Lies, Damned Lies, And Statistics? Examples From Finance & Economics

Karim M. Abadir

Imperial College Business School

December 30, 2015

CEJEME 5: 231-248 (2013)

Abstract:

As the saying goes, lies can be classified into the following, from bad to worse: lies, damned lies, and statistics (the worst of all). But is this saying really true? Many puzzles in finance and economics have accumulated over the years. Seemingly reasonable economic theories have been badly rejected by the data, giving rise to suspicions that something is wrong, and not just with the theories. What if the data contained some hidden features that, if neglected, could be misleading the analysis? What if these features required new statistical tools to bring them out and handle them? With a few examples collected from my research with colleagues, this paper will show how the conclusions could be quite different from what was originally thought.

Lies, Damned Lies, And Statistics? Examples From Finance & Economics – Introduction

As the saying goes, lies can be classified into the following, from bad to worse: lies, damned lies, and statistics (the worst of all). But is this saying really true? Many puzzles in finance and economics have accumulated over the years. Seemingly reasonable economic theories have been badly rejected by the data, giving rise to suspicions that something is wrong, and not just with the theories. What if the data contained some hidden features that, if neglected, could be misleading the analysis? What if these features required new statistical tools to bring them out and handle them? With a few examples collected from my research with colleagues, this paper will show how the conclusions could be quite different from what was originally thought. Reliable data analysis is one of the hardest tasks in sciences and social sciences. The need for it is pervasive, as this quote from John Tukey implies:

“The best thing about being a statistician is that you get to play in everyone’s backyard.”

A host of modern statistical tools are constantly being developed to cope with a wealth of new data coming from different disciplines, but some methods of analysis need to be tailored to cope with different environments. (Not all ailments are cured by the same medicine!) For example, econometrics has been responsible for many developments in statistical tools and methodology, especially in time series. The “backyards” that this paper will focus on are financial econometrics and empirical macroeconomics. In particular, we will touch on the following areas:

1. Statistical distribution theory: how to quantify randomness? Option-pricing requires the specification of a distribution of likely future prices. An arbitrarilychosen functional form will give misleading results. Though from a different field of statistical analysis, the following quote from Light, Singer, and Willett (1990) illustrates what could go wrong if the chosen functional form is not flexible enough:

“You can’t fix by analysis what you bungled by design.”

2. The relation between interest rates on different maturities, now and in the future: the term structure of interest rates.

(a) Rare uncharacteristic events can generate one-off “outliers”. They have distortionary effects on traditional methods of estimating relations between these interest rates.

(b) Short-term interest rates do not have the “Markovian” dynamics that prevail in the literatures on finance and time series. We first need new methods to deal with such data.

3. These new dynamics do not come out of thin air. They arise from a general-equilibrium economic model. Because of this link, it turns out that exchange rates, stock market indexes, and all macroeconomic variables are accurately characterized by this new process. This has implications for trading (momentum, cycles, etc.), but also for the ever important question of macroeconomic stabilization. The recent recession and recovery patterns have been predicted by using this approach, which has unfortunately still not made it into the toolkit of policymakers, whether central bankers or finance ministries.

Finance & Economics

Finance & Economics

Finance & Economics

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