stationary time series are cointegrated, their linear combination is: — Stationary
23. Granger causality tests whether: — Past values of one time series help predict another time series
24. Seasonal ARIMA (SARIMA) models are used to model time series that exhibit: — Both regular (non-seasonal) and seasonal patterns
25. An AR(p) model includes lags up to order: — p
26. The order of the MA component in a time series model is denoted by: — q
27. Which criterion is commonly used to select the order of AR and MA components in an ARMA/ARIMA model? — Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC)
28. Which method is commonly used to smooth out short-term fluctuations and highlight long-term trends in a time series? — Moving averages
29. The difference between a time series and its lagged value (Y_t - Y_{t-1}) is known as: — First difference
30. A long-term increase or decrease in a time series is referred to as: — Trend
31. Fluctuations in a time series that occur over periods longer than a year, often associated with economic booms and busts, are called: — Cyclical component
32. When dealing with non-stationary time series that are cointegrated, the appropriate model is often a: — Error Correction Model (ECM)
33. The concept of cointegration is relevant when dealing with: — Multiple non-stationary time series that share a long-run equilibrium relationship
34. A unit root in a time series indicates that the series is: — Non-stationary
35. A time series where the variance changes over time is called: — Heteroscedastic
36. In a multiplicative time series model, the observed value is: — Product of components
37. When calculating a centered moving average for an even order (e.g., 4-period moving average), what additional step is usually required? — Calculate a 2-period moving average of the moving average
38. A SARIMA model is often denoted as ARIMA(p,d,q)(P,D,Q)_s, where 's' represents: — The length of the seasonal cycle
39. After estimating an ARIMA model, what is checked to ensure the model is adequate? — The residuals should resemble white noise
40. The Partial Autocorrelation Function (PACF) measures the correlation between a time series and its lag after removing the effect of: — The correlations at shorter lags
41. ARCH (Autoregressive Conditional Heteroskedasticity) models are used to capture: — Time-varying variance
42. What is the primary goal of time series analysis in econometrics? — To forecast future values of a variable
43. Which of the following is NOT a typical component of a time series? — Cross-sectional variation
44. Regular, predictable patterns that repeat over a fixed period (e.g., daily, weekly, yearly) in a time series are known as: — Seasonality
45. The Autocorrelation Function (ACF) measures the correlation between a time series and its lags. — True
46. An ARIMA(p, d, q) model implies that the d-th difference of the series is stationary and can be modeled as an ARMA(p, q) process. — True
47. Forecasting with ARIMA models involves extrapolating the identified patterns into the future. — True
48. A simple moving average of order 'k' for a time series at time 't' is the average of the observations from time 't-k+1' to 't'. — True
49. The concept of spurious regression arises when: — Two non-stationary variables with no long-run relationship are regressed on each other
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