Granger Causality

"Granger causality is a standard linear technique for determining whether one time series is useful in forecasting another." (Irwin and Sanders, 2011).

A series 'granger' causes another series if it consistently predicts it. If series X granger causes Y, while we can't be certain that this relationship is causal in any rigorous way, we might be fairly certain that Y doesn't cause X.

Example:

Yt = B0 + B1*Yt-1 +... Bp*Yt-p + A2*Xt-1+.....+Ap*Xt-p + Et

if we reject the hypothesis that all the 'A' coefficients jointly = 0  then 'X' granger causes 'Y'

Xt = B0 + B1*Xt-1 +... Bp*Xt-p + A2*Yt-1+.....+Ap*Yt-p + Et

if we reject the hypothesis that all the 'A' coefficients jointly = 0 then 'Y' granger causes 'X'

Applications:

Below are some applications where granger causality methods were used to test the impacts of index funds on commodity market price and volatility.

The Impact of Index Funds in Commodity Futures Markets:A Systems Approach
DWIGHT R. SANDERS AND SCOTT H. IRWIN
The Journal of Alternative Investments
Summer 2011, Vol. 14, No. 1: pp. 40-49

Irwin, S. H. and D. R. Sanders (2010), “The Impact of Index and Swap Funds on Commodity Futures Markets: Preliminary Results”, OECD Food, Agriculture and Fisheries Working Papers, No. 27, OECD Publishing. doi: 10.1787/5kmd40wl1t5f-en

Index Trading and Agricultural Commodity Prices:
A Panel Granger Causality Analysis
Gunther Capelle-Blancard and Dramane Coulibaly
CEPII, WP No 2011 – 28
No 2011 – 28
December

References:

Using Econometrics: A Practical Guide (6th Edition) A.H. Studenmund. 2011

Confidence Intervals: Fad or Fashion

Confidence intervals seem to be the fad among some in pop stats/data science/analytics. Whenever there is mention of p-hacking, or the ills of publication standards, or the pitfalls of null hypothesis significance testing, CIs almost always seem to be the popular solution.

There are some attractive features of CIs. This paper provides some alternative views of CIs, discusses some strengths and weaknesses, and ultimately proposes that they are on balance superior to p-values and hypothesis testing. CIs can bring more information to the table in terms of effect sizes for a given sample however some of the statements made in this article need to be read with caution. I just wonder how much the fascination with CIs is largely the result of confusing a Bayesian interpretation with a frequentist application or just sloppy misinterpretation. I completely disagree that they are more straight forward to students (compared to interpreting hypothesis tests and p-values as the article claims).

Dave Giles gives a very good review starting with the very basics of what is a parameter vs. an estimator vs. an estimate, sampling distributions etc. After reviewing the concepts key to understanding CIs he points out two very common interpretations of CIs that are clearly wrong:

1) There's a 95% probability that the true value of the regression coefficient lies in the interval [a,b].

2) This interval includes the true value of the regression coefficient 95% of the time.

"we really should talk about the (random) intervals "covering" the (fixed) value of the parameter. If, as some people do, we talk about the parameter "falling in the interval", it sounds as if it's the parameter that's random and the interval that's fixed. Not so!"

In Robust misinterpretation of confidence intervals, the authors take on the idea that confidence intervals offer a panacea for interpretation issues related to null hypothesis significance testing (NHST):

"Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual...Our findings suggest that many researchers do not know the correct interpretation of a CI....As is the case with p-values, CIs do not allow one to make probability statements about parameters or hypotheses."

The authors present evidence about this misunderstanding by presenting subjects with a number of false statements regarding confidence intervals (including the two above pointed out by Dave Giles) and noting the frequency of incorrect affirmations about their truth.

In Mastering 'Metrics, Angrist and Pishcke give a great interpretation of confidence intervals that doesn't lend itself in my opinion as easily to abusive probability interpretations:

"By describing a set of parameter values consistent with our data, confidence intervals provide a compact summary of the information these data contain about the population from which they were sampled"

Both hypothesis testing and confidence intervals are statements about the compatibility of our observable sample data with population characteristics of interest. The ASAreleased a set of clarifications on statements on p-values. Number 2 states that "P-values do not measure the probability that the studied hypothesis is true." Nor does a confidence interval (again see Ranstan, 2014).

Venturing into the risky practice of making imperfect analogies, take this loosely from the perspective of criminal investigations. We might think of confidence intervals as narrowing the range of suspects based on observed evidence, without providing specific probabilities related to the guilt or innocence of any particular suspect. Better evidence narrows the list, just as better evidence in our sample data (less noise) will narrow the confidence interval.

I see no harm in CIs and more good if they draw more attention to practical/clinical significance of effect sizes. But I think the temptation to incorrectly represent CIs can be just as strong as the temptation to speak boldly of 'significant' findings following an exercise in p-hacking or in the face of meaningless effect sizes. Maybe some sins are greater than others and proponents feel more comfortable with misinterpretations/overinterpretations of CIs than they do with misinterpretations/overinterpretaions of p-values.

Or as Briggs concludes about this issue:

"Since no frequentist can interpret a confidence interval in any but in a logical probability or Bayesian way, it would be best to admit it and abandon frequentism"

See also: 
Andrew Gelman: The Fallacy of Placing Confidence in Confidence Intervals.
Noah Smith: The Backlash to the Backlash Against P-values

References:

Methods of Psychological Research Online 1999, Vol.4, No.2 © 1999 PABST SCIENCE PUBLISHERS Confidence Intervals as an Alternative to Significance Testing Eduard Brandstätter1 Johannes Kepler Universität Linz

J. Ranstam, Why the -value culture is bad and confidence intervals a better alternative, Osteoarthritis and Cartilage, Volume 20, Issue 8, 2012, Pages 805-808, ISSN 1063-4584, http://dx.doi.org/10.1016/j.joca.2012.04.001 (http://www.sciencedirect.com/science/article/pii/S1063458412007789)

Robust misinterpretation of confidence intervals
Rink Hoekstra & Richard D. Morey & Jeffrey N. Rouder &
Eric-Jan Wagenmakers Psychon Bull Rev
DOI 10.3758/s13423-013-0572-3 2014