When the Sun Rises
"Estimating Demand" Please respond to the following: Provide an example when it would be appropriate to conduct a time-series or cross sectional data. Evaluate the potential problems that may arise with your example and identify strategies for minimizing the impact of the potential problems.
According to the text, Data collected for use in forecasting the value of a particular variable may be classified into two major categories - time series or cross sectional data. Time-series data are defined as a sequence of the values of an economic variable at different points in time. Cross-sectional data are an array of the values of an economic variable observed at the same time, like the data collected in a census across many individuals in the population. No matter what type of forecasting model is being used, one must decide whether time-series or cross-sectional data are most appropriate (McGuigan, Moyer, and Harris 2011 pg. 141, )
Time-series data can be gathered in a measurement of the population. For example,
We use the data set in the table below to construct a time series graph. The data is from the U.S. Census Bureau and reports the U.S. resident population from 1900 to 2000. The horizontal axis measures time in years, and the vertical axis represents the number of people in the U.S. The graph shows us a steady increase in population that is roughly a straight line. Then the slope of the line becomes steeper during the Baby Boom.
Cross-sectional data are data that are collected from participants at one point in time. Time is not considered one of the study variables in a cross-sectional research design. However, it is worth noting that in a cross-sectional study, all participants do not provide data at one exact moment. Even in one session, a participant will complete the questionnaire over some duration of time. Nonetheless, cross-sectional data are usually collected from respondents making up the sample within a relatively short time frame
According to the text, Data collected for use in forecasting the value of a particular variable may be classified into two major categories - time series or cross sectional data. Time-series data are defined as a sequence of the values of an economic variable at different points in time. Cross-sectional data are an array of the values of an economic variable observed at the same time, like the data collected in a census across many individuals in the population. No matter what type of forecasting model is being used, one must decide whether time-series or cross-sectional data are most appropriate (McGuigan, Moyer, and Harris 2011 pg. 141, )
Time-series data can be gathered in a measurement of the population. For example,
We use the data set in the table below to construct a time series graph. The data is from the U.S. Census Bureau and reports the U.S. resident population from 1900 to 2000. The horizontal axis measures time in years, and the vertical axis represents the number of people in the U.S. The graph shows us a steady increase in population that is roughly a straight line. Then the slope of the line becomes steeper during the Baby Boom.
Cross-sectional data are data that are collected from participants at one point in time. Time is not considered one of the study variables in a cross-sectional research design. However, it is worth noting that in a cross-sectional study, all participants do not provide data at one exact moment. Even in one session, a participant will complete the questionnaire over some duration of time. Nonetheless, cross-sectional data are usually collected from respondents making up the sample within a relatively short time frame