Saturday, August 29, 2015

New CEA Overview of GDO

The U.S. Council of Economic Advisors has a nice new review of "Gross Domestic Output" (GDO), a simple average of expenditure- and income-side GDP estimates now published by the BEA.

In an earlier post I wrote rather negatively about GDO as compared to GDPplus, which is an optimally-weighted blend rather than a simple average. (See the FRB Philadelphia GDPplus site and the corresponding Aruba et al. paper available there.) My view has not changed.

But I want to be very clear about one thing: Quite apart from whether GDO is as accurate as GDPplus, GDO is surely much, much more accurate than standard expenditure-side GDP alone, or income-side GDP alone. Just look at Figure 2 and the surrounding discussion here. (in X. Chen and N. Swanson, eds., Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions, Essays in Honor of Halbert L. White Jr., Springer, 2013, 1-26).

As I said in the above-mentioned earlier post (but alas, burried at the end):
I applaud the BEA's new averaged GDP. If it's not at the cutting edge, it's nevertheless much superior to the standard approach of doing nothing ... and it's an official acknowledgment of the wastefulness of doing so. Hence it's a significant step in the right direction. Hopefully its publication by BEA will nudge people away from uncritical and exclusive reliance on expenditure-side GDP. 
So here's to GDO.

[By the way, speaking of the Hal White volume, the introductory chapter is marvelous, filled with wonderful memories of Hal's career and insights into his research. You must read his description of his career path leading to UCSD, pp. vii-xi in the gray box.]

Wednesday, August 26, 2015

Monday, August 24, 2015

The Superiority of Economists

The title of this post is the title of a newish paper by Marion Fourcade (Berkeley), Etienne Ollion (Strasbourg), and Yann Algan (Sciences Po, Paris) (FOA). 

Yes, I know FOA is already published, even insightfully blogged by Krugman. (Blogged on? Blogged upon? Or maybe give up and just say "reviewed"?) But I'm often slow to notice things; maybe you are too. So if you haven't read it yet, take a look. Regardless of your reaction, it's undeniably fascinating reading.


FOA popped back into my head because I recently received an email announcing its September 2015 presentation at the 20th Anniversary Conference of the Foundation Banque de France, with discussion by Ramon Marimon (EUI) and Lucrezia Reichlin (LBS).


It's interesting that FOA is still being presented after publication, which is highly unusual in economics. But it makes sense: it's a unique paper, and there's still a lot to discuss.

Sunday, August 16, 2015

Manski on Uncertainty in Official Statistics

Chuck Manski has a fascinating forthcoming Journal of Economic Literature piece, "Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years After Morgenstern."

Manski's subtitle refers to Oskar Morgenstern's book, On the Accuracy of Economic Observations (1950, 2nd rev. ed. 1963), in which he assessed the accuracy of economic data and argued for the provision of error estimates in official statistics. (Morgenstern's book also famously reports that, after reading an early version, Norbert Wiener remarked that "economics is a one or two digit science.")

Significantly updating and amplifying Morgenstern's already-forceful case, Manski's treatment contains important new insights. He organizes his discussion not around the usual "sampling error" and "non-sampling error," but rather around what he calls "transient statistical error" (whether sampling or non-sampling), "permanent statistical error" (again, whether sampling or non-sampling), and conceptual error. I'll stop here; you can read it for yourself. Great stuff.

Monday, August 10, 2015

2015 CIRANO Real‐Time Workshop

The 2015 CIRANO Real‐Time Workshop will be in Montreal, October 9-10, 2015. As usual, the program is looking great, thanks to the Program Committee of Dean Croushore (University of Richmond),  Domenico Giannone (FRB New York), Shaun Vahey (University of Warwick) and  Simon van Norden (HEC Montreal and CIRANO). The preliminary program appears below. Presumably the program and the papers will be posted online in due course.

Forecasting Software

Charles, De Antonio Liedo, Maggi and Palate 
JDEMETRA + Nowcasting: Macroeconomic Monitoring and Visualizing News

McDonald, Thamotheram, Vahey and Wakerly 
Assessing the Economic value of Probabilistic forecasts in the Presence of an Inflation Target


Forecasting Inflation

Jarocinski and Lenza 
Output gap and inflation forecasts in a Bayesian dynamic factor model of the euro area

Kishor and Koenig 
The Role of Inflation Expectations, Core Inflation and Slack in Real‐Time Inflation Forecasting

Mertens and Nason 
Inflation and Professional Forecast Dynamics: An Evaluation of Stickiness, Persistence and Volatility


