Multiple Horizons and Information in USDA Production Forecasts. Dwight R. Sanders. And. Mark R. Manfredo

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1 Muliple Horizons and Informaion in USDA Producion Forecass Dwigh R. Sanders And Mark R. Manfredo Paper presened a he NCR-34 Conference on Applied Commodiy Price Analysis, Forecasing, and Marke Risk Managemen S. Louis, Missouri, April 7-8, 2006 Copyrigh 2005 by Dwigh R. Sanders and Mark R. Manfredo. All righs reserved. Readers may make verbaim copies of his documen for non-commercial purposes by any means, provided ha his copyrigh noice appears on all such copies. * Dwigh R. Sanders is an Assisan Professor of Agribusiness Economics a Souhern Illinois Universiy, Carbondale, Illinois. Mark R. Manfredo is an Associae Professor in he Morrison School of Agribusiness and Resource Managemen a Arizona Sae Universiy, Mesa, Arizona.

2 Muliple Horizons and Informaion in USDA Producion Forecass Praciioner s Absrac USDA livesock producion forecass are evaluaed for informaion across muliple horizons using he direc es developed by Vuchelen and Guierrez. Forecass are explicily esed for raionaliy (unbiased and efficien) as well as for incremenal informaion ou o hree quarers ahead. The resuls sugges ha alhough he forecass are ofen no raional, hey ypically do provide he forecas user wih unique informaion a each horizon. Turkey and milk producion forecass ended o provide he mos consisen performance, while beef producion forecass provided lile informaion beyond he wo quarer horizon. Keywords: USDA forecass, forecas consisency, efficiency Inroducion USDA forecass are widely used by hose in agribusiness who provide farmers wih seeds, equipmen, chemicals, and oher goods and services, [who] sudy he repors when planning heir markeing sraegies (Naional Agriculural Saisics Service, hp:// imporn.hm). By heir naure, hese sraegic decisions in agribusiness require relaively longhorizon forecass. Moreover, producer s invesmen-decisions and markeing horizons ofen span a number of producion cycles. The USDA accommodaes his need by providing forecass from one o hree quarers ahead for mos ypes of livesock producion. In conras o crops, where producion forecass generally span only one producion cycle, livesock producion forecass can span a number of producion cycles, especially in indusries wih relaively shor biological lags such as poulry. Given he imporance of long-horizons in sraegic planning, i is imporan ha boh he USDA and forecas users undersand he marginal informaion provided in muliple horizon forecass. Researchers have closely examined USDA forecass in erms of accuracy (Kasens, Schroeder, and Plain; Garcia, Irwin, Leuhold, and Yang), informaion conen (Carer and Galopin), and marke impac (Sumner and Mueller). Noably, Bailey and Brorsen examine he accuracy of he USDA s monhly forecass for annual beef and pork producion in a fixed-even framework. They repor ha over he sample period, he USDA forecass are biased predicors, and furhermore, do no mee he condiions for opimaliy. In a similar vein, Sanders and Manfredo repor ha USDA one quarer-ahead producion forecass for beef, pork, and broilers are unbiased, bu inefficien. Moreover, he USDA forecass do no encompass simple ime series models. Addiional research by Sanders and Manfredo show ha boh USDA and Cooperaive Exension Service forecass by hemselves are inferior o composie forecass a longer-horizons. While hese findings are imporan, hey do no explicily examine he informaional conen of he muliple horizon forecass vis-à-vis heir one sep-ahead counerpar. In his research, we specifically es he informaion provided by muliple horizon forecass, using he mehod of Vuchelen and Guierrez which direcly ess if muliple horizon forecass provide informaion beyond he one sep-ahead forecas. For insance, in January of 2005, he USDA was 2

