Recession buzz: April update

Recession talk decreased slightly again in April. According to an index which includes eight newspapers, each publication included an average of 1.96 articles per day using the word “recession,” down from 2.16 in March and significantly below the level of 3.70 in January (see chart 1). The decrease occurred in spite of the continued softening of the job market in March, which was known the first week of April. The release of the first-quarter growth of GDP and the meeting of the FOMC committee, which might have generated some R-talk, occurred on the last days of the month, so their impact on R-word statistics probably straddled April and May.

Chart 1 (click to enlarge)

In spite of the diminishing buzz, the current level of talk is still consistent with an economy in recession, judging from historical trends (see chart 2).

Chart 2 (click to enlarge)


Previous updates: January, February , March



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Ranking of WSJ forecasters: May update

The GDP estimate for the first quarter is out, so it's time to update my ranking of the Wall Street Journal forecasters. (Remember: to pit forecasters against each other I use the Root Mean Squared Error (RMSE) and the most accurate forecaster is the one with the lowest RMSE, among all who have submitted at least ten forecasts since May of 2004. I only include the predictions submitted in the months of February, May, August and November for quarters Q1, Q2, Q3 and Q4, respectively. The Q1 forecast is the one submitted in February, the Q2 forecast is the one posted in May, and so on.)

According to the latest ranking, the most accurate forecaster is Gene Huang of FedEx, pushing Gary Thayer of A.G. Edwards to second place. Resler and Hoffman keep third and fourth place, respectively. The median forecast falls from 6th to 10th. The lanterne rouge continues to be James Smith of the University of North Carolina. At 2.6%, his forecast for 2008:Q1 was as inaccurate as usual.

The naive forecast, which is equal to the growth rate observed during the previous quarter, would have been spot-on this time, since GDP grew by 0.6% in both Q4 and Q1. Historically, however, it has performed worse than any forecaster but one.

Only Edward Leamer of UCLA Anderson Forecast was right on the mark. The predictions of 38 of the 52 forecasters were within one percentage point of the truth.


Top-20 WSJ forecasters, by Root Mean Squared Error (RMSE), as of 2008:Q1


Rank
Forecaster

Firm
RMSE

Absolute deviation for 2008:Q1
1
Gene Huang
FedEx Corp.
0.95
0.4
2
Gary Thayer*A.G. Edwards0.95
--
3
David Resler
Nomura Securities International
1.01
0.8
4
Stuart Hoffman
PNC Financial Services Group
1.01
0.6
5
Allen Sinai
Decision Economics Inc.
1.01
0.1
6
Dana JohnsonComerica Bank1.02
0.1
7
Nicholas S. PernaPerna Associates1.02
0.3
8
Mike Cosgrove
Econoclast
1.02
0.4
9
J. Prakken and C. Varvares
Macroeconomic Advisers
1.03
0.1
--
Median
--
1.05
0.6
10
Scott Anderson
Wells Fargo & Co.1.07
0.4
11
Nairmen Behravesh
Global Insight
1.08
0.9
12
John LonskiMoody's Investors Service
1.08
0.8
13
Edward Leamer
UCLA Anderson Forecast
1.08
0
14
R. Berner and D. Greenlaw
Morgan Stanley
1.08
1.3
15
Douglas Duncan
Mortgage Bankers Association
1.09
0.2
16
Robert DiClemente*
Citibank SSB
1.09
--
17
Diane Swonk
Mesirow Financial
1.09
0.1
18
Neal Soss
Credit Suisse
1.11
0.1
19
Dean Maki
Barclays Capital
1.12
0.4
20
David Rosenberg
Merrill Lynch
1.12
1
Source: WSJ's survey of forecasters and author's calculations.
*Not in WSJ group of forecasters, as of February 2008.

The May forecast for Q2 is also available now. On average, the top five forecasters according to my ranking predict growth of -0.1%. The median of all forecasts is 0.3%.

