No Hesitations at 500K

Some company just emailed to inform me that No Hesitations had made its list of the Top 100 Economics Blogs.  I was pretty happy until I decided that there were probably only 70 or 80 economics blogs.

But seriously, thanks a lot for your wonderful support.  No Hesitations has about 500,000 pageviews since launching in summer 2013, and the trend (below) looks good.  The time has flown by, and I look forward to continuing.

Listening to Your Sentences, II

Here's a continuation of this recent post (for students) on listening to writing.

OK, you say, Martin Amis interviews are entertaining, but Martin Amis is not a mere mortal, so what's the practical writing advice for the rest of us? Read this, from Gary Provost (evidently the highlighting is keyed to different sentence lengths):

Martin Amis on How to Write a Great Sentence

It's been a while since I did a piece on good writing, for students.   In an old post I said "Listen to your words; push your prose toward poetry."  That's perhaps a bit much -- you don't need to write poetry, but you do need to listen to your writing. 

On the listening theme, check out this Martin Amis clip, even if I don't see why you shouldn't repeat prefixes or suffixes in the same sentence (in fact I think the repetition can sometimes be poetic, a sort of alliteration, when done tastefully).  And while you're at it, take a look at this marvelous older clip too.

Hazard Functions for U.S. Expansions

Glenn Rudebusch has a very nice 2016 FRBSF Letter, "Will the Economic Recovery Die of Old Age?".  He draws on perspective and results from our joint work of 25 years ago (including a paper we did with Dan Sichel -- see below), and he applies them to the present expansion.  He correctly emphasizes that U.S. expansion hazard functions are basically flat, so "old" expansions are no more likely to end than "young" ones. That's of some comfort, since the present expansion, which started in mid-2009, is getting long in the tooth!

Actually, the flat expansion hazard is only for post-WWII expansions; the prewar expansion hazard is sharply increasing. Here's how they compare (copied from Glenn's FRBSFLetter):

Probability of an Expansion ending within a month

Perhaps the massive difference is due to "good policy", that is, post-war policy success in "keeping expansions alive".  Or perhaps it's just "good luck" -- but it's so big and systematic that luck alone seems an unlikely explanation.

For more on all this, and to see the equally-fascinating and very different results for recession hazards, see Diebold, Rudebusch and Sichel (1992), which I consider to be the best statement of our work in the area.

[Footnote:  I wrote this post about three days ago, intending to release it next week. I just learned that The Economist (May 21st issue) also reports on the Rudebusch FRBSF Letter (see http://www.economist.com/news/finance-and-economics/21699124-when-periods-economic-growth-come-end-old-age-rarely-blame-murder), so I'm releasing it early.  Interesting that both The Economist and I are not only slow -- Glenn sent me his Letter in February, when it was published! -- but also identically slow.]

R/Finance 2016: Applied Finance with R

At R/Finance 2016: Applied Finance with R.  Interesting group, with many constituencies, and interesting program, which appears below (or go to http://www.rinfinance.com/agenda/).