Mixed Frequency Forecasting

Brave, Butters, Justiniano 
Forecasting Economic Activity with Mixed Frequency Bayesian VARs

Dahlhaus, Guenette and Vasishtha 
Nowcasting BRICS+M in Real Time


Data Revision and Measuring Shocks

Amir‐Ahmdi, Matthes and Wang 
Measurement Errors and Monetary Policy: Then and Now

Jo and Sekkel 
Macroeconomic Uncertainty Through the Lens of Professional Forecasters


Density Forecasting

Smith and Vahey
Asymmetric Forecast Densities for US Macroeconomic Variables from a Gaussian Copula Model of Cross‐Sectional and Serial Dependence

Diebold, Schorfheide and Shin 
Real‐Time Forecast Evaluation of DSGE Models with Stochastic Volatility

Rossi and Sekhposyan 
Alternative Tests for Correct Specification of Conditional Predictive Densities


Macro Financial Linkages

Kapetanios, Price and Young 
A financial conditions index using targeted data reduction

Crump, Eusepi and Mönch 
The Term Structure of Expectations and Bond Yields

Saturday, August 1, 2015

On the Great Financial Panic of 2007

(a) I've always felt that the "Great Financial Panic of 2007" was a good old-fashioned banking panic, even if the modern version at first looks quite different from those of the nineteenth and early twentieth centuries. Gary Gorton's wonderful book, building on his earlier research, gets it exactly right:
"Holders of short-term liabilities...refused to fund "banks" [that is, various vehicles in the shadow banking system] due to rational fears of loss. ... As with the earlier panics, the problem at root is a lack of information."
(b) Simultaneously, I've always felt that the Great Panic of 2007 was largely driven by "too big to fail" (TBTF) (e.g. see Gary Stern here), which creates incentives that promote excessive risk-taking. (If you win, you make a fortune; if you lose, you get bailed out.)

What didn't occur to me until a dinner with Gorton during the 2015 PIER Workshop is that (a) and (b) are largely incompatible. That is, if the Panic of 2007 really is like those of the nineteenth century, then it can't have been driven by TBTF, which didn't exist back then. And the Panic of 2007 really was like those of the nineteenth century. 

So my view has evolved significantly: TBTF may well have made the Great Panic more likely than it otherwise would have been, and TBTF may well have increased its severity relative to what otherwise would have been, but TBTF simply can't be "responsible." Thanks, Gary for pushing me forward.

Monday, July 27, 2015

Rebonato on Bond-Yield Econometrics

Riccardo Rebonato (R) has a fascinating new paper, which builds on important earlier work of Cieslak and Povala (2010) (CP). 

The cool thing about CP is the way it advances and blends certain aspects of both the spanning literature ("all information of relevance for yield prediction is embedded in the current term structure," e.g. via forward-rate tent functions as in Cochrane-Piazessi (2004)), and the non-spanning literature ("not all information of relevance for yield prediction is embedded in the current term structure," e.g. because certain macro variables seem to help predict risk premia, as in Ludvidson and Ng (2009)).

In turn, the cool thing about R is its insightful high-frequency / low-frequency interpretation of CP, with the macro predictors of primary relevance at low frequencies. 

Adapted from the R abstract:
This paper presents a simple reformulation of the restricted CP return-predicting factor which retains by construction exactly the same (impressive) explanatory power as the original one, but affords an alternative and attractive interpretation. What determines future returns, the new factor shows, is ... the distance of the yield-curve level and the slope not from fixed reference levels, but from conditional ones determined by ... long-term inflation.

I'm reminded of key early work by Kozicki and Tinsley (2001) on market perceptions of central bank credibility providing low-frequency anchoring for long yields.

More generally, high-frequency / low-frequency decompositions have a long and distinguished history in time-series econometrics, from cycle / trend real-output decompositions in macro-econometrics (e.g., Cochrane (1988)) to short-run / long-run volatility decompositions in financial econometrics (e.g., the "component GARCH" model of Engle and Lee (1999)).

A final thought: Bauer and Hamilton (2015) have recently questioned the entire non-spanning literature. Perhaps I'll cover that in a subsequent post, and its relation to CP and R (e.g., why worry about blending the spanning and non-spanning approaches if the non-spanning approach is suspect?).

Sunday, July 19, 2015

Introducing Ben Connault

I should introduce Benjamin ("Ben") Connault, Penn's newly-hired young econometrician, arriving from Princeton any day now. We're extremely grateful to Bo Honoré, Ulrich MüllerAndriy Norets, and Chris Sims for sending him our way.