Previous ranking

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A ranking of WSJ forecasters

For a number of years, The Wall Street Journal has been conducting a survey of economic forecasts. The newspaper is kind enough to publish the prediction of each forecaster, so we can entertain ourselves observing how they fare.

I restrict my attention to quarterly forecasts of GDP growth. Between 2004 and 2007, 27% of the predictions were within 0.5 percentage points of the actual outcome (see Table 1), whereas 56% (27% + 29%) were within one percentage point. Or, if you’re a member of the empty-glass club, 44% missed the target by more than one point.

Table 1. Distribution of accuracy of forecasts (2004-2007)

Forecasts within...
Percentage
0.5 percentage points (p.p.) of actual value
27.2
0.5 - 1 p.p.
28.9
1 - 1.5 p.p.
22.6
1.5 - 2 p.p.
13.7
2 - 2.5 p.p.
5.2
over 2.5 p.p.
2.4
All
100
Source: WSJ's survey of forecasters and author's calculations

To pit forecasters against each other I use the Root Mean Squared Error (RMSE), a one-number summary of the deviations of several forecasts. The RMSE punishes both positive and negative deviations equally, but penalizes big errors proportionally more than small ones*. I also can use it to form confidence intervals.

According to the RMSE measure, the most accurate forecaster is Gary Thayer, of the firm A.G. Edwards, although he no longer participates in the survey. The second most-accurate forecaster, and still in the panel, is Gene Huang of FedEx. (See Table 2.) The best forecaster is able to predict GDP growth within 1.67 percentage points, at a 90% level of confidence. (That means that if he posted 100 forecasts, 90 of them would deviate from the actual GDP growth rate by plus or minus 1.67 percentage points.)

Table 2. Top-20 WSJ forecasters, by Root Mean Squared Error (RMSE)

Rank
Forecaster

Firm
RMSE
Forecasts'
90% confidence
margin (p.p.)
1
Gary Thayer*
A.G. Edwards
0.95
1.67
2
Gene Huang
FedEx Corp.
0.98
1.72
3
David Resler
Nomura Securities International
1.02
1.79
4
Stuart Hoffman*
PNC Financial Services Group
1.03
1.82
5
Allen Sinai
Decision Economics Inc.
1.05
1.83
--
Median forecast
--
1.05
1.83
6
Mike Cosgrove
Econoclast
1.05
1.84
7
Nicholas S. Perna
Perna Associates
1.05
1.85
8
Dana Johnson
Comerica Bank
1.06
1.88
9
J. Prakken and C. Varvares
Macroeconomic Advisers
1.06
1.86
10
R. Berner and D. Greenlaw*
Morgan Stanley
1.07
1.87
11
Nairmen Behravesh
Global Insight
1.09
1.90
12
Robert DiClemente*
Citibank SSB
1.09
1.92
13
John Lonski
Moody's Investors Service
1.09
1.91
14
Scott Anderson
Wells Fargo & Co.
1.11
1.97
15
Douglas Duncan
Mortgage Bankers Association
1.12
1.97
16
David Rosenberg
Merrill Lynch
1.13
1.97
17
Diane Swonk
Mesirow Financial
1.13
1.99
18
David Lereah*
National Association of Realtors
1.13
1.99
19
Neal Soss
CSFB
1.15
2.01
20
Paul Kasriel
The Northern Trust
1.15
2.02
Source: WSJ's survey of forecasters and author's calculations.
*Not in WSJ group of forecasts anymore, as of November 2007.


It is well known that, over time, a group’s forecast is closer to the mark than almost any particular individual’s. Among the WSJ panel it’s no different: the median forecast is sixth in the ranking, out of 47. The same conclusion applies to the average forecast (average and median are very close to the each other in every release of the WSJ survey).

