Efficient Cointegration Test In Python Stack Overflow

Leo Migdal

-Dec 4, 2025, 3:46 AM

efficient cointegration test in python stack overflow

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Learn more Bring the best of human thought and AI automation together at your work. I have two time series X and Y of length ~2 million. I have a parameter window_size which is an integer, of the order of thousands. I want to compute the compute whether each rolling window of X and Y is cointegrated or not, for which I am using the p-values from the statsmodels.tsa.stattools.coint function. So essentially, I need to compute the p-values for the cointegration test for the arrays (X[0:window_size], Y[0:window_size]), (X[1:window_size+1], Y[1:window_size+1])….

The code to do that is: I am wondering if there is a better way to test if two variables are cointegrated than the following method: The above method works; however, it is not very efficient. When I run sm.OLS, a lot of things are calculated, not just the residuals, this of course increases the run time. I could of course write my own code that calculates just the residuals, but I don't think this will be very efficient either. I looking for either a build in test that just tests for cointegration directly.

I was thinking Pandas, but don't seem to be able to find anything. Or maybe there is a clever to test for cointegration without running a regression, or some efficient method. I have to run a lot of cointegration tests, and it would nice to improve on my current method. Methods of Testing for CointegrationThe Engle-Granger Two-Step method starts by creating residuals based on the static regression and then testing the residuals for the presence of unit-roots. It uses the Augmented Dickey-Fuller Test (ADF) or other tests to test for stationarity units in time series. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work. Bring the best of human thought and AI automation together at your work. Learn more Bring the best of human thought and AI automation together at your work. I have three time series df['a'], df['b'] and df['c'] which I want to test for cointegration using the statsmodels.tsa.vector_ar.vecm.coint_johansen function (and obtain a cointegration vector).

Last modified: Jan 26, 2025 By Alexander Williams Cointegration is a key concept in time series analysis. It helps identify long-term relationships between variables. The coint() function in Python's Statsmodels library is a powerful tool for this purpose. This guide will walk you through the basics of using coint(). You'll learn its syntax, how to interpret results, and see practical examples.

Let's dive in! Cointegration refers to a statistical relationship between two or more time series. Even if individual series are non-stationary, their linear combination can be stationary. This implies a long-term equilibrium relationship. For example, stock prices and dividends may be cointegrated. While both series may trend over time, their relationship remains stable.

Cointegration is crucial in econometrics and finance. Python implementation of the Johansen test for cointegration This package requires scipy, which in turn requires blas, lapack, atlas, and gfortran. These can be installed on a Ubuntu system with: See examples folder for a jupyter notebook with example usage. The cases when the chosen model (in the language of MacKinnon 1996) is 1* or 2* have not yet been fully implemented.

They will be completed in the near future. Cointegration analysis is a sophisticated statistical approach widely used in the field of financial time series analysis. It’s particularly useful for identifying and analyzing the long-term relationship between two or more time series. This technique is essential in the field of finance, where it’s used to model and predict market behavior, assess risk, and inform investment strategies. The concept of cointegration becomes particularly important in the study of pairs trading, hedge funds strategies, and risk management. Cointegration is a statistical property of a collection (two or more) of time series variables.

Two or more time series are cointegrated if they share a common stochastic drift. In simpler terms, while each series can wander randomly over time, if they are cointegrated, there is a constant equilibrium mechanism that ties them together. This is crucial in finance, where asset prices are often non-stationary (their statistical properties change over time) but can be bound together by economic or market forces. Firstly, consider two time series, and . These series are said to be non-stationary if their mean, variance, or covariance with respect to time (t) is not constant. This is typically modeled as a unit root process, often checked using unit root tests like the Augmented Dickey-Fuller (ADF) test.

Two or more time series and are cointegrated if: In other words, there exists a vector (a, b) such that the time series is stationary. Communities for your favorite technologies. Explore all Collectives Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work.

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Efficient Cointegration Test In Python Stack Overflow

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