3 forecasing a 4.5% year-over-year increase in broiler producion one-quarer ahead, a 3.% increase wo quarers ahead, followed by a 3.3% increase in he hird quarer: Does he forecased 3.% increase wo quarers hence really provide any useful informaion beyond 4.5% increase forecased for he nex quarer? Moreover, does he 3.3% increase forecas for hree quarers ahead provide any incremenal informaion over he 3.% wo quarer ahead forecass? In general, do he forecass beyond one period ahead incorporae addiional informaion? Or, are hey simply random adjusmens o he one period ahead forecas? These quesions form he crux of our inquiry. The research quesion is addressed using quarerly livesock producion forecass published by he USDA in he World Agriculural Supply and Demand Esimaes (WASDE). Imporanly, a broad range of markes beef, pork, broilers, urkey, eggs, and milk are examined in order o conras he resuls across differen producion cycles. The value of muliple sep-ahead forecass is imporan for boh forecas users and he USDA. If muliple sep-ahead forecass are essenially a random adjusmen o he one sep-ahead forecas, hen he forecas user may be beer served by simply exrapolaing he one sep-ahead forecas. Also, in his case, he USDA may be able o improve he forecas service by allocaing resources differenly across horizons or concenraing more on markes where he muliple sep horizons add he mos value. I is imporan o noe ha his approach differs from prior work because i specifically addresses he incremenal value added by forecass beyond one period-ahead. In ha sense, i is no a horse race among compeing models or forecasing mehods. Raher, we pose he quesion: Wha is he incremenal informaion, if any, is added a each horizon? As a resul of his sudy, he USDA will beer undersand he value of is muliple sep-ahead forecass across differen indusries. Based on hese resuls, hey may be able o beer allocae forecasing resources. More imporanly, praciioners will be beer able o uilize USDA producion forecass, pin poining hose which provide he greaes incremenal informaion across planning horizons and improving economic decision-making. Mehodology The radiional Mincer and Zarnowiz definiion of forecas efficiency has been esed wih he following regression, () A + = α + βf + u where, A is he realized value a ime, F is he forecas for ime made a ime, and u is he error erm. Forecas raionaliy is esed under he join null of a zero inercep (α=0) and uniary slope coefficien (β=). Moreover, an efficien forecas is characerized by an i.i.d. error erm. Holden and Peel have shown ha he radiional join null hypohesis is a sufficien, bu no necessary, condiion for raionaliy. Therefore, a rejecion of he null hypohesis in () does no lead o clear alernaive saemens abou forecas properies. Granger and Newbold sugges researchers focus sricly on he error erms and a number of sudies have employed hese mehods (see Pons; Sanders and Manfredo). These evaluaions are imporan in deermining he raionaliy properies 3

4 of he forecass. However, hey provide lile insigh as o he informaional conen of he forecass, especially a longer horizons. Noing hese issues, Vuchelen and Guierrez build on () and develop a direc es for informaion conen by wriing forecass as he sum of consecuive adjusmens o he mos recen observaion. So, he one-quarer ahead forecas can be decomposed ino he following componens, (2) F = A + ( F A ), and for he wo sep ahead forecas, 2 2 (3) F = A + ( F A ) + ( F F ). From (2), i is clear ha he one sep ahead forecas can be expressed as a simple adjusmen o he curren level, A. Tha is, he one sep ahead forecas, F, is equal o he curren level, A, plus he expeced change or adjusmen from he curren level, (F -A ). Likewise, in (3), he wo-sep ahead forecas is equal o he curren level, A, plus he forecased change from he curren level, F -A, plus he forecased adjusmen in he following period, (F 2 - F ). Vuchelen and Guierrez develop heir direc es for informaion by subsiuing he decomposiion from (2) ino () o ge he one-sep ahead es, (4) A = θ + κa + λ( F A ) + u. In (4) we can see ha A is equal o previous period s value, A, plus he forecased change in value, (F -A ). Noe, his represenaion provides a wealh of informaion abou he forecas s qualiy and informaion conen. Firs, an unbiased and efficien forecas means θ=0 and λ=, which resuls in (4) reducing o () under he null hypohesis of raionaliy. Second, a forecas ha conains some informaion relaive o he mos recen observaion (or he naïve no change forecas) only requires ha λ 0. So, under his mehodology, he ess are more revealing in he sense ha we can es for opimal properies and direcly es for informaion conen. The es for informaion conen is perhaps more ineresing in he case of muliple sep-ahead forecass. The es equaion for wo sep ahead forecas is developed by subsiuing equaion (3) ino equaion (), 2 (5) A + 2 = γ + δa + η( F A ) + ε ( F F ) + u 2. In (5), an unbiased and efficien forecas is esed under he null ha γ=0, and δ=η=ε=. Again, under he null hypohesis, equaion (5) simplifies o he wo-sep-ahead version of (). Noe, however, equaion (5) ells us he amoun of informaion ha F 2 provides relaive o he mos recen observaion, A, and he one period ahead forecas, F. In he even ha γ=ε=0 and δ=η=, hen he forecaser may as well use he one sep-ahead forecas. Moreover, if ε=0, hen he wo sep ahead forecas is no providing any incremenal informaion over he one sep ahead forecas. In his research, we will also evaluae hree sep-ahead forecass wih a similarly derived equaion, 4