The top participants in the group hold but a tiny advantage over the rest. Even the 20th most accurate person has a margin of error of just over 2 percentage points, versus 1.67 points for the top forecaster. It’s not surprising then that rankings tend to change frequently. For example, at the end of 2006 the top five forecasters were (latest ranking in parentheses): Thayer (1), Rosenberg (17), Perna (8), Sinai (5) and Lonski (14).

Catching a “hot streak” seems to be exceedingly difficult too. Suppose that we define “winning” as being among the 50% most-accurate accurate forecasts for a given quarter. (A rather modest victory, may I say.) By that measure, only 37% of successes were followed by a second win, 31% of two-in-a-row’s were followed by a third success, and just 17% of those were followed by a fourth one.

Can a simple predictor outperform the pros? Michael Bryan of the Federal Reserve of Cleveland, whose commentary I follow in this post, asks that question. He compares the predictions in the Survey of Professional Forecasters (SPF) with the naïve forecast that next period’s outcome will be the same as the latest observed outcome. In terms of my data, that is the prediction that GDP growth in, say, 2008:Q1 will be the same as in 2007:Q4.

Bryan finds that 53% of economists made worse predictions than the naïve forecast. The WSJ panel shows much better marksmanship. All of them performed better than the naïve forecast, except one. (The exception is James F. Smith of Western Carolina University, and by a long shot. His RMSE is 2.83, whereas that of the naïve forecast is 1.89. Compare with the values in Table 2.)

By Clay Bennett
In five days we will have an advance estimate of how much the economy grew during the first quarter. The naïve forecast indicates 0.6 percentage points. The median WSJ forecast in April is exactly zero —neither cold nor hot, as a friend of mine likes to say. Gene Huang, the top forecaster of the hour, says it will be 0.8%. Which one will be closer to the mark?









* For the ranking of forecasters by RMSE, I include all the participants in the survey who submitted at least ten GDP forecasts between May of 2004 and December of 2007. I only include the predictions submitted at the beginning of the months of February, May, August and November for quarters Q1, Q2, Q3 and Q4, respectively. “Actual” GDP growth is taken to be the advance estimate, released one month after the end of the corresponding quarter. Given the timing of the forecasts and of the advance releases of GDP growth, each forecast appeared about three months before the actual outcome was known.

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The Fed's new tools (II)

Last week I described the traditional tools of the Fed (open market operations and the discount window) and an old, but less well-known one (repurchase agreements). Then I described the first innovation, the Term Auction Facility, inaugurated in December.

This week I’ll go over the forms of lending introduced in 2008, and then I'll discuss the options that the Fed is rumored to be considering next.

* * *

In 2007 the Federal Reserve made an effort to provide liquidity through channels other than open market operations and repos. To that effect, it created the Term Discount Window Program (TDWP) and the Term Auction Facility (TAF), as I explained last week. Both of those facilities, however, are available only to depository institutions.

So far I’ve been using the ambiguous term “banks” to refer to institutions that borrow funds or buy Treasurys from the Fed. There are however two broad classes of “banks”: depository institutions and primary dealers. Depository institutions are allowed to accept deposits. Primary dealers, on the other hand, are investment banks and brokers that trade in Treasurys with the Federal Reserve. Bear Stearns and Lehman Brothers are two examples in the latter group. As of today, there are 20 of them.

One defining characteristic of depository institutions is that they can use a broad range of assets to secure their loans from the Fed. The discount window, the TDWP and the TAF all accept a set of assets known as “discount collateral.” That includes pretty much all paper of investment quality, including performing sub-prime mortgages. Primary dealers, on the other hand, only have access to open-market operations (OMOs) and repos. The latter only can be obtained after posting General Collateral —that is paper issued by the Treasury or US agencies only.

