Question

If there is a violation of the independence of error assumption for time series data, this...

If there is a violation of the independence of error assumption for time series data, this could most likely cause _________. Question 1 options: autocorrelation. collinearity. a deflation of the standard error of the slope. unequal error variances.

Homework Answers

Answer #1

If there is a violation of the independence of error assumption for time series data, this could most likely cause: autocorrelation

[ we know that,

Serial correlation , which is known as autocorrelation is sometimes a byproduct of a violation of linearity assumption,for example, in the case of a simple trend line fitted to data which are growing exponentially over time. extreme serial correlation is a symptom of a badly mis-specified model ]

***in case of doubt, comment below. And if u liked the solution, please like.

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
which of the following is a violation of the independence assumption? negative correlation a pattern of...
which of the following is a violation of the independence assumption? negative correlation a pattern of cyclical error terms over time positive autocorrelation a pattern of alternating error terms over time all of the other choices are correct
Consider the following gasoline sales time series data. Click on the datafile logo to reference the...
Consider the following gasoline sales time series data. Click on the datafile logo to reference the data. Week Sales (1000s of gallons) 1     16    2     20    3     19    4     23    5     19    6     15    7     19    8     17    9     23    10     20    11     15    12     21    a. Using a weight of (1/2) for the most recent observation, (1/3) for the second most recent observation, and (1/6) third the...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14 16 12 17 14 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. Round the intermediate calculations to two decimal places. Mean absolute error (to 1 decimal). Mean squared error (to 1 decimal). Mean absolute percentage error (to 2 decimals). % What is the forecast for week 7 (to the nearest whole...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14 17 12 17 14 Calculate the measures of forecast error using the naive (most recent value) method and the average of historical data (to 2 decimals). Naive method Historical data Mean absolute error Mean squared error Mean absolute percentage error Which method provides the most accurate forecasts? SelectNaiveHistorical dataItem 7
Consider the following gasoline sales time series data. Week Sales (1000s of gallons) 1     18   ...
Consider the following gasoline sales time series data. Week Sales (1000s of gallons) 1     18    2     22    3     20    4     24    5     17    6     15    7     19    8     17    9     23    10     19    11     14    12     23    a. Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and 1/6 third the most recent observation, compute a three-week weighted moving average...
2. We will be creating a monthly time series plot from raw data from the internet....
2. We will be creating a monthly time series plot from raw data from the internet. 2.1 Using data at from the Bureau of Labor Statistics (BLS), create a MONTHLY time series dataset. You may use site http://data.bls.gov/cgi-bin/surveymost?bls (Links to an external site.)Links to an external site. which is a list of "Top Picks"; select the first CPI for all urban consumers (Price Indexes), and click "Retreive Data".   On the resulting data page, click on the "More Formatting Options" button,...
Consider the following time series data. Week 1 2 3 4 5 6 Value 19 14...
Consider the following time series data. Week 1 2 3 4 5 6 Value 19 14 16 11 18 15 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. Round the intermediate calculations to two decimal places. Mean absolute error (to 1 decimal). Mean squared error (to 1 decimal). Mean absolute percentage error (to 2 decimals). % What is the forecast for week 7 (to the nearest whole...
1. Using the data below, what is the value of RMSE? Week Time Series Value Forecast...
1. Using the data below, what is the value of RMSE? Week Time Series Value Forecast 1 3 7.00 2 5 4.00 3 2 5.00 4 8 8.00 2. Using the data below, calculate the squared error for the 4th week. Use the 2 period moving average to create the forecast. Week Time Series Value 1 19.00 2 11.00 3 22.00 4 7.00 Please, do the all the work and show processes
Consider the following time series data. Week 1 2 3 4 5 6 Value 19 14...
Consider the following time series data. Week 1 2 3 4 5 6 Value 19 14 17 10 17 13 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE = (b) mean squared error MSE = (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE = % (d) What is the forecast for week 7?
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 12...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 12 17 11 17 14 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE = (b) mean squared error MSE = (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE = % (d) What is the forecast for week 7?