This is a free open source project for software tools useful in financial economics. We develop research notebooks which are executable scripts capable of statistical computations, as well as collecting raw data in real-time. This serves to directly verify theoretical ideas and practical methods.

The project name derives from the seminar series held at the University of California at Berkeley, jointly sponsored by the Department of Economics and the Haas School of Business. Selected topics are treated for in-depth studies.

• Source data, both historical and the most current.

• Easy data munging, e.g. resampling and alignment of time series.

• Exploration of data using graphical packages.

• Analysis using techniques from econometrics and machine learning.

• Presentation of mathematical results (in LaTex) and models.

• Reproducible research which is collaborative and publicly accessible.

Database: currently our main source is FRED, the U.S. Federal Reserve Economic Data bank which is accessed directly online using our programs. Some specialized data can be directly retrieved using our Quandl API module, for example, futures prices. Data for stocks, ETFs, and mutual funds comes directly from Yahoo Finance, and falls back on Google Finance.

Models: we emphasize original research where the baseline comparison is to Ferbus, the model used internally by the Federal Reserve Bank. Accuracy of out-of-sample forecasts takes precedence over traditional measures of model fit.

• Deployment: for introduction to the Python ecosystem for financial economists, the best reference for getting started is the Quantitative Economics site by Thomas Sargent.

• We rely primarily on Python, especially the IPython notebook and pandas packages. The code is designed to be cross-platform for users, however, for developers we assume a Linux environment. R code may be used as needed, usually indirectly within wrappers.

• Configuration: we strongly recommend Anaconda, a completely free Python distribution which includes about 200 of the most useful Python packages for science, math, engineering, data analysis. It will resolve your headaches due to dependency hell.

• Dependencies: pandas > 0.15 is suggested. Python 2.7 is our base (but recent code should be python3 compatible, though there are still some outstanding external issues upstream). Our tests under the Jupyter fork of the IPython notebook has not encountered any problems thus far.

The best way to see the code in action is to run the primary notebooks in the nb directory. Some of them are described at the end of this page. (Note that GitHub can render IPython notebooks directly in the browser, however, they will not be executable.) Here is a rendering of a notebook at GitHub for Housing starts, home prices and affordibility. If you locally executed that notebook, it would also seek out the latest available data to bring the research up-to-date.

To see how we score the Federal Reserve's performance under its dual mandate for inflation and unemployment, see https://git.io/fed where the Phillips curve is discredited by constructing heat map scatter plots.

Some basic commands, e.g. get() and plot() in the fecon module, will do a lot of the heavy lifting to get you started immediately. They are designed for scripts (not necessarily within IPython notebooks) and any Python IDE interactive development environment. Our wiki should be gradually adding tutorials and FAQs for any clarifications.

These standalone Python modules are frequently imported into our Python scripts:

• yi_1tools.py : essential utility functions.
• yi_plot.py : essential plot functions.
• yi_timeseries : useful time series functions.
• yi_simulation : useful functions for simulation studies.
• yi_fred.py : Access FRED with pandas for plots and analysis.
• yi_quandl.py : Access Quandl with pandas for plots and analysis.
• yi_stocks.py : Access stocks and funds with pandas for plots and analysis.

However, for IPython notebooks and interactive sesssions, only one generalized module fecon needs to be imported. The commands are very easy to learn and customize, producing relatively sophisticated results quickly without detailed knowledge of the underlying numerical packages.

• Code revisions: please kindly follow Github flow. You are also invited to directly add commentary to existing notebooks.

• Running tests: details are in the tests directory. For integration testing, we run all notebooks in batch mode. This also syncs temporary notebooks with current data.

• Guidelines: we welcome your pull request to improve our code. Please be sure to pull origin/master and rebase beforehand.

• Documents and data for fecon235 seminars: contact us if you need help incorporating your material into this repository.

