Time Series Analysis for Economics MSc: Master Time Series Forecasting

Question 1

What is the main distinction between AR(1) and MA(1) models?

Accepted Answer

An AR(1) model includes a lagged dependent variable, while an MA(1) model has a lagged error term

Answer

An MA(1) model includes a lagged dependent variable, while an AR(1) model has a lagged error term

Answer

AR(1) models include a constant term, but MA(1) models do not

Answer

There is no difference between AR(1) and MA(1) models

Question 2

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

Accepted Answer

To make a time series stationary

Answer

To remove seasonality

Answer

To forecast future values

Answer

To identify trends

Question 3

The autocorrelation function (ACF) of a time series measures the:

Accepted Answer

Correlation between observations at different time lags

Answer

Volatility of the series over time

Answer

Seasonality of the series

Answer

Stationarity of the series

Question 4

Which forecasting method assumes a linear trend in the data?

Accepted Answer

Holt's linear trend method

Answer

Exponential smoothing

Answer

ARIMA modeling

Answer

Neural networks

Question 5

In time series analysis, the term 'residual' refers to the:

Accepted Answer

Difference between observed and forecasted values

Answer

Standard deviation of the forecast errors

Answer

Correlation between the time series and another variable

Answer

Volatility of the time series

Question 6

The order of an ARIMA model is determined by:

Accepted Answer

Number of AR and MA terms, and degree of differencing

Answer

Number of observations in the time series

Answer

Length of the forecast horizon

Answer

Type of data transformation applied

Question 7

The Akaike Information Criterion (AIC) is utilized for:

Accepted Answer

Selecting the most suitable ARIMA model for a given time series

Answer

Testing the statistical significance of autocorrelation

Answer

Evaluating the precision of a forecast

Answer

Transforming a non-stationary time series

Question 8

The autocorrelation function (ACF) is primarily used to:

Accepted Answer

Quantify the correlation between a time series and its past values

Answer

Estimate the variance of a time series

Answer

Determine the mean of a time series

Answer

Test for the stationarity of a time series

Question 9

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

Accepted Answer

Ordinary Least Squares (OLS)

Answer

Autoregressive Integrated Moving Average (ARIMA)

Answer

Seasonal Autoregressive Integrated Moving Average (SARIMA)

Answer

Generalized Autoregressive Conditional Heteroskedasticity (GARCH)

Question 10

The Box-Jenkins methodology aims to:

Accepted Answer

Identify and estimate an appropriate time series model

Answer

Forecast future values of a time series

Answer

Plot a time series

Answer

Test for the stationarity of a time series

Question 11

The Ljung-Box test is primarily used for:

Accepted Answer

Testing for autocorrelation in a time series

Answer

Testing for stationarity in a time series

Answer

Forecasting future values of a time series

Answer

Transforming a non-stationary time series into a stationary one

Question 12

Which characteristic is NOT a defining feature of a stationary time series?

Accepted Answer

Time-varying autocorrelation

Answer

Constant mean

Answer

Constant variance

Question 13

What is the primary purpose of an autocorrelation function (ACF) within time series analysis?

Accepted Answer

Detecting serial correlation

Answer

Estimating ARIMA model parameters

Answer

Predicting future series values

Answer

Establishing stationarity

Question 14

Explain the distinction between additive and multiplicative seasonal patterns.

Accepted Answer

Additive patterns add a constant, while multiplicative patterns multiply by a constant.

Answer

Additive patterns multiply by a constant, while multiplicative patterns add a constant.

Answer

Both types involve adding a constant.

Answer

Both types involve multiplying by a constant.

Question 15

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

Accepted Answer

Achieving stationarity

Answer

Removing seasonality

Answer

Estimating ARIMA model parameters

Answer

Enhancing forecast accuracy

Question 16

Which option is NOT a valid method of seasonal decomposition?

Accepted Answer

Cubic decomposition

Answer

Additive decomposition

Answer

Multiplicative decomposition

Answer

Log-linear decomposition

Question 17

What is the primary objective of cross-validation in time series forecasting?

Accepted Answer

Assessing model performance on unseen data

Answer

Optimizing model parameters

Answer

Improving forecast accuracy

Answer

Accounting for seasonality

Question 18

Which of the following accurately defines the concept of stationarity in time series analysis?

Accepted Answer

The statistical characteristics of a time series are constant over time.

Answer

The mean of a time series is time-dependent.

Answer

The variance of a time series increases with time.

Answer

The autocorrelation between values in a time series is random.