Frank is the de facto required name for male Penn econometricians (as in Frank Schorfheide, Frank DiTraglia, and yours truly), but Ben somehow managed to dodge the requirement. At any rate, if his name is highly original by Penn's standard, so too is his research, by any standard. Very much to his credit, Ben is an independent thinker whose econometrics isn't easily catagorized. Just check him out for yourself. We look forward to great things from him.


Welcome, Ben!

Monday, July 13, 2015

What Seasonally-Adjusted U.S. Economic Data Needs Most...

...is non-adjustment. This is not a minor issue: there's not even an unadjusted U.S. GDP! 

Seasonal adjustment is sometimes desirable, but sometimes not. Sometimes it's done poorly, sometimes it's better done with extra care and transparency by the researcher, etc. And the restrictions implied by economic theory generally hold across all frequencies, not just non-seasonal frequencies. There are many, many issues. (See, for example, the discussion in Hansen and Sargent (1993) and the references there.) 


Sometimes seasonality is the center of attention, and hence of intrinsic interest, so access to unadjusted data is crucial. But even when seasonality is arguably just a "nuisance," it's valuable to have access to unadjusted series, which are more fundamental. If I have an unadjusted series, I can adjust it myself, and then you and I can have a potentially valuable discussion as to how and why I adjusted it. In contrast, if I have only an adjusted series, in general I have no way to recover the underlying unadjusted series, so you and I have no choice but to rely completely on agencies' seasonal-adjustment procedures and their many embedded judgments.

Let me be clear: Both academic researchers and the data-providing agencies have made important seasonal-adjustment advances over many decades. I'm grateful and I hope they continue. I'm simply saying that we should also have access to unadjusted data. So let me add to the seasonality research "to-do list" that I recently offered


Any series provided in seasonally-adjusted form should also be provided in unadjusted form. 

Sunday, July 5, 2015

Being a Millionaire Isn't What it Used to be

One evening a few weeks ago, some friends and I wound up talking about the "roaring twenties" in the U.S., and all the "millionaires" created, and wondering just what $1 million 1925 dollars would be in 2015 dollars. Obviously the price level has multiplied greatly since 1925, but how many times? Five? Fifteen? Fifty? Five hundred? We weren't really sure.

The handy CPI calculator at FRB Minneapolis came to the rescue: $1 in 1925 is $13.67 in 2015. That is, you'd need $13.67 million in 2015 to have the purchasing power of someone with $1 million in 1925!

If you want to dig a little deeper, the Fed's full annual CPI data 1801-2015 appear in the table below (year, CPI price level, inflation rate). Note that the price level was stable during 1801-1913, after which it grew steadily forevermore. Quiz: Besides steady inflation, what didn't exist in the U.S. before 1913 but has been with us ever since? You know the answer.

Don't get me wrong. I'm not wishing we were back in 1880 with no Federal Reserve System. But the price level pattern certainly does suggest that the benefits delivered by central banks, printing fiat money, come at a significant cost -- the inflation tax -- which is highly regressive, borne disproportionately by the unsophisticated poor.


