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“The Future of Economics Uses the Science of Real-Life Social Networks” - PAUL OMEROD 1 The goal of this project is not to make a report or literature review or synthesis, rather to get some hands-on experience in working with Graphs and Network Data based on some classical and original (own) datasets and problems. It will involve both some theoretical understanding and programming. The outcome would be to get comfortable with this type of data and maybe build the ground for some future research.


The King County Homes prices prediction challenge is an excellent dataset for trying out and experimenting with various regression models. As we’ll see in the following post on Moscow flats, the modeler deals with similar challenges: skewed data and outliers, highly correlated variables (predictors), heteroskedasticity and a geographical correlation structure. Ignoring one of these may lead to undeperforming models, so in this post we’re going to carefully explore the dataset, which should inform which modeling strategy to choose.


In order to build adequate models of economic and other complex phenomena, we have to take into account their inherent stochastic nature. Data is just the appearance, an external manifestation of some latent processes (seen as random mechanisms). Even though we won’t know the exact outcome for sure, we can model general regularities and relationships as a result of the large scale of phenomena. For more ideas see (Ruxanda 2011)


I was impressed by the down-to-earth debate between Eugene Fama and Richard Thaler. Their discussion was very insightful in order to make sense of what’s going on with Efficient Market Hypothesis, CAPM, Fama and French 3 Factor Model, Markowitz and where is the field moving. This will be my last blog post on economics for a while, so expect lots of Machine Learning and Statistics topics next. This is a continuation that is supposed to add some missing pieces to the analysis done in the partI and partII


Last time we went through a rigorous process of eliciting prior beliefs about 5 stocks, exploratory data analysis and quite advanced descriptive stats. The last part of the assignment has the goal of drawing connections to the behavioral economics principles. A lesson learned for now, is that there are many pitfalls even in most innocently looking questions. Part IV. Portfolio Construction by Simulation Before we dig in, I would like to suggest the following reading "Please no, not another bias" by Jason Collin.



Explorations, learning, research and lots of fun!

Dissertation: Bayesian Inference and Forecasting with applications in microeconometrics and business

A Bayesian Hierarchical Model for TV Attribution, Hidden Markov Model for panel customer data, Forecasting demand for new products and short time series

Probabilistic Dynamic Modeling

Dynamics Linear and Nonlinear Models. Stochastic Filtering Institute of Mathematics' of Romanian Academy Research Group

Time Series Clustering

Bizovi Mihai and Jumanazar Gurbanov on Dynamic Time Wraping. Exploration of R Implementations

Thesis: Stochastic Modeling and Bayesian Inference

A synthesis of three perspectives: Stochastic Modeling, Machine Learning and Bayesian Inference. Gaussian Processes and Mixture Models

New Economic Thinking for a Knowledge-Based Society

Bizovi Mihai A critique of neoclassical economics and an exploration of models based on complexity science. With the support of dr. eng. Florin Munteanu.