Awkward: NY Fed Debunks Myth Of "First-Quarter Residual Seasonality"

Steve Liesman is quaking in his reporter's boots this morning as the SF Fed & BEA's credibility-crushing "double-seasonal-adjustment" thesis is crushed into statistical neverland by the The NY Fed. A study by economists at the Board of Governors of the Federal Reserve did not find significant statistical evidence for such distortions on the aggregate GDP level, despite meteoroconomist Joe Lavorgna's assertion that Q1 grew 1.2% thanks to the magic of made-up numbers. As The NY Fed concludes, in a tone that suggests "sigh, again, "it will not be surprising if the question of residual seasonality comes up again next year when first-quarter growth numbers are announced."


The SF Fed and further The BEA did their best to provide evidence of growth to justify rate hikes (despite macro data's collapse) thanks to the very-smart-sounding "residual seasonality"...

Although the agency adjusts its figures for seasonal variations, growth in any given first quarter still tends to be weaker than in the remaining three, economists have found, a sign there may be some bias in the data. It’s a phenomenon economists call “residual seasonality.”

And Lavorgna trolled:

Recent research by the San Francisco Fed shows that the trend of weak Q1 output growth over the past several years can be largely explained by residual seasonality in the seasonally-adjusted GDP data. Our calculations along the same lines suggest that applying an additional seasonal adjustment to the BEA’s data results in Q1 real GDP growth of 1.2%.




We find that the effect of residual seasonality on GDP estimates has been increasing sharply for over a decade.

Our last discussion of this joke ended as follows:

Normally, we would respond to this idiocy, in which apparently a $100 billion build up in inventory to offset a 2% collapse in GDP due to a secular plunge in global trade and a surge in the USD due to fears of an imminent rate hike, are seasonal but we are just too busy laughing.

It turns out the NY Fed did respond... (via Liberty Street Economics blog),

The Myth of First-Quarter Residual Seasonality

The current policy debate is influenced by the possibility that the first-quarter GDP data were affected by “residual seasonality.” That is, the statistical procedures used by the Bureau of Economic Analysis (BEA) did not fully smooth out seasonal variation in economic activity. If this is indeed the case, then the weak readings of the economy in the first quarter give an inaccurate picture of the state of the economy. In this post, we argue that unusually adverse winter weather, rather than imperfect seasonal adjustment by the BEA, was an important factor behind the weak first-quarter GDP data.


Recent studies by economists at the Federal Reserve Bank of San Francisco as well as a number of private sector research firms (as summarized by the Wall Street Journal) suggest that there are problems with the seasonal adjustment of GDP data for the first quarter of the year. A study by economists at the Board of Governors of the Federal Reserve, however, did not find significant statistical evidence for such distortions on the aggregate GDP level, although they found weak evidence for some of its components.


We revisit the issue of residual seasonality using regression analysis. As a start, seasonally adjusted annualized GDP growth is regressed on a constant, seasonal dummies for each of the first, second, and fourth quarters, and lagged GDP growth rates, using data from 1975 through the first quarter of 2015. The hypothesis is that an accurate seasonal adjustment would make the seasonal dummies statistically insignificant, whereas a significant seasonal dummy suggests an unexplained influence on growth rates in certain quarters even after seasonal adjustment. Regressions are estimated using moving ten-year data windows. The chart below shows the evolving estimates of the first-quarter seasonal dummy parameter over our sample. The coefficient estimates are not statistically significant until the final estimation window, which finds uncorrected seasonality in the first quarter over the past ten years, averaging almost 2 percentage points in annualized terms. The seasonal dummies for the second and fourth quarters are never significantly different from zero.


Residual Seasonality


Using sliding ten-year windows of data means some of the regressions will be dominated by recessions. To correct for this effect, we add an NBER recession dummy variable and repeat the analysis, the results of which are shown in the chart below. The results are essentially the same.


Residual Seasonality


The next round of analysis is to see if the recent negative first-quarter readings might be the result of harsher-than-usual winter weather. To make this assessment, we use a mixed-frequency regression for GDP growth with monthly variables that measure how much the temperature undershoots relative to the monthly average in both the current and previous quarters plus lagged quarterly growth rates. Using monthly temperature data allows the model to let weather in some months be more important than in others. This model’s out-of-sample forecasting ability of GDP growth is better than a more basic model with only lagged growth rates and a constant, suggesting that data on adverse weather conditions are useful information in tracking GDP growth.


Given this outcome, we repeated the above analysis for residual seasonality with a new variable that proxies the growth impact of weather severity in the current quarter using our weather conditions data. The final chart below depicts how the potential growth impact of residual seasonality in the first quarter evolved over our sample. The estimated impact is now much more centered toward zero and it never becomes statistically significant now that the impact of adverse weather is accounted for separately in the regression. As for the likely weather impact on GDP growth, the model has the unusually cold readings for the first quarter of 2015 taking about 2 percentage points off growth on an annualized basis.


Residual Seasonality


It will not be surprising if the question of residual seasonality comes up again next year when first-quarter growth numbers are announced. This analysis highlights that the usefulness of seasonally adjusting data can be hampered by weather conditions that are unusual relative to the period over which the seasonal factors were calculated.

*  *  *

So will historical GDP data be 'corrected' for 'weather' (as opposed to 'residual seasonality')? As we previously concluded:

And, of course, once the Fed's credibility finally crashes, its seasonally adjusted credibility will be at an all time high.

It appears we just got closer to that peak un-credibility moment.