Friday, May 20th, 2016
08:00 - 09:00	Optional Pre-Conference Tutorials
	Ross Bennett: Feasible Space Analysis and Hierarchical Optimization with PortfolioAnalytics
	Dirk Eddelbuettel: Introduction to Rcpp and RcppArmadillo
	Doug Service: Leveraging Azure Compute from R
	T. Harte + M. Weylandt: Modern Bayesian Tools for Time Series Analysis
09:00 - 09:30	Registration (2nd floor Inner Circle) & Continental Breakfast (3rd floor by Sponsor Tables)
	Transition between seminars
09:30 - 09:35	Kickoff
09:35 - 09:40	Sponsor Introduction
09:40 - 10:20	Rishi Narang: Rage Against the Machine Learning
10:20 - 10:50	Robert McDonald: The derivmkts package
	Piotr Orłowski: Modeling Divergence Swap Rates
	Jerzy Pawlowski: Exploring Higher Order Risk Premia Using High Frequency Data
	Majeed Simaan: The Implicit Value of Tracking the Market
	Kris Boudt: Block rearranging elements within matrix columns to minimize the variability of the row sums
10:50 - 11:20	Break
11:20 - 11:40	Brian Boonstra: Calibrating Parsimonious Models Of Equity-Linked Default Intensity
11:40 - 12:00	Matthew Ginley: Simulation of Leveraged ETF Volatility Using Nonparametric Density Estimation
12:00 - 12:20	Klaus Spanderen: Calibration of the Heston Local Stochastic Volatility Model
12:20 - 13:25	Lunch
13:25 - 14:05	Tarek Eldin: Random Pricing Errors and Systematic Returns: The Flaw in Fundamental Prices
14:05 - 14:25	Sanjiv Das: An Index-Based Measure of Liquidity
14:25 - 14:45	Ryan Hafen: Interactively Exploring Financial Trades in R
14:45 - 15:03	Nidhi Aggarwal: The causal impact of algorithmic trading on market quality
	Chirag Anand: Liquidity provision in a high-frequency environment
	Maria Belianina: OneTick and R
	Patrick Howerter: Connecting QAI to R
15:09 - 15:40	Break
15:40 - 16:00	Marc Wildi: Monitoring the US Economy: a System of Timely (Real-Time Daily Mixed-Frequency) Indicators
16:00 - 16:06	Sile Li: Constructing US Employment Google Search Index by Applying Principal Component Analysis
16:06 - 16:12	Doug Martin: Information Ratio Maximizing Fundamental Factor Models
16:12 - 16:18	Robert Franolic: Eyes on FX
16:18 - 16:24	Warren Durrett: Comparing Private Equity Managers Using an Objective, Data-Driven Approach
16:24 - 17:04	Frank Diebold: Estimating Global Bank Network Connectedness
17:04 - 17:10	Information about reception and dinner
17:10 - 19:10	Conference Reception
19:10 - 19:30	(Optional) Transfer to Conference Dinner
19:30 -	(Optional) Conference Dinner (Riverside Room and Gallery at Trump Hotel)
Saturday, May 21st, 2016
08:00 - 09:00	Coffee/ Breakfast
09:00 - 09:05	Kickoff
09:05 - 09:35	Hsiu-lang Chen: Do Mutual Funds Exploit Information from Option Prices for Equity Investment?
	Kyle Balkissoon: A Practitioners analysis of the overnight effect
	Mark Bennett: Measuring Income Statement Sharpe Ratios using R
	Mark Bennett: Implementation of Value Strategies using R
	Colin Swaney: Evaluating Fund Manager Skill: A Mixture Model Approach
09:35 - 09:55	Bernhard Pfaff: Portfolio Selection with Multiple Criteria Objectives
09:55 - 10:15	Douglas Service: Quantitative Analysis of Dual Moving Average Indicators in Automated Trading Systems
10:15 - 10:45	Marjan Wauters: Smart beta and portfolio insurance: A happy marriage?
	Michael Kapler: Tax Aware Backtest Framework
	Miller Zijie Zhu: Backtest Graphics
	Laura Vana: Portfolio Optimization Modeling
	Ilya Kipnis: Hypothesis Driven Development: An Understandable Example
10:45 - 11:05	Break
11:05 - 11:25	Mark Seligman: Controlling for Monotonicity in Random Forest Regressors
11:25 - 11:45	Michael Kane: glmnetlib: A Low-level Library for Regularized Regression
11:45 - 12:05	Xiao Qiao: A Practitioner's Defense of Return Predictability
12:05 - 13:05	Lunch
13:05 - 13:45	Patrick Burns: Some Linguistics of Quantitative Finance
13:45 - 14:05	Eran Raviv: Forecast combinations in R using the ForecastCombinations package
14:05 - 14:35	Kjell Konis: Comparing Fitted Factor Models with the fit.models Package
	Steven Pav: Madness: a package for Multivariate Automatic Differentiation
	Paul Teetor: Are You Trading Mean Reversion or Oscillation?
	Pedro Alexander: Portfolio Selection with Support Vector Regression
	Matthew Dixon: Seasonally-Adjusted Value-at-Risk
14:35 - 15:05	Break
15:05 - 15:25	Bryan Lewis: R in Practice
15:25 - 15:45	Matt Dziubinski: Getting the most out of Rcpp: High-Performance C++ in Practice
15:45 - 16:09	Mario Annau: h5 - An Object Oriented Interface to HDF5
	Robert Krzyzanowski: Syberia: A development framework for R
	Dirk Eddelbuettel: Rblapi Revisited: One Year Later
	Matt Brigida: Community Finance Teaching Resources with R/Shiny
16:09 - 16:29	Jason Foster: Multi-Asset Principal Component Regression using RcppParallel
16:29 - 16:49	Qiang Kou: Deep learning in R using MxNet
16:49 - 17:04	Prizes and Feedback
17:04 - 17:09	Conclusion
17:09 - 17:19	Transition to Jak's
17:19 -	Post-conference Drinks at Jak's Tap

Statistical Machine Learning Circa 1989

I've always been a massive fan of statisticians whose work is rigorous yet practical, with emphasis on modeling. People like Box, Cox, Hastie, and Tibshirani obviously come to mind.  So too, of course, do Leo Brieman and Jerry Friedman.  

