Question 1

Econometric models can also be called _____ models.

Accepted Answer

A) judgmental 
B) time series 
C) causal 
D) environmetric 
A) judgmental 
B) time series 
C) causal 
D) environmetric

Question 2

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else,except possible time itself - to forecast values of the variable.

Accepted Answer

A) True 
 B)False

Question 3

Extrapolation methods attempt to

Accepted Answer

A) use non-quantitative methods to predict future values. 
B) search for patterns in the data and then use those to predict future values. 
C) find variables that are correlated with the data being predicted. 
D) predict the next period's value by using the latest period's value. 
A) use non-quantitative methods to predict future values. 
B) search for patterns in the data and then use those to predict future values. 
C) find variables that are correlated with the data being predicted. 
D) predict the next period's value by using the latest period's value.

Question 4

Holt's method is an exponential smoothing method,which is appropriate for a series with seasonality and possibly a trend.

Accepted Answer

A) True 
 B)False

Question 5

If a time series exhibits an exponential trend,then a plot of its logarithm should be approximately linear.

Accepted Answer

A) True 
 B)False

Question 6

Which of the following is not one of the commonly used summary measures for forecast errors?

Accepted Answer

A) MAE (mean absolute error) 
B) MFE (mean forecast error) 
C) RMSE (root mean square error) 
D) MAPE (mean absolute percentage error) 
A) MAE (mean absolute error) 
B) MFE (mean forecast error) 
C) RMSE (root mean square error) 
D) MAPE (mean absolute percentage error)

Question 7

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

Accepted Answer

A) True 
 B)False

Question 8

The most common form of autocorrelation is positive autocorrelation,in which

Accepted Answer

A) large observations tend to follow both large and small observations. 
B) small observations tend to follow both large and small observations. 
C) large observations tend to follow large observations and small observations tend to follow small observations. 
D) large observations tend to follow small observations and small observations tend to follow large observations. 
A) large observations tend to follow both large and small observations. 
B) small observations tend to follow both large and small observations. 
C) large observations tend to follow large observations and small observations tend to follow small observations. 
D) large observations tend to follow small observations and small observations tend to follow large observations.

Question 9

The runs test uses a series of 0's and 1's.The 0's and 1's typically represent whether each observation is

Accepted Answer

A) above or below the predicted value of Y. 
B) above or below the mean value of Y. 
C) is above or below the mean value of the previous two observations. 
D) is positive or negative. 
A) above or below the predicted value of Y. 
B) above or below the mean value of Y. 
C) is above or below the mean value of the previous two observations. 
D) is positive or negative.

Question 10

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

Accepted Answer

A) True 
 B)False

Question 11

Assume that the trend line   was calculated from quarterly data for 2011 - 2015,where t = 1 for the first quarter of 2011.The trend value for the second quarter of the year 2016 is 0.75.

Accepted Answer

A) True 
 B)False

Question 12

In an additive seasonal model,we add an appropriate seasonal index to a "base" forecast.These indexes,one for each season,typically average to 0.

Accepted Answer

A) True 
 B)False

Question 13

Winters' model differs from Holt's model and simple exponential smoothing in that it includes an index for

Accepted Answer

A) seasonality. 
B) trend. 
C) residuals. 
D) cyclical fluctuations. 
A) seasonality. 
B) trend. 
C) residuals. 
D) cyclical fluctuations.

Question 14

The number of reported accidents at a manufacturing plant located in Flint,Michigan,is recorded at the start of each month.State investigators are responding to a recent complaint by reviewing data from past complaints over sets of three years at a time.The data from 2006-2008 are provided in the table below:   Is this time series random? Perform a runs test and compute a few autocorrelations to support your answer.

Accepted Answer

@#IMG-DLM& The runs test indicates the data may be

Question 15

A moving average is the average of the observations in the past few periods,where the number of terms in the average is the span.

Accepted Answer

A) True 
 B)False

Question 16

Use the method of moving average with an appropriate span to forecast retail sales for 2010.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

Accepted Answer

@#IMG-DLM& @#IMG-DLM& The fit statistics show a MA

Question 17

An equation for the random walk model is given by the equation:   ,where   is the change in the time series from time t to time t - 1,   is a constant,and   is a random variable (noise)with mean 0 and some standard deviation   .

