Thursday, April 5, 2012

Summary of Findings (White Team): Economic Trend Analysis (2.5 out of 5 stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University on 5 April 2012 regarding Economic Trend Analysis specifically. This technique was evaluated based on its overall validity, simplicity, flexibility, its ability to effectively use unstructured data, and its ease of communication to a decision maker.

Description: 

Economic trend analysis is a predictive methodology which allows users to analyze current and historical data on economic factors by comparing and contrasting trends and patterns within this data to reduce uncertainty and form estimates about relevant events and situations. Missing or unreliable information, particularly from less-developed regions, tends to lower analytic confidence in the results of this methodology.  While the analysis can project normal continuations of historical trends, it is unreliable in forecasting rare or “black swan” events.

Strengths:

  • Useful for handling large amounts of data over time when comparing and contrasting different economic factors and their effects on each other.
  • Useful in forecasting and creating a hypothetical trajectory of economic trends based on historical data.
  • Can be used to translate unfamiliar or seemingly random data into sensible patterns based on past trends.
  • Even in cases of unreliable or incomplete data, it can be used to produce a range or show general trends that can be helpful in reducing uncertainty.
Weaknesses:

  • Information is often missing or incomplete on less-developed regions.
  • Information reliability is vitally important to the utility of data for developing confident estimates - for example, high corruption in a region lowers the value of reports from any internal information sources.
  • Unable to predict “black swan” events.
  • Multiple sources may give different answers or inaccurate answers. 
 
How-To:

  1. Clearly identify the target of the methodology (a city/county/region/industry, etc).
  2. Select a time-period to examine and its granularity (data divided by day, month, year, etc).
  3. Select one or more economic indicators on which to collect data.
  4. Collect and organize economic data. Aggregation sites such as the following are often useful at this stage:
    1. World Bank
    2. IndexMundi
    3. Gap Minder
    4. Organization for Economic Co-operation and Development
    5. Google Public Data
    6. SIPRI Facts on International Relations and Security
  5. Analyze economic trends and patterns evident within the collected data, drawing conclusions and forming estimates based on the data’s content and reliability.

Personal Application of Technique:
For the activity, the class was divided into 5 groups, each assigned a country such as Sweden or Uzbekistan. Each group was tasked with collecting information on a variety of economic indicators such as GDP, population growth rate, and inflation rates over the period from 2006 to 2010. The teams entered relevant data on their assigned tabs of a Google Documents spreadsheet, which auto-created a few relevant charts to compare trends between indicators. Finally, each group was asked to draw some simple conclusions from the data available, both about the economic trends each team found and about their confidence in that information based on availability and national corruption index scores.

 

1 comment:

  1. Great many thanks! Your post contains information that brings me a step closer to getting the bigger picture. Cheers!
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