Boiler Plate

 

State Space Model Estimation

The Measurement Matrix for the state space models was constructed using Principal Components Analysis with standardized data from the World Development Indicators. The statistical analysis was conducted in an extension of the dse package. The package is currently supported by an online portal (here) and can be downloaded, with the R-programming language, for any personal computer here. Code for the state space Dynamic Component models (DCMs) is available on my Google drive (here) and referenced in each post.

Atlanta Fed Economy Now

My approach to forecasting is similar to the EconomyNow model used by the Atlanta Federal Reserve. Since the new Republican Administration is signaling that they would like to eliminate the Federal Reserve, the app might well not be available in the future.

While the app is still available, there have been some interesting developments. In earlier forecasts, the Atlanta Fed was showing GDP growth predictions outside the Blue Chip Consensus. Right now, after unorthodox economic policies from the Trump II Administration, the EconomyNow model is predicting a drastic drop in GDP (the Financial Forecast Center is only predicting a slight drop here).

Climate Change

Another comparison for what I have presented above are the IPCC Emission Scenarios. These scenarios are for the World System. Needless to say, (1) the new Right-Wing Republican administration plans on withdrawing the US from all attempts to study or ameliorate Climate Change and (2) the IPCC does not produce any RW modes for the World System (but seem my forecasts here).

World System

The longest running set of data we have for the World-System is the Maddison Project based on the work of Angus Maddison (more information is available here). Data on production (Q) and population (N) for most countries and regions runs from years 0-2000. More data becomes available as we near the year 2000. 

Available data were entered in a spreadsheet (see Population above, double click to enlarge). Missing data were interpolated with nonlinear spline smoothing using the R programming Language.

In cases where initial values were not available (see GDP above), the E-M Algorithm was used to estimate initial conditions.

From the graph of GDP above (W_Q) for the World System, it can be seen that economic growth from the year 0-1500 was basically flat. The period of British Capitalism (after 1500) had a small plateau of growth. Takeoff does not happen until the Nineteenth Century.

From a system's perspective, the only model that can be tested for the entire period is Kenneth Boulding's Malthusian Systems Model [Q,N] = f[Q,N].

When developed as a State Space model (measurement matrix above) there are two components: W1=Growth and W2=(Q-N), the Malthusian Controller. When more data is available, the Malthusian Controller can be generalized to other SocioEconomic theories.

What the Malthusian Controller shows (plotted as Q-N above) is that a long-developing Malthusian Crisis (Q<N) started in the Late Middle Ages and accelerated through the period of British Capitalism (Dark Satanic Mills) and was reversed spectacularly during the Nineteenth Century.  Takeoff in response to a deepening Malthusian Crisis would not be an unreasonable way to view Modern Economic Growth.

Error Correcting Controllers (ECC)

In another post (here), I presented Leibenstein's Malthusian Error Correcting Controller (ECC). It can be generalized to the dominant ECCs in most theoretical economic models (above). These controllers can be further generalized. For example, (X-U) and (L-U) can be generalized to (N-U), a more general Urbanization Controller which describes market expansion for economic growth. In countries and periods with limited data, (N-U) might subsume all these processes. ECCs describe important feedback processes in SocioTechnical System that are typically not recognized as such in academic literature.

Kaya Identity

The basic theoretical model underlying all the World-System models I crate is the Kaya Identity. There are a number of advantages to starting theoretical development with the Kaya Identity: (1) An "identity" is true by definition Adding other variables to the model ensure that theory construction is on a solid footing. (2) The Kaya Identity is also used as the foundation for the IPCC Emissions Scenarios allowing a linkage between World-Systems Theory and the work of the IPCC.

World Development Indicators (WDI)

After WWII, extensive data sets on all countries in the World-System became available from the World Bank (here). The indicators above where chose to construct the state space for each WDI-based model. Addition indicators can be added for specific forecasts and analyses.

About

The Long Nineteenth Century is a term used by Historians to describe the period from the French Revolution in 1789, and ending with the outbreak of World War I in 1914. My dissertation in 1981 (here) studied the German Reich from the period 1872-1908 using state space statistical models. Since then, I have developed models for the major countries in the World System using the Maddison database. In this blog, I present results from those models.

My interest is developing models of Societal Development. My canonical model is a State Space Dynamic Components Model (DCM, see the Boiler Plate).  There are different types of DCMs based on types of systems inputs. Each model has three state variables: (1) Overall Growth, (2)  Dominant Historical Feedback Controller and (3) Secondary Historical Feedback controller. My general hypothesis is that the form of the DCM varies over time as a result of historical conditions and conditions in the World-System.

Periodization

For the Quantitative Historian, selecting a time period over which to display statistical data or estimate models is an interesting problem with no solution. My approach has been estimate multi-models over whatever period seems appropriate to the purple (from a decade to 2000 years). A meta-analysis of the multi-models will have to be relegated to the future.

A General Periodization based on Centuries is provided in the Boiler Plate. In general, when I'm analyzing a Long "Century," the estimates are based on a given Century (for example, the Twentieth Century) and then projected into the future (for example, the Twenty-first Century).

Starting with the Twentieth Century, here are some interesting references with links where you can download the free pdf files:

• The Age of Extremes (Wikipedia Article)
• The Long Twentieth Century, Giovanni Aright 
• The Age of Extremes, The Short Twentieth Century, 1914-1991, Eric Hobsbawm
• The Age of Capital, 1848-1875, Eric Hobsbawm
• The Age of Revolution, 1789-18484, Eric Hobsbawm
• The Age of Empire, 1875-1914, Eric Hobsbawm

Notes

World-system theory (WST) has developed a new language for analyzing the historical social, economic and political development of societies based on concepts from General Systems Theory (GST). The basic GST concept is that all systems are organized in hierarchies. Applying this concept to the world system, WST find nation states organized into core, semi-peripheral and peripheral countries that create an inter-regional and transnational division of labor.

The premise about hierarcical organization, the analysis of world-system history and the adoption of other GST concepts has led to a number of conjectures. For example, the system works to the benefit of the core countries, bringing in the Marxist theory of Capitalist Exploitation. Or, the role of long-swings and business cycles in creating historical conjunctures when the system can change its orientation. Or, technological changes originate in the core and are used to further exploit the peripheral countries. Or, hierarchical imbalances generate world-system conflict that reaches a peak during system conjunctures.

WST has, understandably, not pursued every insight from GST. There is more to be done, particularly in the area of quantitative systems analysis and model building. My interest in this blog is to investigate World-System conjectures using quantitative models based on GST. In another blog, I am developing Causal Macrosystems using directed graphs (structural equation models) and state-space theory, quantitative tools drawn from GST. In yet another blog (Economic Bubble Machine), I am looking at economic cycles. This blog will use the insights from those blogs to develop empirical systems models of core, semi-peripheral and peripheral countries.

The kinds of questions I will be asking of these models are:

1. Is there a difference between the structure and time-series behavior of nation states based on their position in the world system?
2. Are models for failed states different form other peripheral country models?
3. Are technological parameters and technological change different based on world-system position?
4. How are effects transmitted, if at all, from core to semi-peripheral and peripheral countries?
5. Is there a difference in how systems react to economic crises based on world-system position?
6. etc.

I have been working with GST macro models since the mid-1970's. My first macro-model was published in my dissertation Instability and Late Nineteenth Century German Development. My goal is to put together the things that I have learned in the last forty years for the next generation of model builders.