Following problems in the mortgage and real estate markets last summer, primary dealers found it increasingly hard to obtain short-term financing because nobody would take their suspicious assets as collateral —or would do so only at very high prices. The Fed stepped up to the plate by opening the Term Securities Lending Facility (TSLF) to primary dealers, on March 27. Roughly speaking, a TSLF loan is an exchange of risky securities for Treasuries for 28 days between Federal Reserve and primary dealers. The range of acceptable collateral, although not as wide as at the discount window, includes some types of paper issued by non-agency institutions (AAA/Aaa-rated private label RMBS and CMBS).

To be sure, the Fed has had a securities lending program for a number of years. The novelty of the TSLF is that it extends the range of acceptable collateral beyond Treasuries. A second novelty is that the term of the loans increases from overnight to 28 days.

Unlike the other tools I have discussed, the TSLF does not have an effect on reserve balances by design. This allows the Fed to pursue its recent strategy of providing liquidity to the banking system without increasing the monetary base.

This is what these loans would look like on the balance sheet of the Fed:

Changes in the Fed's balance sheet after a $1,000M TSLF loan
Assets
US government securities
-1,000
Repurchase agreements
0
Reverse repurchase agreements
0
Direct loans
0
TSLF loan
+1,000
Other assets
0
Liabilities
Currency in circulation
0
Reserve balances
0

In March the Fed inaugurated a second form of lending: the Primary Dealer Credit Facility (PDCF). This venue provides overnight cash loans to all primary dealers, at the same interest rate as the discount window does, and by pledging the same type of collateral. With the PDCF the Federal Reserve has de facto opened the discount window to primary dealers.

PDCF loans increase the monetary base (read the FAQ). Because this facility is meant to oil the credit market, not to provide a monetary stimulus, the Fed will continue to offset the increase in reserves using "a number of tools, including, but not necessarily limited to, outright sales of Treasury securities, reverse repurchase agreements, redemptions of Treasury securities, and changes in the sizes of conventional RP transactions." Here's what a PDCF loan looks like, after it has been offset:

Changes in the Fed's balance sheet after a $1,000M PDCF loan, offset by an open market operation
Assets
US government securities
-1,000
Repurchase agreements
0
Reverse repurchase agreements
0
Direct loans
0
PDCF loan
+1,000
TSLF loan
0
Other assets
0
Liabilities
Currency in circulation
-1,000 + 1,000
Reserve balances
0


The composition of the Fed’s assets has changed substantially over the last nine months. Here’s the balance sheet of the Fed again, in December and March:

Federal Reserve's balance sheet, $ millions
Assets
Aug. 15, 2007
Mar. 19, 2008
US government securities
789,601
660,484
Repurchase agreements24,000
62,000
Reverse repurchase agreements-31,941-46,143
Term Auction Facility loans
0
80,000
Primary Dealers Credit Facility
0
28,800
Direct loans264
125
Other assets37,058
36,603
LiabilitiesCurrency in circulation813,085818,362
Reserve balances5,897
3,507
Source: Federal Reserve, H.4.1 release.

With its new tools, the Fed has provided liquidity without printing much money. In a way, the Fed has become a pawnbroker.

The future?

Loans to commercial banks and primary dealers, from one facility or another, represent now a much larger fraction of assets (see chart, from the Wall Street Journal). The fraction of Treasurys has declined to 53% from 87%.

The concern now is that the Fed may run out of Treasurys. In theory, the Fed could continue extending loans indefinitely. The problem is that, with no Treasurys left over, the Fed would not be able to offset expansions of the monetary base, as it’s been doing for months. Reserve balances would balloon, pushing down the federal funds interest rate to zero. So the Fed is now pondering the following alternatives:

1) Purchase mortgage-backed securities directly —as opposed to taking them as collateral, as it does through the discount window programs and the PDCF. The Fed could finance such purchases by selling Treasurys, and in that case reserve balances would not be affected. But the amount of Treasurys in the Fed’s balance sheet is, as I said, limited and shrinking rapidly.