I had the good luck to stumble into a week-long intensive lecture series with Jerry Friedman in 1989, a sort of summer school for twenty-something assistant professors and the like.  At the time I was a young economist in DC at the Federal Reserve Board, and the lectures were just down the street at GW.

I thought I would attend to learn some non-parametrics, and I definitely did learn some non-parametrics.  But far more than that, Jerry opened my eyes to what would be unfolding for the next half-century -- flexible, algorithmic, high-dimensional methods -- the statistics of "Big Data" and "machine learning".  

I just found the binder containing his lecture notes.  The contents appear below.  Read the opening overview, "Modern Statistics and the Computer Revolution".  Amazingly prescient.  Remember, this was 1989!

[Side note:  There I also had the pleasure of first meeting Bob Stine, who has now been my esteemed Penn Statistics colleague for more than 25 years.]

Great Yield Curve Graphic

I'm giving an overview lecture today on certain aspects of yield curves and their modeling, which reminds me of this phenomenal NYT interactive graphic.  CLICK HERE to get going, and give it time to load.  Then click "next" to go through nine fascinating graphics, ending with Germany and Japan.  You can also grab and rotate each graphic with your mouse.

On the Origin of "Forecasts"

The word forecasts, that is. From the BBC Magazine, http://www.bbc.com/news/magazine-32483678:

One hundred and fifty years ago Admiral Robert FitzRoy, the celebrated sailor and founder of the Met Office, took his own life. One newspaper reported the news of his death as a "sudden and shocking catastrophe". Today FitzRoy is chiefly remembered as Charles Darwin's taciturn captain on HMS Beagle, during the famous circumnavigation in the 1830s. But in his lifetime FitzRoy found celebrity not from his time at sea but from his pioneering daily weather predictions, which he called by a new name of his own invention - "forecasts".

And quite apart from the origin of the term, the description of the early development of weather forecasting is fascinating.

[Thanks to Glenn Rudebusch for bringing this to my attention.]

Safe Assets

Gary Gorton has a fascinating new paper, "History and Economics of Safe Assets", which contains the quote of the week: "...almost all of human history can be written as the search for and the production of different forms of safe assets".  Not sure that's the first cut I'd take at "all of human history", but it's certainly an interesting perspective.  As the old maxim says: "When you have a hammer, everything looks like a nail".

Unsung Hero: NBER Conference on Research in Income and Wealth

Here's to the the NBER's ongoing Conference on Research in Income and Wealth (CRIW), unsung hero, home of down-and-dirty measurement mavens since 1935.  Yes, since 1935!  Check out Chuck Holten's fascinating CRIW description in the NBER Reporter, and the full list of associated CRIW volumes published. What a stunning record of steady service.

FYI a typical program (in this case, from last summer) appears below.

NATIONAL BUREAU OF ECONOMIC RESEARCH, INC.

