GDP+Forecast+Project

Willamette University - April 2011

 =GDP Forecast Project = --

//By Annelise Hagmann and Jérémy Faure// //GSM672 – Pr. Paul Dwyer.//

**Introduction and Literature Review**

Economists use historical data to determine a country’s Gross Domestic Product (GDP) every year. This is a measure of how healthy a particular country’s economy is and is used to rank countries. Countries with higher GDPs are growing markets with lots of opportunities for investments and business ventures. The annual GDP is an economic tracking device that also influences political and economic decisions for the future. Since this measure is used for many different strategic decisions, is there a way to predict a country’s GDP based on past historical data. For example, when will China and other emerging markets overtake the US and other developed countries? At PwC, they use historical data to predict future country ranks through GDP analysis. They study the “rate at which poorer countries can catch up to the more advanced technologies used in developed nations”//[1]////. They have tried to predict over 50 years but our purpose is to have shorter-term goals and not predict so far out in the future. We could use their data as a means of testing our own prediction models to see how accurate they are.//

//GDP projections to 2050: how the rankings change. //[2]

How can we predict if a country is on the right economic track? That is to say in a world that is flattening//[3]// //, the purpose of this study is to see if it is possible to predict with a good accuracy//[4] the GDP of one country based on several other indicators gathered by the World Bank for 213 countries over 1157 variable series covering the time period between 1960 and 2007. In case we do not have an accurate enough prediction of the GDP, we would seek to build a model that could classify clusters to identify similar countries. Additionally for that purpose we may be able to identify a country today that has approximately the same financial indicators that another country had in previous years. For instance, if Pakistan today is in the same location as China in 1993.
 * Purpose **

**Hypothesis** We selected the GDP as the indicator that would reflect the condition of leaving. We do in advance acknowledge that this may not be the best-aggregated indicator to indicate such thing and thus other aggregated indicators may better reflect the condition of leaving, like for instance the Gini coefficient, the Physical Quality of Life Index. However, based on the data we were able to collect the GDP appears to be the best to suit the study.

**Data collection** We collected data from the World Bank data catalog which is downloadable on their website. The dataset we selected focus on 1157 variables ranging from major financial indicators to non-financial (forest area, average precipitation, time required to start a business) over 40 years for 213 countries.

Being able to predict GDP or cluster countries together, can help determine the future and see if the country is “on the right track”. Although we believe that financial variables are not the only issues that should determine the standard of living. Before using the variables in the model, they will be evaluated to see if they are relevant and can be used. Data provided by the World Bank covers: social, economic, financial, natural resources, infrastructure, governance and environmental indicators. This should enable us to have a broad variety of data and help us achieve our goal to have a comprehensive model while not using only financial variables, although our ultimate measurement is the GDP. As we have a great amount of variables (1157), some may already include/serve to calculate GDP and that some may be redundant or do not have enough information over time (internet connectivity for instance), the data will be cleaned manually for that purpose. Unnecessary data, meaning outliers and extreme, will be cleaned through the data preparation feature available within SPSS Modeler. Using the PCA feature on SPSS we will be able to combine important variables that load similarly to reduce our data size and see which variables are useful for predictions. We will also need to suppress data participating in the calculation of the GDP: private consumption, gross investment, government spending, exports and imports. Otherwise there will be no real prediction, only additions to retrieve the GDP value. In the same way, some data is expressed as a percentage of the GDP; consequently they need to be deleted as well. Eventually we may have to revamp the way the data is organized. The matrix today as the following shape: To better handle the data we may need to have the following matrix:
 * Procedure **
 * **Business Understanding**
 * **Data Understanding**
 * **Data Preparation**
 * Series Name || Country || 1960 || … || 2007 ||
 * Serie1 || USA || xxx || … || aaa ||
 * Serie1 || France || yyy || … || bbb ||
 * date || Country || Series 1 || … || Serie2 ||
 * 1960 || USA || xxx || … || aaa ||
 * 1960 || France || yyy || … || bbb ||

This manipulation can easily be done thru a pivot table in Excel. If we have trouble using the data time frame we can combine country with dates to form a new variable that represents both indicators. For example:
 * Country/Date || Series 1 || Series 2 || Series 3 ||
 * USA 2000 || X || YX || YYY ||
 * USA 2001 || XY || YY || XXX ||

We will use the Cox regression model initially to evaluate the data. We will also use other models available within SPSS Modeler to determine which model is the best. Multiple comparisons will be done between the different models and within the model to compare tweaking effectiveness. Those measurements will help us determine which model should be used and how it should be setup to analyze and best predict the GDP for certain countries over time.
 * **Modeling**
 * **Evaluation**

[] [] [|http://www.guardian.co.uk/news/datablog/2011/jan/07/gdp-projections-china-us-uk-brazil#]
 * Links: **

[1] //“GDP Projections from PwC: how China, India, and Brazil will overtake the West by 2050”, Larry Elliot. Jan 7th, 2011. www.guardian.co.uk/news/datablog/2011/jan/07/gdp-projections-china-us-uk-brazil// // [2] Id. //

[3] //« The World is Flat », Friedman//

// [4] Above 95% //