2) Have the Treasury issue more debt than it needs and deposit the cash at the Fed. The extra cash would be separate from reserve balances, and thus a priori wouldn’t have any impact on the fed funds rate. The Fed would use that cash to purchase Treasurys. This is what this maneuver would look like:

Changes in the Fed's balance sheet after taking $1,000M worth of Treasury deposits, after an issue of "unnecessary" Treasurys
Assets
US government securities
+1,000
Repurchase agreements
0
Reverse repurchase agreements
0
Direct loans
0
PDCF loan
0
TSLF loan
0
Other assets
0
Liabilities
Currency in circulation
0
Reserve balances
0
Treasury deposits
+1,000

While lending conditions don't improve, the new funds would soon turn into loans to banks, so the actual effect on the balance sheet would be (assuming the funds are loaned through the discount window; other forms of lending would affect different lines in the asset side of the balance sheet):

Changes in the Fed's balance sheet after taking $1,000M worth of Treasury deposits, after an issue of "unnecessary" Treasurys
Assets
US government securities
0
Repurchase agreements
0
Reverse repurchase agreements
0
Direct loans
+1,000
PDCF loan
0
TSLF loan
0
Other assets
0
Liabilities
Currency in circulation
0
Reserve balances0
Treasury deposits
+1,000

This would change the way we view sovereign debt. Traditionally, the government’s power to raise taxes and set public expenditures have determined the creditworthiness of sovereign debt. With this plan, the value of the government’s debt obligations would become contingent on the portfolio of dodgy securities that the Fed accepts as collateral.

3) Let the Fed issue its own debt. The Fed would use the funds to purchase securities or make loans. A new entry would appear in the list of Federal Reserve’s liabilities:

Changes in the Fed's balance sheet after issuing $1,000M worth of its own debt
Assets
US government securities
0
Repurchase agreements
0
Reverse repurchase agreements
0
Direct loans
+1,000
PDCF loan
0
TSLF loan
0
Other assets
0
Liabilities
Currency in circulation
0
Reserve balances0
Federal Reserve bonds
+1,000


4) Remunerate reserves. Reserve balances are like checking accounts: they don’t earn interest. For that reason banks have little incentive to hold more reserves than they need to meet the Fed’s requirements and clear transactions. Any excess reserves are loaned to other banks. As Greg Ip explains, “if the Fed paid, say, 2% interest on reserves, banks would have no incentive to lend out excess reserves once the federal funds rate fell to that level.”

This measure would lead to a higher equilibrium level of reserve balances, for a given value of the federal funds interest rate. It would also reduce the amount of inter-bank lending, as banks would keep more of their cash in their safe-deposit box at the Fed. That lending would be replaced by loans from the Federal Reserve.

This reviews my review of the Fed's new monetary policy. Will these new tools make it to the textbooks? It’s hard to tell whether the particular facilities (TAF, TSLF, etc.) will survive. But I think that some standardized form of loans to non-depository institutions will stay, and that the Fed will become willing to accept dodgier collateral than it traditionally has.



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The odds of getting an H-1B visa

I just read about this. US Citizenship and Immigration Services (USCIS) received 163,000 applications for H1-B visas by April 7. Since that number exceeds the visa cap, the government will not consider any more applications this year (Source: USCIS, via the H1B data blog). 31,200 of the applications were from individuals with an advanced degree. The government quotas are: 20,000 for applicants with an advanced degree and 65,000 for the rest. (I wrote about the H1-B visa system a couple of weeks ago.)

The USCIS will hold two lotteries this week. The first one is for applicants with an advanced degree from a US institution (MA or higher). Applicants who are not selected in that first lottery will be pooled with the rest of the applications in the second lottery.

Unless my math is failing me, the probability of getting a work visa is then 80.4% for advanced-degree holders, and 45.5% for the rest of the applicants.

UPDATE: I'm really behind on this. USCIS already conducted the lottery (they did it on April 14, two days ago). Lucky applicants should get a notification by early June. I really recommend reading H1B data if you want timely information.