Question 19

The measure that quantifies the linear relationship between values in a time series at different time lags is known as:

Accepted Answer

Autocorrelation function (ACF)

Answer

Cross-correlation function

Answer

Partial autocorrelation function

Answer

Spectral density function

Question 20

In an ARIMA model, the symbol 'd' represents:

Accepted Answer

The order of differencing

Answer

The order of autoregression

Answer

The order of moving average

Answer

The number of seasonal periods

Question 21

A characteristic feature of a random walk process is:

Accepted Answer

Zero autocorrelation at all time lags

Answer

Constant mean and variance

Answer

Predictable future values

Answer

Steady fluctuations around a linear trend

Question 22

The ADF test is primarily used to test for:

Accepted Answer

Stationarity

Answer

Causality

Answer

Heteroskedasticity

Answer

Skewness

Question 23

A time series with a pronounced seasonal component typically exhibits:

Accepted Answer

High autocorrelation within seasons

Answer

Low variance across seasons

Answer

Zero mean

Answer

Constant amplitude

Question 24

Which of the following is an essential prerequisite for a time series to be considered stationary?

Accepted Answer

Constant mean and variance over time

Answer

Symmetric distribution

Answer

Periodic oscillations

Answer

Positive autocorrelation

Question 25

The autocorrelation function (ACF) provides information about:

Accepted Answer

The correlation between observations separated by varying time lags

Answer

The mean of the time series

Answer

Only large time lags

Answer

Only positive time lags

Question 26

Which of the following methods is widely used for forecasting time series data?

Accepted Answer

Exponential smoothing

Answer

Factor analysis

Answer

Logistic regression

Answer

K-nearest neighbors

Question 27

The Box-Jenkins methodology is a structured approach to time series analysis that involves:

Accepted Answer

Identifying, estimating, and validating appropriate time series models

Answer

Primarily graphical techniques

Answer

Only applicable to stationary data

Answer

Only short-term forecasting

Question 28

A random walk process is characterized by:

Accepted Answer

Unpredictable future values

Answer

High persistence in the data

Answer

Recurring periodic patterns

Answer

Constant mean and variance

Question 29

Which of the following is a prevalent source of non-stationarity in economic time series?

Accepted Answer

Seasonality

Answer

White noise

Answer

Heteroscedasticity

Answer

Autocorrelation

Question 30

The Dickey-Fuller test is primarily used to determine:

Accepted Answer

The presence or absence of a unit root in a time series

Answer

The optimal forecasting model

Answer

The degree of autocorrelation

Answer

The stationarity of the data

Question 31

A key benefit of employing time series analysis in economic modeling is:

Accepted Answer

Improved forecasting accuracy

Answer

Reduced data requirements

Answer

Simplified model specification

Answer

Lower computational complexity

Question 32

Which of the following software packages is widely used for time series analysis?

Accepted Answer

R

Answer

Google Sheets

Answer

Microsoft Excel

Answer

Tableau

Question 33

Which of the following is a defining characteristic of a stationary time series?

Accepted Answer

Constant mean, variance, and autocorrelation structure over time

Answer

Strong autocorrelation at all lags

Answer

Deterministic trend

Answer

Periodic seasonality

Question 34

What is the primary purpose of examining autocorrelation in time series analysis?

Accepted Answer

To evaluate the dependence of current observations on past observations

Answer

To determine the order of integration of the series

Answer

To directly forecast future values of the series

Question 35

When forecasting a time series with a pronounced seasonal pattern, which method is most appropriate?

Accepted Answer

Seasonal Autoregressive Integrated Moving Average (SARIMA)

Answer

Exponential smoothing

Answer

Linear regression

Answer

Random walk model

Question 36

What fundamental distinction exists between time series data and cross-sectional data?

Accepted Answer

Time series data are ordered sequentially in time, whereas cross-sectional data represent observations at a single point in time

Answer

Time series data can be continuous or discrete, while cross-sectional data are always discrete

Answer

Time series data can be stationary or non-stationary, while cross-sectional data are always stationary

Question 37

What is the overarching objective of time series analysis in the field of economics?

Accepted Answer

To gain insights into economic phenomena and forecast economic variables

Answer

To develop complex statistical models for economic data

Answer

To validate economic theories solely through time series analysis

Answer

To describe the underlying structure of economic time series

Question 38

Which statistical test is commonly used to evaluate the stationarity of a time series?

Accepted Answer

Augmented Dickey-Fuller (ADF) test

Answer

Jarque-Bera test

Answer

Granger causality test

Answer

Breusch-Godfrey test

Question 39

What is the key distinction between Autoregressive (AR) and Moving Average (MA) processes in time series analysis?

Accepted Answer

AR processes have autoregressive terms, while MA processes have moving average terms

Answer

AR processes are stationary, while MA processes are non-stationary

Answer

AR processes can be forecasted using linear regression, while MA processes cannot