A Quarterly Econometric Model for Short-Term Forecasting of the U.S. Dairy Industry
by Roberto Mosheim
Technical Bulletin No. (TB-1932) 38 pp, January 2012
What Is the Issue?
This report documents the ongoing forecasting activities by
USDA's Economic Research Service (ERS) that combine judgment-based
forecasting with rigorous econometric estimations and data
construction that can provide better forecasts than a mostly
judgment-based system alone. The formal modeling process gives the
forecasting activity at ERS transparency and full documentation on
the specification, estimation, and validation procedures employed.
Specifically, the econometric models generate the monthly dairy
sector forecasts that contribute to the USDA World Agricultural
Supply and Demand Estimates (WASDE) report. In addition, the
model's estimates potentially can be used to examine the structure
of the sector and the infl uence of policy-relevant variables. The
merit of various econometric and time series models, however, is
more about their ability to forecast effectively than for their
potential contribution to policy analysis.
What Did the Study Find?
Various estimation methods successfully forecast different
endogenous variables, a situation that might change as the sector
evolves or as additional data become available and the applied
econometric model improves. The ERS model generated projections
that outperformed the consensus forecast by USDA's Interagency
Commodity Estimates Committee (ICEC) in roughly half of the
instances in terms of accuracy and predicting turning points in the
data of interest.
The results demonstrate how different methods are preferable,
depending on the variable. To produce forecasts in the dairy
sector, we cannot rely on a single estimating method. Moreover, the
findings suggest that composite forecasting or forecast blending
will play a significant role in this process. Careful econometric
specification and data development will ensure a successful
transition from the mostly judgment-based forecasting system
previously used at ERS.
The ERS forecasting model relied on specific characteristics not
seen in previous studies of this kind. Specifically, it:
• Uses a variety of methods to estimate endogenous
• Employs both time series and structural models; and
• Uses quarterly data and ex-post forecasting (seldom seen in this
type of research).
The estimations highlight certain characteristics of the U.S.
• Milk production per cow is seasonal and increases over
• Herd size movements are cyclical and tied to fluctuations in the
• As the margin (all-milk price minus feed cost) decreases
(increases), herd sizes decrease (increase) after a number
• Price movement in the all-milk price is correlated to the price
of cheese and butter more than whey and nonfat dry
• The dairy sector is highly interlinked (reflected in block
recursive structure where variables at one stage serve as
determinants for the next).
How Was the Study Conducted?
The model is divided into 4 blocks comprised of 15 behavioral
equations and 1 block comprised of 5 identities. Most of the
behavioral equations are specified in logarithmic form that permits
interpretation of estimated coefficients as elasticities. Each
equation within a block forecasts a variable required by ICEC. The
blocks are linked in a blockrecursive fashion such that the fi rst
one generates estimates of variables that are then employed as
predetermined variables in the second, third, and fourth. The
resulting structure produces consistent forecasts across different
sections of the dairy sector.
Economic theory typically defines the structural equations in
models, although that practice is not as useful in the case of time
series models where all variables within each block influence each
other. The ERS model is based on quarterly data, beginning with the
first quarter when all necessary variables are available
(fourth-quarter 1998 or Q4/1998). Possible limitations, with
respect to degrees of freedom for estimation, were the main reasons
that the system of equations were divided into blocks and also
explains why some modeling choices, such as the econometric
specification of dairy product prices by means of inverse (price
dependent) product supply equations, were made.
Prior to estimating, the various blocks were identified by rank
condition to ensure that unique values of the structural parameters
could be derived from the reduced form of the system. Estimations
of the model were conducted by various simultaneous equations and
time series methods that generate different values for the
endogenous variables. These results were validated by withholding
four quarters of known data and estimating the model, generating an
ex-post forecast. The ex-post forecasts were compared with the
known values of the withheld data to determine how well the models
performed based on data available at the end of first-quarter 2009
(Q1/2009). These projections were also compared with those agreed
upon by USDA in early April 2009 for the four successive quarters
ending in first quarter 2010 (Q1/2010).