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The paper "Economics and Business Forecasting" is a great example of business statistics project.

The time series for the U.S Imports of Goods from the United Kingdom, Customs Basis: Millions of Dollars

Classical time series decomposition techniques,

The series appears volatile as it keeps going up and down over the last five years and so the forecast will be the same. It will be irregular.

A simple linear regression model where you account for possible time trends

Accordingly, ft+1 will decrease if it turns out that yt < ft – the term in the parenthesis will then be negative and as long as _ 6= 0 then ft+1 < ft.

A multiple regression model

Variables to be included are

Interest rate and Income

Forecasts will be produced from the independent variables by using Holt-Winter's methods to do that because this is seasonally adjusted data. The model is the run to obtain the fitted values. For this period of forecasting, these would be based on the forecasted independent variables.

An appropriate ARMA model

The dynamic forecasting

Although it may make a wrong forecast for one time period, in future time periods, the actual values for the variable will be used in making forecasts, so that the wrong or bad forecasts forecast do not have any impacts on forecasts for subsequent time periods.

The time series for U.S. Exports of Goods to China, Mainland, f.a.s. basis: Millions of Dollars Classical time series decomposition techniques

This will tend to be constant depending on the demand and the supply of exports and so it will be regular for the next five years.

A simple linear regression model where you account for possible time trends

The set of explanatory variables, in this case, includes only a "time count" variable i.e. x = 1 for the first observation, x = 2 for the second and so on until x = N for the last observation in a sample of size N.

A multiple regression model

The model is common for economic data so as to manifest nonlinear tendencies. A good example is income profiles where the income diminishes with age.

An appropriate ARMA model

The model gives a prediction that calculates the one-step-ahead predictions from the fitted model.

Harmonized Index of Consumer Prices: Passenger Transport by Air for the United Kingdom: Index 2005=100

Classical time series decomposition techniques

The consumer prices and transport are to vary because of the fluctuations n the fuel industry and consumer goods.

A simple linear regression model where you account for possible time trends

There will be a linear relationship between consumer goods and prices and the harmonization of the prices of transport by air.

A multiple regression model

According to the business environment, the technical ability of harmonization of the prices in the next five years will be a series of irregular prices in the market.

An appropriate ARMAX model

The list of the independent variables should be included when the harmonization of prices is done to give a regular prediction in the next five years.

The time series for the U.S Imports of Goods from the United Kingdom, Customs Basis: Millions of Dollars

Classical time series decomposition techniques,

The series appears volatile as it keeps going up and down over the last five years and so the forecast will be the same. It will be irregular.

A simple linear regression model where you account for possible time trends

Accordingly, ft+1 will decrease if it turns out that yt < ft – the term in the parenthesis will then be negative and as long as _ 6= 0 then ft+1 < ft.

A multiple regression model

Variables to be included are

Interest rate and Income

Forecasts will be produced from the independent variables by using Holt-Winter's methods to do that because this is seasonally adjusted data. The model is the run to obtain the fitted values. For this period of forecasting, these would be based on the forecasted independent variables.

An appropriate ARMA model

The dynamic forecasting

Although it may make a wrong forecast for one time period, in future time periods, the actual values for the variable will be used in making forecasts, so that the wrong or bad forecasts forecast do not have any impacts on forecasts for subsequent time periods.

The time series for U.S. Exports of Goods to China, Mainland, f.a.s. basis: Millions of Dollars Classical time series decomposition techniques

This will tend to be constant depending on the demand and the supply of exports and so it will be regular for the next five years.

A simple linear regression model where you account for possible time trends

The set of explanatory variables, in this case, includes only a "time count" variable i.e. x = 1 for the first observation, x = 2 for the second and so on until x = N for the last observation in a sample of size N.

A multiple regression model

The model is common for economic data so as to manifest nonlinear tendencies. A good example is income profiles where the income diminishes with age.

An appropriate ARMA model

The model gives a prediction that calculates the one-step-ahead predictions from the fitted model.

Harmonized Index of Consumer Prices: Passenger Transport by Air for the United Kingdom: Index 2005=100

Classical time series decomposition techniques

The consumer prices and transport are to vary because of the fluctuations n the fuel industry and consumer goods.

A simple linear regression model where you account for possible time trends

There will be a linear relationship between consumer goods and prices and the harmonization of the prices of transport by air.

A multiple regression model

According to the business environment, the technical ability of harmonization of the prices in the next five years will be a series of irregular prices in the market.

An appropriate ARMAX model

The list of the independent variables should be included when the harmonization of prices is done to give a regular prediction in the next five years.