BOEの広報サイトKnowledgebankで、かつてBOE内に墓地があったことが紹介されている(H/T Mostly Economics)。

Why was there a graveyard inside the Bank of England?

The Bank of England moved to its current site on Threadneedle Street in 1734. We quickly outgrew our first building so to expand further we bought a church that was situated next-door.

The church was deconsecrated and demolished, but its graveyard was left in place. This later became the Bank’s Garden Court:

Who was the giant buried in the graveyard at the Bank?

The Bank of England’s “giant” was William Jenkins, an employee who worked here for nine years in the late 1700s.

Jenkins was 6ft 7½ inches tall (202 cm) – much taller than the average man at that time, who would have been 5ft 7 inches (170cm):

On his death in 1798, Jenkins’ friends asked permission to have his body buried in the Bank’s Garden Court.

The Bank of England’s Directors granted the request and Jenkins was buried very early one morning, before the start of the working day.

Why did Jenkins’ friends want to bury him in the Bank’s Garden Court?

Jenkins was in poor health in the weeks prior to his death.

He was concerned that because of his height, his corpse would be stolen by body snatchers following his death and sold to surgeons.

The going rate for a skeleton of this size in 1798 was in the region of 200 guineas. Because of inflation in the period since then, that would be around £25,000 in today’s money.

Jenkins’ friends argued that the Bank’s Garden Court would be the safest place for him given the risk of being taken by body snatchers – which was something that had made him “considerably disturbed in his mind” before his death.

Where is the Bank’s giant now?

The Bank was completely rebuilt in the 1920s and ‘30s, and Jenkins’s coffin was found when the Garden Court was dug up. Along with the other coffins found, it was moved to Nunhead Cemetery near Peckham, South London. However, Jenkins’ coffin proved to be too long to fit in the vaults there, so arrangements were made for it to be placed in the catacombs:

So there are no longer any graves in or under our Garden Court today. At least, not to our knowledge…





















というNBER論文をアラン・ブラインダーらが書いているungated版原題は「Necessity as the Mother of Invention: Monetary Policy after the Crisis」)。著者はAlan Blinder(プリンストン大)、Michael Ehrmann(ECB)、Jakob de Haan(オランダ銀行、フローニンゲン大)、David-Jan Jansen(オランダ銀行)。


We ask whether recent changes in monetary policy due to the financial crisis will be temporary or permanent. We present evidence from two surveys—one of central bank governors, the other of academic specialists. We find that central banks in crisis countries are more likely to have resorted to new policies, to have had discussions about mandates, and to have communicated more. But the thinking has changed more broadly—for instance, central banks in non-crisis countries also report having implemented macro-prudential measures. Overall, we expect central banks in the future to have broader mandates, use macro-prudential tools more widely, and communicate more actively than before the crisis. While there is no consensus yet about the usefulness of unconventional monetary policies, we expect most of them will remain in central banks’ toolkits, as governors who gain experience with a particular tool are more likely to assess that tool positively. Finally, the relationship between central banks and their governments might well have changed, with central banks “crossing the line” more often than in the past.






昨日紹介したFrancis Dieboldの3連エントリの2番目にHal Varianがコメントし、Dieboldが指摘した問題――機械学習因果関係の無い予測に重点を置くが、計量経済学因果関係のある予測に重点を置く――について自分が以前書いた論文を2篇紹介している。一つ機械学習に詳しい人向けで、もう一つ経済学者向けとの由。

以下は前者の論文「Causal inference in economics and marketing」の要旨。

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.



以下は後者の論文「Big Data: New Tricks for Econometrics」の一節。

There are a number of areas where there would be opportunities for fruitful collaboration between econometrics and machine learning. I mentioned above that most machine learning uses independent and identically distributed data. However, the Bayesian Structural Time Series model shows that some of these techniques can be adopted for time series models. It is also possible to use machine learning techniques to look at panel data, and there has been some work in this direction.

However, the most important area for collaboration involves causal inference. Econometricians have developed several tools for causal inference such as instrumental variables, regression discontinuity, difference-in-differences, and various forms of natural and designed experiments (Angrist and Krueger 2001). Machine learning work has, for the most part, dealt with pure prediction. In a way, this is ironic, since theoretical computer scientists, such as Pearl (2009a, b) have made significant contributions to causal modeling. However, it appears that these theoretical advances have not as yet been incorporated into machine learning practice to a significant degree.



