Time Series Analysis for Economics: Master the Fundamentals

Question 1

How does autocorrelation affect the accuracy of a forecast?

Accepted Answer

Autocorrelation can increase the forecast standard error, making the forecast appear more precise than it actually is. It can also bias the forecast, leading to systematic over- or under-forecasting.

Answer

Autocorrelation can decrease the forecast standard error but bias the forecast.

Answer

Autocorrelation can increase the forecast standard error but not bias the forecast.

Answer

Autocorrelation has no effect on the forecast standard error or bias.

Question 2

How does non-stationarity affect time series analysis, especially the accuracy of forecasting models?

Accepted Answer

Non-stationarity can result in unreliable forecasts due to time-varying patterns or seasonality.

Answer

Non-stationarity does not enhance forecasting accuracy.

Answer

Non-stationarity is not beneficial for time series analysis as it hinders the identification of long-term trends.

Question 3

How can you visually identify autocorrelation in a time series plot? Explain the limitations of visual inspection for detecting autocorrelation.

Accepted Answer

Repeating patterns, clustering, or a lack of randomness in the data.

Answer

Outliers or extreme values in the data.

Answer

A steady increase or decrease in the data over time.

Answer

Visual inspection alone can reliably detect autocorrelation in all time series datasets.

Question 4

What is the primary purpose of the autocorrelation function (ACF)?

Accepted Answer

To measure the correlation between observations at different time lags

Answer

To forecast future values of a time series

Answer

To detect the presence of seasonality

Answer

To remove noise from the data

Question 5

What is the primary purpose of cross-correlation in time series analysis?

Accepted Answer

To assess the relationship between two or more time series

Answer

To forecast future values of a time series

Answer

To detect the presence of autocorrelation

Answer

To eliminate noise from the data

Question 6

What is the MAIN purpose of the autocorrelation function (ACF)?

Accepted Answer

To measure the correlation between observations at different time lags

Answer

To identify the optimal forecast horizon

Answer

To detect seasonality in the data

Answer

To estimate the parameters of a time series model

Question 7

What is the defining characteristic that distinguishes a white noise process from a stationary process?

Accepted Answer

No autocorrelation

Answer

Constant mean

Answer

Constant variance

Answer

Gaussian distribution

Question 8

Which characteristic defines a stationary time series?

Accepted Answer

Constant mean and variance over time

Answer

Time-varying mean and variance

Answer

Periodic fluctuations in the data

Answer

Random and unpredictable behavior

Question 9

To quantify the correlation between observations at different time lags in a time series, which measure is used?

Accepted Answer

Autocorrelation function

Answer

Partial autocorrelation function

Answer

Cross-correlation function

Answer

Covariance matrix

Question 10

Which forecasting method utilizes past values of a time series to predict future values?

Accepted Answer

Autoregressive integrated moving average (ARIMA) model

Answer

Simple exponential smoothing

Answer

Linear regression

Answer

Neural network

Question 11

What is the primary purpose of differencing in time series analysis?

Accepted Answer

Achieving stationarity in time series

Answer

Seasonality removal from data

Answer

Improving forecast accuracy

Answer

Outlier identification in data

Question 12

The Akaike Information Criterion (AIC) is used in time series analysis to:

Accepted Answer

Select the best-fitting ARIMA model

Answer

Test statistical significance of a model

Answer

Forecast future values of a time series

Answer

Identify outliers in data

Question 13

Which of the following is NOT a specific type of time series model?

Accepted Answer

Linear Regression

Answer

ARMA

Answer

GARCH

Question 14

Which of the following is NOT a characteristic of a stationary time series?

Accepted Answer

The mean changes over time

Answer

The time series is serially correlated

Answer

The autocorrelation function is constant

Answer

The variance is constant

Question 15

The autocorrelation function measures the correlation between a time series and:

Accepted Answer

Itself at different time lags

Answer

A random variable

Answer

Its first difference

Answer

Its moving average

Question 16

Which of the following forecasting methods is appropriate for time series that exhibit strong seasonality?

Accepted Answer

Seasonal Autoregressive Integrated Moving Average (SARIMA)

Answer

Moving average

Answer

Exponential smoothing

Answer

Linear regression

Question 17

The differencing operator (d) in an ARIMA model removes:

Accepted Answer

Non-stationarity

Answer

Heteroscedasticity

Answer

Seasonality

Answer

Autocorrelation

Question 18

Which of the following is NOT an assumption of the classical linear regression model?

Accepted Answer

The errors are serially correlated

Answer

The errors are normally distributed

Answer

The errors have a constant variance

Answer

The regressors are exogenous

Question 19

Which of the following is a potential cause of non-stationarity in economic time series?

Accepted Answer

Technological change

Answer

Heteroskedasticity

Answer

White noise

Question 20

The Akaike Information Criterion (AIC) is used to:

Accepted Answer

Select the best model among a set of candidate models

Answer

Estimate the parameters of a time series model

Answer

Test the significance of autocorrelation

Question 21

Which of the following is a benefit of using time series analysis in economics?

Accepted Answer

Improved forecasting accuracy

Answer

Simplified model specification

Answer

Elimination of all uncertainty

Answer

Reduced data requirements

Question 22

The Yule-Walker equations are used to:

Accepted Answer

Estimate the parameters of an autoregressive (AR) model

Answer

Test for stationarity

Answer

Forecast future values of a time series