SI 2015 NBER/CRIW Workshop

Susanto Basu, Nicholas Bloom, Carol Corrado and Charles R. Hulten, Organizers

July 13-14, 2015

Charles B Room Royal Sonesta Hotel 40 Edwin H. Land Blvd. Cambridge, MA

PROGRAM

Monday, July 13:
8:30 am	Continental breakfast
9:00 am	Thomas Piketty, Paris School of Economics Emmanuel Saez, University of California at Berkeley and NBER Gabriel Zucman, London School of Economics Distributional National Accounts: Methods and Estimates for the United States Since 1913 Discussant: John Sabelhaus, Federal Reserve Board
10:00 am	Break
10:30 am	Jae Song, Social Security Administration David J. Price, Stanford University Fatih Guvenen, University of Minnesota and NBER Nicholas Bloom, Stanford University and NBERTill M. von Wachter, University of California at Los Angeles and NBER Firming Up Inequality Discussant:  Johannes Schmieder, Boston University
11:15 am	Phillipe Aghion, Harvard University and NBERUfuk Akcigit, University of Pennsylvania and NBER Antonin Bergeaud, Banque de France Richard Blundell, University College London David Hemous, INSEAD Innovation and Top Income Inequality Discussant: Scott Stern, Massachusetts Institute of Technology and NBER
12:00 pm	Lunch
1:00 pm	Wolfgang Keller, University of Colorado and NBER Hale Utar, Bielefeld University International Trade and Job Polarization: Evidence at the Worker Level Discusant: David Autor, MIT and NBER
1:45 pm	Dongya Koh, University of Arkansas Raul Santaeulalia-Llopis, Washington University in St. Louis Yu Zheng, City University of Hong Kong Labor Share Decline and the Capitalization of Intellectual Property Products Discussant: Dan Sichel, Wellesley College and NBER
2:30 pm	Break
3:00 pm	Maggie R. Jones, U.S. Census Bureau Measuring the Effects of the Tipped Minimum Wage Using W-2 Data
3:30 pm	Muge Adalet McGowan, Organisation de Coopération et de Développement Économiques(OCDE) Dan R. Andrews, Organisation de Coopération et de Développement Économiques(OCDE)Labour Market Mismatch and Labour Productivity: Evidence from PIAAC Data
4:00 pm	Adjourn
Tuesday, July 14:
8:30 am	Continental breakfast
9:00 am	Carol Corrado, The Conference Board Jonathan Haskel, Imperial College London Cecilia Jona-Lasinio, LUISS University of Rome Bilal Nasim, Institute of EducationIs International R&D Tax Competition a Zero-sum Game? Evidence from the EU  Discussant: Bronwyn Hall, University of California, Berkeley and NBER
9:45 am	Antonio Falato, Federal Reserve Board Jae Sim, Federal Reserve Board Why Do Innovative Firms Hold So Much Cash? Evidence from Changes in State R&D Tax Credits Discussant: Daniel Wilson, Federal Reserve Bank of San Francisco
10:30 am	Break
11:00 am	Neil Thompson, Massachusetts Institute of Technology Moore’s Law goes Multicore: The Economic Consequences of a Fundamental Change in how Computers work Discussant: Chris Forman, Georgia Institute of Technology
11:45 am	John Bai, University of Southern California Daniel Carvalho, University of Southern California Gordon Phillips, University of Southern CaliforniaThe Impact of Bank Credit on Labor Reallocation and Aggregate Industry Productivity Discussant: Javier Miranda, Census Bureau
12:30 pm	Lunch
Joint Session with Macro Productivity:
1:30 pm	Gita Gopinath, Harvard University and NBER Sebnem Kalemli-Ozcan, University of Maryland and NBER Loukas Karabarbounis, University of Chicago and NBER Carolina Villegas-Sanchez, ESADE-Universitat Ramon LlullCapital Allocation and Productivity in South Europe Discussant: Diego Restuccia, University of Toronto
2:15 pm	Colin J. Hottman, Columbia University Stephen J. Redding, Princeton University and NBER David Weinstein, Columbia University and NBER What is ’Firm Heterogeneity’ in Trade Models? The Role of Quality, Scope, Markups, and Cost Discussant: Daniel Xu, Duke University and NBER
3:00 pm	Break
3:15 pm	Lucia Foster, Bureau of the Census Cheryl Grim, Bureau of the Census John C. Haltiwanger, University of Maryland and NBER Zoltan Wolf, Center for Economic Studies, US Bureau of Census Macro and Micro Dynamics of Productivity: Is the Devil in the Details? Discussant: Jan De Loecker, Princeton University and NBER
4:00 pm	Adjourn
5:15 pm	Reception at the Royal Sonesta Hotel

On Forecasting Variation and Covariation

One hallmark of a great idea is that it's "obvious" (ex post). Fantastic recent work by Bollerslev, Patton, and Quaedvlieg (BPQ) certainly passes that test.

BPQ build on the classic Barndorff-Nielsen and Shephard result that the precision with which realized variation and covariation are estimated is time-varying but can be estimated (let's just speak of "variation" for short, whether univariate or multivariate). Put differently, the measurement error in realized variation is heteroskedastic but can be estimated. Hence, for optimal variation prediction, one should presumably weight the recent past differently depending on the estimated size of the measurement error. BPQ do it and get large predictive gains. Check it out here. (This is the new and multivariate (covariance) paper, which cites the earlier univariate (variance) paper.)

Why didn't I think of that? I mean, really, the Barndorff-Nielsen and Shephard result is more than a decade old, and I know it well. Can I not put two and two together? Damn.

But seriously, congratulations to BPQ.