In recent years, the use of randomised controlled trials has spread from labour market and welfare programme evaluation to other areas of economics (and to other social sciences), perhaps most prominently in development and health economics. This column argues that some of the popularity of such trials rests on misunderstandings about what they are capable of accomplishing, and cautions against simple extrapolations from trials to other contexts.

# Very brief notes on measures: From σ-fields to Carathéodory’s Theorem

**Definition 1.** A -field is a non-empty collection of subsets of the sample space closed under the formation of complements and countable unions (or equivalently of countable intesections – note ). Hence is a -field if

whenever

whenever

**Definition 2. Set functions and measures**. Let be a set and be an algebra on , and let be a non-negative set function

- is
**additive**if and, for , - The map is called
**countably additive**(or -additive) if and whenever is a sequence of disjoint sets in with union in , then - Let be a measurable space, so that is a -algebra on .
- A map is called a
**measure**on if is countable additive. The triple is called a**measure space**. -
The measure is called

**finite**ifand –

**finite**if, () s.th.

- Measure is called a
**probability measure**if and is then called a**probability triple**. - An element of is called -null if .
- A statement about points of is said to hold
**almost everywhere**(a.s.) if

** Continue reading “Very brief notes on measures: From σ-fields to Carathéodory’s Theorem” **

# David Aldous’ review of the Black Swan

The phrase “Black Swan” (arising earlier in the different context of Popperian falsification) is here defined as an event characterized [p. xviii] by rarity, extreme impact, and retrospective (though not prospective) predictability, and Taleb’s thesis is that such events have much greater effect, in financial markets and the broader world of human affairs, than we usually suppose. The book is challenging to review because it requires considerable effort to separate the content from the style. The style is rambling and pugnacious—well described by one reviewer as “with few exceptions, the writers and professionals Taleb describes are knaves or fools, mostly fools. His writing is full of irrelevances, asides and colloquialisms, reading like the conversation of a raconteur rather than a tightly argued thesis”. And clearly this is perfectly deliberate. Such a book invites a review that reflects the reviewer’s opinions more than is customary in the Notices. My own overall reaction is that Taleb is sensible (going on prescient) in his discussion of financial markets and in some of his general philosophical thought but tends toward irrelevance or ridiculous exaggeration otherwise.

# Rio’s Inequality

Let and be two integrable real-valued random variables and let be the quantile function of . Then if is integrable over we have

where is the a-mixing coefficient.

* Proof:* Set and then

since and

note also that

which implies that

# Kolmogorov’s Maximal Inequality

- Let be independent random variables with . Set . Then
*Proof:*LetNotice that and

# LAN for Linear Processes

Consider a m-vector linear process

where are i.i.d. m-vector random variables with p.d.f. on , are matrices depending on a parameter vector .

Set

Assume the following conditions are satisfied

**A1** i) For some

where denotes the sum of the absolute values of the entries of .

ii) Every is continuously two times differentiable with respect to , and the derivatives satisfy

for where .

iii) for and can be expanded as follows:

where , satisfy

iv) Every is continuously two times differentiable with respect to , and the derivatives satisfy

for

**A2** satisfies

**A3** The continuous derivative of exists on .

**A4**

where .

From A1 the linear process can be expressed as

and hence

where