Repo admin

We examine the spread between two interest rates: LIBOR and Fed Funds. The former has a much greater depth in the futures market in terms of volume and maturity horizon, implying richer information content. Modeling their relationship, we construct a synthetic forward Fed Funds rate, useful in gauging market sentiment regarding Fed policy. Estimate is given for the change in Fed Funds rate over the next 12 months. Shortcut: https://git.io/fedfunds

The London Bullion Market Association ceased publishing daily data on their Gold Forward Offered Rate (GOFO), as of 30 January 2015 -- so we develop an observable proxy called tango using gold futures and LIBOR. This supply/demand indicator is then compared against spot prices.

Commitment of Traders Report (COTR) is useful to extract market positions in precious metals, US dollar, interest rates, and equities markets. We develop our own scale-free measures to gauge market sentiment across time which can diverge from price directionality at interesting points. Shortcut: https://git.io/cotr

Sort percentage allocation to long equities. Caveats should be noted for portfolio management. Module yi_secform.py, derived from this notebook, easily sums up 13F filings in one function.

We examine government debt in real terms, and the current debt per capita.

We examine euro FX data from the Fed Reserve FRED database. Our synthetic time-series, which takes us far back as 1971, give additional perspective to observe the cross-rates against U.S. dollar and Japanese yen.

We examine the usual suspects: unemployment, inflation, real interest rate, foreign exchange rate, comparative GDP. Appendix 1 concisely explains the euro crisis in a video.

We examine the US gross domestic product's relationship to the US equity market, in real terms. Forecasts for both are demonstrated using Holt-Winters technique. We derive the most likely range for real GDP growth, and identify excessive equity valuations aside from inflationary pressures.

For every wage dollar paid, what is GDP output? In answering this question, we derive a model for GDP growth based on observations from wage growth.

We examine economic and financial time series where Holt-Winters is used to forecast one-year ahead. Daily data for bonds, equity, and gold is then analyzed. The focus is on geometric mean returns because they optimally express mean-variance under logarithmic utility. Shortcut: https://git.io/georet

Greenspan in 2014 pointed out that there was never a recovery from recession without improvements in housing construction. Here we examine some relevant data, including the Case-Shiller series, and derive an insightful measure of the housing economy which takes affordibility into account.

We examine inflation data: CPI and PCE, including the core versions, along with the 10-year BEI rate (break-even inflation). We also examine gold returns and its correlations to inflation. A combined inflation statistic m4infl is defined, and we make some forecasts. Shortcut: https://git.io/infl

We examine unemployment and inflation data to construct a time-series which gives a numerical score to the Fed's performance on its dual mandate. The key is to find comparable units to measure performance and a suitable scalar measure to show deviation from the dual mandate. The visualization includes sequential scatter plots using color heat map, which can be extended to studies of the Phillips curve.

We examine and attempt to replicate the results of two interesting articles by Yi Wen and Maria Arias -- along the way, we take a detour and explore the connection between money velocity and bond rates. This will tie together their relationship with GDP and the money supply in a fitted equation.

We examine the history of oil prices, and their spreads. Real prices give additional insight, along with some of the statistical characteristics used in financial economics.

We examine the value of USD against a basket of 26 foreign currencies using real trade numbers. Trade statistics are released annually, however, the Fed uses international inflation data to adjust the weights monthly.

We determine how much real capital has been necessary for risk-free interest to match annual wage.

Long-term comparison of two asset classes: we boxplot their return distribution and also compute geometric mean returns. Correlation between the two is shown to be nil. We then look at the history of projected returns using Holt-Winters method, which also gives the latest forecasts. To conclude, we closely examine the relative value of the two assets in terms of gold troy ounces per equity share. Analytically short equities vs. long gold is favorable for an investor with log utility, but hardly profitable over the long haul.

Using monthly data we previously found that there is a strong correlation between gold and real rates, so we investigate this on a finer time scale. We then use this correlation to help make forecasts using the univariate Holt-Winters method.

Revision date : 2015-12-16