Blog Topics...

3D plotting (1) Academic Life (2) ACE (18) Adaptive Behavior (2) Agglomeration (1) Aggregation Problems (1) Asset Pricing (1) Asymmetric Information (2) Behavioral Economics (1) Breakfast (4) Business Cycles (8) Business Theory (4) China (1) Cities (2) Clustering (1) Collective Intelligence (1) Community Structure (1) Complex Systems (42) Computational Complexity (1) Consumption (1) Contracting (1) Credit constraints (1) Credit Cycles (6) Daydreaming (2) Decision Making (1) Deflation (1) Diffusion (2) Disequilibrium Dynamics (6) DSGE (3) Dynamic Programming (6) Dynamical Systems (9) Econometrics (2) Economic Growth (5) Economic Policy (5) Economic Theory (1) Education (4) Emacs (1) Ergodic Theory (6) Euro Zone (1) Evolutionary Biology (1) EVT (1) Externalities (1) Finance (29) Fitness (6) Game Theory (3) General Equilibrium (8) Geopolitics (1) GitHub (1) Graph of the Day (11) Greatest Hits (1) Healthcare Economics (1) Heterogenous Agent Models (2) Heteroskedasticity (1) HFT (1) Housing Market (2) Income Inequality (2) Inflation (2) Institutions (2) Interesting reading material (2) IPython (1) IS-LM (1) Jerusalem (7) Keynes (1) Kronecker Graphs (3) Krussel-Smith (1) Labor Economics (1) Leverage (2) Liquidity (11) Logistics (6) Lucas Critique (2) Machine Learning (2) Macroeconomics (45) Macroprudential Regulation (1) Mathematics (23) matplotlib (10) Mayavi (1) Micro-foundations (10) Microeconomic of Banking (1) Modeling (8) Monetary Policy (4) Mountaineering (9) MSD (1) My Daily Show (3) NASA (1) Networks (46) Non-parametric Estimation (5) NumPy (2) Old Jaffa (9) Online Gaming (1) Optimal Growth (1) Oxford (4) Pakistan (1) Pandas (8) Penn World Tables (1) Physics (2) Pigouvian taxes (1) Politics (6) Power Laws (10) Prediction Markets (1) Prices (3) Prisoner's Dilemma (2) Producer Theory (2) Python (29) Quant (4) Quote of the Day (21) Ramsey model (1) Rational Expectations (1) RBC Models (2) Research Agenda (36) Santa Fe (6) SciPy (1) Shakshuka (1) Shiller (1) Social Dynamics (1) St. Andrews (1) Statistics (1) Stocks (2) Sugarscape (2) Summer Plans (2) Systemic Risk (13) Teaching (16) Theory of the Firm (4) Trade (4) Travel (3) Unemployment (9) Value iteration (2) Visualizations (1) wbdata (2) Web 2.0 (1) Yale (1)

Tuesday, November 23, 2010

International Trade Network: A First Pass...

I will be presenting a talk on the structure of international trade at a networks workshop being held here at the University of Edinburgh.  What follows is a dump of my thoughts related to the subject which so far are largely based on my attempt to replicate recently published work on the structure of international trade network.

International Trade Network Data...
There are two principle sources of network data on international trade (that I have been able to find):
  1. Prof. Kristian Skrede Gleditsch at the University of Essex:  Data are from 1948-2000 and the primary source is the IMF.
  2. UN Comtrade: Data are from 1962-2009 and the primary source is of course the UN.  
  3. There is a third data source, the Economics Web Institute, that I would like to dissuade people from using even though they use Prof. Gleditsch's IMF data (without going into too much detail, the Economics Web Institute, seems to use an inconsistent methodology to assign edge weights (trade values) to countries when converting from Prof. Gleditsch's raw .asc files to .xls workbooks). 
If you would like to hear the gory details on the data validity issues feel free to contact me.

Hierarchical Clustering and Trade Network Density:
For the moment I am simply trying to replicate the work of the Deem et al. (2010) paper (linked to above).  I have applied a average linkage hierarchical clustering algorithm to the OECD international trade network as outlined in their paper.  Below are some plots of the cophenetic correlation coefficient (CCC) and network density for the international trade network for OECD countries using two different data-sets.  The first plot uses data entirely from the UN Comtrade database.  NBER recessions are marked with gray bars.  I downloaded the data by hand from Comtrade (commodity code is SITC ver. 1 AG0) and then used a Python script to clean and reorganize the .xls spreadsheets into more manageable text files.  Statistical analysis is done using SciPy, network analysis (so far) has been done using NetworkX, and plotting has been done using Matplotlib.     
The plot below is the CCC and network density for the international trade network for OECD countries using Prof. Gleditsch's IMF data for 1948-2000 and then UN Comtrade from 2001-2009 (commodity code this time is SITC ver. 3 AG0). 
There are some differences between the two plots of the CCC (I have not yet tested whether or not they are significantly different nor have I tested whether or not the CCC jumps significantly during/after recessions...this is on my to do list!).

Still to come:
  1. Dendrogram of identified clusters
  2. Results of community structure algorithm applications
  3. Weighted clustering algorithms and other graph measures
Basically I am just trying to learn to do network analysis using Python by applying the tools to an interesting topic...

No comments:

Post a Comment