1801
50
-2.0%
1802
43
-14.0%
1803
45
4.7%
1804
45
0.0%
1805
45
0.0%
1806
47
4.4%
1807
44
-6.4%
1808
48
9.1%
1809
47
-2.1%
1810
47
0.0%
1811
50
6.4%
1812
51
2.0%
1813
58
13.7%
1814
63
8.6%
1815
55
-12.7%
1816
51
-7.3%
1817
48
-5.9%
1818
46
-4.2%
1819
46
0.0%
1820
42
-8.7%
1821
40
-4.8%
1822
40
0.0%
1823
36
-10.0%
1824
33
-8.3%
1825
34
3.0%
1826
34
0.0%
1827
34
0.0%
1828
33
-2.9%
1829
32
-3.0%
1830
32
0.0%
1831
32
0.0%
1832
30
-6.3%
1833
29
-3.3%
1834
30
3.4%
1835
31
3.3%
1836
33
6.5%
1837
34
3.0%
1838
32
-5.9%
1839
32
0.0%
1840
30
-6.3%
1841
31
3.3%
1842
29
-6.5%
1843
28
-3.4%
1844
28
0.0%
1845
28
0.0%
1846
27
-3.6%
1847
28
3.7%
1848
26
-7.1%
1849
25
-3.8%
1850
25
0.0%
1851
25
0.0%
1852
25
0.0%
1853
25
0.0%
1854
27
8.0%
1855
28
3.7%
1856
27
-3.6%
1857
28
3.7%
1858
26
-7.1%
1859
27
3.8%
1860
27
0.0%
1861
27
0.0%
1862
30
11.1%
1863
37
23.3%
1864
47
27.0%
1865
46
-2.1%
1866
44
-4.3%
1867
42
-4.5%
1868
40
-4.8%
1869
40
0.0%
1870
38
-5.0%
1871
36
-5.3%
1872
36
0.0%
1873
36
0.0%
1874
34
-5.6%
1875
33
-2.9%
1876
32
-3.0%
1877
32
0.0%
1878
29
-9.4%
1879
28
-3.4%
1880
29
3.6%
1881
29
0.0%
1882
29
0.0%
1883
28
-3.4%
1884
27
-3.6%
1885
27
0.0%
1886
27
0.0%
1887
27
0.0%
1888
27
0.0%
1889
27
0.0%
1890
27
0.0%
1891
27
0.0%
1892
27
0.0%
1893
27
0.0%
1894
26
-3.7%
1895
25
-3.8%
1896
25
0.0%
1897
25
0.0%
1898
25
0.0%
1899
25
0.0%
1900
25
0.0%
1901
25
0.0%
1902
26
4.0%
1903
27
3.8%
1904
27
0.0%
1905
27
0.0%
1906
27
0.0%
1907
28
3.7%
1908
27
-3.6%
1909
27
0.0%
1910
28
3.7%
1911
28
0.0%
1912
29
3.6%
1913
29.7
2.4%
1914
30.1
1.3%
1915
30.4
0.9%
1916
32.7
7.7%
1917
38.5
17.8%
1918
45.2
17.3%
1919
52.1
15.2%
1920
60.2
15.6%
1921
53.6
-10.9%
1922
50.3
-6.2%
1923
51.2
1.8%
1924
51.5
0.4%
1925
52.7
2.4%
1926
53.2
0.9%
1927
52.2
-1.9%
1928
51.6
-1.2%
1929
51.6
0.0%
1930
50.2
-2.7%
1931
45.7
-8.9%
1932
41.0
-10.3%
1933
38.9
-5.2%
1934
40.2
3.5%
1935
41.2
2.6%
1936
41.7
1.0%
1937
43.2
3.7%
1938
42.3
-2.0%
1939
41.8
-1.3%
1940
42.1
0.7%
1941
44.2
5.1%
1942
49.1
10.9%
1943
52.0
6.0%
1944
52.9
1.6%
1945
54.1
2.3%
1946
58.6
8.5%
1947
67.1
14.4%
1948
72.2
7.7%
1949
71.5
-1.0%
1950
72.3
1.1%
1951
78.0
7.9%
1952
79.8
2.3%
1953
80.4
0.8%
1954
80.7
0.3%
1955
80.5
-0.3%
1956
81.7
1.5%
1957
84.4
3.3%
1958
86.7
2.7%
1959
87.6
1.0%
1960
88.9
1.5%
1961
89.8
1.1%
1962
90.9
1.2%
1963
92.0
1.2%
1964
93.2
1.3%
1965
94.7
1.6%
1966
97.5
3.0%
1967
100.2
2.8%
1968
104.5
4.3%
1969
110.2
5.5%
1970
116.7
5.8%
1971
121.7
4.3%
1972
125.7
3.3%
1973
133.4
6.2%
1974
148.2
11.1%
1975
161.7
9.1%
1976
171.0
5.7%
1977
182.1
6.5%
1978
196.0
7.6%
1979
218.1
11.3%
1980
247.6
13.5%
1981
273.2
10.3%
1982
290.0
6.1%
1983
299.3
3.2%
1984
312.2
4.3%
1985
323.2
3.5%
1986
329.4
1.9%
1987
341.4
3.7%
1988
355.4
4.1%
1989
372.5
4.8%
1990
392.6
5.4%
1991
409.3
4.2%
1992
421.7
3.0%
1993
434.1
3.0%
1994
445.4
2.6%
1995
457.9
2.8%
1996
471.3
2.9%
1997
482.4
2.3%
1998
489.8
1.6%
1999
500.6
2.2%
2000
517.5
3.4%
2001
532.1
2.8%
2002
540.5
1.6%
2003
552.8
2.3%
2004
567.6
2.7%
2005
586.9
3.4%
2006
605.8
3.2%
2007
623.1
2.9%
2008
647.0
3.8%
2009
644.7
-0.4%
2010
655.3
1.6%
2011
676.0
3.2%
2012
689.9
2.1%
 2013
700.0
1.5%
 2014
711.4
1.6%
 2015*
720.3
2%