Friday, August 28, 2015

Python Data Analysis Library — pandas: Python Data Analysis Library

Python Data Analysis Library — pandas: Python Data Analysis Library: "pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language"

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Macroeconomic models and estimation

Angry Bear asks, How Many Equations Should There be in Macroeconomic Models ? in an assessment of the relative merits of Cowles Commission, VAR and DSGE models.  

"This post is long. The punchline is that I think that a promising approach would be to combine CC models with a pseudo prior that a good model is not too far from a standard DSGE model. This is the sort of thing done with high dimensional VARs using the so called Minnesota prior."

Part of the rational expectations, macroeconomic debate.

Thursday, August 27, 2015

Book extract: ‘The Silo Effect’, by Gillian Tett -

Book extract: ‘The Silo Effect’, by Gillian Tett -

 "First, they were organising the company into discrete project teams, dedicated groups to perform tasks. A company such as Facebook needs silos, in the sense of specialist departments and teams, simply to get its work done. Project groups were needed for focus and accountability. But the second aim of Bootcamp was to overlay those project teams with another set of informal social ties not defined by the formal department boundaries. This, it was hoped, would prevent the project teams from hardening into rigid, inward-looking groups and ensure that employees felt a sense of affiliation with the entire company, not just their tiny group. “Boot camp [can foster] cross-team communication and prevent the silos that so commonly spring up in growing engineering organisations,” Boz said. Facebook was both creating the preconditions for silos and instilling systems to break down those silos."

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CPB World Trade Monitor June 2015

CPB World Trade Monitor reports "The volume of world trade expanded 2.0% in June 2015, following a 1.3% decline in May (initial estimate: -1.2%). This is shown by the CPB World Trade Monitor."

This is an excellent resource for exercises in digging into data.  What do the data show?  How are they measured?  What theories can be used to understand more about the data here?

A Tale of Two Liquidities - Bloomberg View

A Tale of Two Liquidities - Bloomberg View: "Now in some ways this is a weird thing to say. Traditionally, what we talk about when we talk about "liquidity" is something like "a market's ability to facilitate the purchase/sale of an asset without causing a change in the asset's price." If bond prices aren't changing much, despite all the sound and fury in the equity markets, then that seems like a sign that liquidity is unusually good."

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Tuesday, August 25, 2015

Long term views

"To finish, James Surowiecki at the New Yorker finds an ounce of method in the financial madness. Why have the likes of Amazon and Netflix lost more share value than the average US stock? The answer, he submits, is that these are companies whose valuation depends on their promise of making significantly more money over the long term than they are today. So even a slight downgrade in investors’ hopes for economic growth — which a huge emerging markets financial crisis could easily induce — would justify a big adjustment in the most future-heavy companies’ value."

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Saturday, August 01, 2015

‘The Weather Experiment,’ by Peter Moore - The New York Times

‘The Weather Experiment,’ by Peter Moore - The New York Times: "Before the Royal Charter storm, FitzRoy had been agitating in London for government funding for collection of weather data. He and other Victorian men of meteorology knew that the more they could parse what the weather had done in the past, the better they could warn what it might do in the future. FitzRoy called the concept “forecasting.” To show just how ludicrous that idea seemed at the time, Moore unearths a telling 1854 Commons debate. When a scientifically enthusiastic member of Parliament suggested that amassing weather observations from sea and land could someday mean “we might know in this metropolis the condition of the weather 24 hours beforehand,” laughter broke out raucously enough to stop the proceeding."

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Why you can't trust journalism | Fusion

Why you can't trust journalism | Fusion: "But here’s the thing: it turns out that the scientific world is actually far, far ahead of the journalistic world on these matters. Yes, the world of online journalism is full of parasites, and a lot of those parasites have real value. But all that the parasites have to go on are published articles: no one is transparent about the process that created those articles. No one shows their work, and no one ever tries to replicate anything."

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