しかし、両者が協力すべき最も重要な分野は因果関係の推定に関するものである。計量経済学者は、操作変数、回帰不連続デザイン、差の差分析、および、様々な形の自然実験や計画された実験のように、因果関係の推定のためのツールを幾つか開発してきた(Angrist and Krueger 2001)。機械学習は、大体において、純粋な予測を扱ってきた。ある意味で、これは皮肉なことである。というのは、Pearl(2009a *1, b)のような理論コンピュータ科学者因果関係のモデル化に顕著な貢献をしてきたからである。にも関わらず、そうした理論的進展は未だに機械学習の実務にそれほどは浸透していないように思われる。


統計的因果推論 -モデル・推論・推測-

統計的因果推論 -モデル・推論・推測-




についてFrancis Dieboldが3つのエントリに亘って論じている(ここここここ;H/T Economist’s View)。





というブログエントリをクリス・ディローが書いている(原題は「Economics as literature」)。これは、(8日エントリで紹介した)Avner Offerが、アトランティック誌のインタビューで、経済学物理学よりも文学に近い(more like literature than like physics)、と述べたのに反応したもの。

...economics is more like literature than physics in that it doesn’t always progress. We can read 19th century novels with profit, but few physicists would advise their students to study 19th century work on the subject. Likewise, we should read the classical economists, not least because they were interested in issues with much of later economics overlooked, such as the distribution of income between capital and labour.

And some economics is like literature in a bad way. Those RBC models that assume continuous labour market clearing are like Iain M. Banks’ culture novels: they describe societies which do not exist, have not existed and will not exist in our lifetimes. And a common criticism of at least early versions of DSGE models was that they were as scrupulous in ignoring important matters as Jane Austen was in ignoring the source of her gentlemen’s incomes: Charles Goodhart said of the DSGE approach that “it excludes everything I am interested in.”

Nevertheless, there are two ways in which economics is and should be like literature.

One is that it asks the same question as writers do of their characters: given his motives, information set and constraints, how does he act and with what effects? Sloppy writers and economists give simplistic and implausible answers – as in the “incentives-magic!!-nice effects” approach of simple-minded free market economics. Great writers and economists, however, are much more careful and insightful. You can think of the recent Nobel prizes given to Hart, Bengstrom and Tirole as rewards for such carefulness.

Secondly, economics must be unlike physics because there are, as Jon Elster said, no (or few) law-like generalizations in the social sciences. Instead, like literature, there can only be detailed studies of time and place – although the best such studies yield great insights.

Does this mean that economics isn’t a “science”? I side with McCloskey. It’s a stupid question. What matters is whether the study is careful and disciplined? The objection to “mathy” economics is that it doesn’t bother with the discipline imposed by pesky facts.

In this sense, economics is like literature in that in both there is a constrained subjectivity. Literary scholars might reasonably differ on whether, say, Virginia Woolf was a better writer than D.H Lawrence but most would agree that both are better than, say, Louise Bagshawe. Likewise, whilst there might be disagreement in economics, there can also be unity or at least general consensus about what constitutes rank bad economics.





一つは、小説家が登場人物に尋ねるのと同じ質問を尋ねる、という点である。動機、情報集合、および制約を所与として、彼はどのように行動し、それはどんな結果をもたらすだろうか? いい加減な小説家経済学者は、単純化された説得力の無い回答を与える。単純な自由市場経済学の「動機ー魔法!!−素晴らしい結果」アプローチがその一例である。一方、偉大な小説家経済学者はもっと注意深く、洞察力に富んでいる。そうした注意深さへの報酬として、最近ハート、ベングストロム*3ティロールに与えられたノーベル賞のことが想起されよう。


このことは、経済学は「科学」ではないことを意味するのだろうか? この点について私はマクロスキー*4に同意する。それは馬鹿げた質問なのだ。重要なのは、研究が注意深く行われ、統制が取れているか、である。「数学もどき」経済学への抗議は、煩わしい事実によって課される統制を気に掛けていない、という点に向けられている。


*1cf. イアン・バンクス - Wikipedia

*2cf. ここ



Knowledge and Persuasion in Economics

Knowledge and Persuasion in Economics

*5cf. Louise Mensch - Wikipedia関連日本語ブログ記事