Accepted Answer

A) True 
 B)False

Question 18

In a random walk model,there are significantly more runs than expected,and the autocorrelations are not significant.

Accepted Answer

A) True 
 B)False

Question 19

Forecasting software packages typically report several summary measures of the forecasting error.The most important of these are MAE (mean absolute error),RMSE (root mean square error),and MAPE (mean absolute percentage error).

Accepted Answer

A) True 
 B)False

Question 20

A linear trend means that the time series variable changes by a _____ each time period.

Accepted Answer

A) constant amount 
B) constant percentage 
C) positive amount 
D) negative amount 
A) constant amount 
B) constant percentage 
C) positive amount 
D) negative amount

Econometric models can also be called _____ models.

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else,except possible time itself - to forecast values of the variable.

Extrapolation methods attempt to

Holt's method is an exponential smoothing method,which is appropriate for a series with seasonality and possibly a trend.

If a time series exhibits an exponential trend,then a plot of its logarithm should be approximately linear.

Which of the following is not one of the commonly used summary measures for forecast errors?

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

The most common form of autocorrelation is positive autocorrelation,in which

The runs test uses a series of 0's and 1's.The 0's and 1's typically represent whether each observation is

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

Assume that the trend line was calculated from quarterly data for 2011 - 2015,where t = 1 for the first quarter of 2011.The trend value for the second quarter of the year 2016 is 0.75.

In an additive seasonal model,we add an appropriate seasonal index to a "base" forecast.These indexes,one for each season,typically average to 0.

Winters' model differs from Holt's model and simple exponential smoothing in that it includes an index for

A moving average is the average of the observations in the past few periods,where the number of terms in the average is the span.

Use the method of moving average with an appropriate span to forecast retail sales for 2010.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

An equation for the random walk model is given by the equation: ,where is the change in the time series from time t to time t - 1, is a constant,and is a random variable (noise)with mean 0 and some standard deviation .

In a random walk model,there are significantly more runs than expected,and the autocorrelations are not significant.

Forecasting software packages typically report several summary measures of the forecasting error.The most important of these are MAE (mean absolute error),RMSE (root mean square error),and MAPE (mean absolute percentage error).

A linear trend means that the time series variable changes by a _____ each time period.

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 12: Time Series Analysis and Forecasting

Econometric models can also be called _____ models.

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else,except possible time itself - to forecast values of the variable.

Extrapolation methods attempt to

Holt's method is an exponential smoothing method,which is appropriate for a series with seasonality and possibly a trend.

If a time series exhibits an exponential trend,then a plot of its logarithm should be approximately linear.

Which of the following is not one of the commonly used summary measures for forecast errors?

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

The most common form of autocorrelation is positive autocorrelation,in which

The runs test uses a series of 0's and 1's.The 0's and 1's typically represent whether each observation is

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

Assume that the trend line was calculated from quarterly data for 2011 - 2015,where t = 1 for the first quarter of 2011.The trend value for the second quarter of the year 2016 is 0.75.

In an additive seasonal model,we add an appropriate seasonal index to a "base" forecast.These indexes,one for each season,typically average to 0.

Winters' model differs from Holt's model and simple exponential smoothing in that it includes an index for

A moving average is the average of the observations in the past few periods,where the number of terms in the average is the span.

Use the method of moving average with an appropriate span to forecast retail sales for 2010.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

An equation for the random walk model is given by the equation: ,where is the change in the time series from time t to time t - 1, is a constant,and is a random variable (noise)with mean 0 and some standard deviation .

In a random walk model,there are significantly more runs than expected,and the autocorrelations are not significant.

Forecasting software packages typically report several summary measures of the forecasting error.The most important of these are MAE (mean absolute error),RMSE (root mean square error),and MAPE (mean absolute percentage error).

A linear trend means that the time series variable changes by a _____ each time period.

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Analysis of Variance and Experimental Design

Statistical Process Control

Filters