Question

1. What is the objectives of doing time series analysis?

2. In a regression analysis, which single statistic should you use to evaluate the adequacy of the regression equation?

3. List the three main functions of CPI, the consumer price index?

Answer 1

1. A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. But the variable under study can be Continuous or discrete. Examples of time series are prediction of weather or weather forecasting, etc. It is an important field under Econometrics where the

Dependent variable is explained by its own past values and past errors in the model. Because for few phenomena, a variable can be predicted based on its behavious across time.

Time series are very frequently plotted via line charts to extract meaningful statistics and other characteristics of the data.

Objective : Description , Explanation, Prediction and Control for the variable under study.

2. For the adequacy of entire model , we use F statistic = where MST is the variance explained by the regression and MSE is the unexplained variance. If this statistic comes to be significant (F > Fcritical), then the model becomes significant. Here, MST = SST/df1 and MSE = SSE/df2.

3. The Consumer Price Index (CPI) measures the average price change of a set of consumer goods and services. CPIs can be calculated for single items or a predetermined group of items. All of these items are defined as "household goods and services. The 3 main functions of Consumer Price Index are:

• CPI measures the average price change of certain consumer goods and services.
• CPI can reveal inflation and cost of living over a given period of time.
• CPI influences government economic policy and helps maintain the health of our economy.

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