5 2 3 2 (6) A + 3 = φ + κa + ψ ( F A ) + ω( F F ) + ρ( F F ) + u 3 Similar o (5), he unbiased and efficien forecas is esed under he null hypohesis: φ=0, and κ=ψ=ω=ρ=. A number of conclusions are possible from he esimaion resuls. For insance, if κ=ψ= and ω=ρ=0, hen muliple horizon forecass are no adding informaion. Insead, hey are simply random adjusmens o he one sep-ahead forecas. Likewise, if κ=ψ=, 0<ω<, and ρ=0, hen F 2 provides some informaion abou A 3, bu F 3 has no informaion ha improves upon ha conained in he wo sep ahead forecas. Equaion (4) can be esimaed using sandard OLS procedures. However, equaions (5) and (6) are characerized by overlapping forecas horizons, which will resul in correlaed forecas errors and subsequen biased and inconsisen sandard errors. To correc his problem, we follow he lead of Brown and Maial and use he OLS coefficien esimaes, bu correc he variance-covariance marix using he mehods proposed by Hansen and used by Hansen and Hodrick. Daa This sudy focuses on USDA producion forecass for beef, pork, broilers, urkeys, eggs, and milk published in he USDA s World Agriculural Supply and Demand Esimaes (WASDE) repors. For beef and pork, he forecass are for oal commercial producion during he calendar quarer. The broiler and urkey forecas is for federally inspeced producion on a ready-o-cook basis for he calendar quarer. Egg producion is measured as millions of dozen of oal eggs: able eggs plus haching eggs. Milk producion is billions of pounds of farm-level producion. The WASDE forecas is released beween he 8 h and 4 h of each monh. Thus, he forecased level of producion is colleced from he January, April, July, and Ocober WASDE repors for each calendar quarer. For insance, from he January issue, he forecased mea producion for he firs calendar quarer (January, February, and March) is colleced. Acual or final producion levels are colleced as repored in he USDA s Livesock, Dairy, and Poulry repors. The USDA began o consisenly repor hree-quarer ahead forecass in 994. So, he daa span from he firs quarer of 994 (994.) o he hird quarer of 2005 (2005.3), resuling in 46, 45, and 44 one-, wo-, and hree-sep ahead producion forecass and acual values, respecively. In forming heir forecass, he USDA s approach is probably bes described as a composie forecas (personal communicaion). The indusry expers on he World Agriculural Oulook Board essenially pu ogeher an annual supply and use balance shee for each indusry. The annual forecass are adjused for seasonal endencies wihin each indusry. Then, using saisics provided by Economic Research Service and he Naional Agriculural Saisical Service such as cale on feed, hog invenory, and eggs in incubaors as well as recen producion rends, he quarerly producion esimaes are fine uned o reflec he available producion indicaors. Alhough some formal modeling is used, many adjusmens are made based on he experience and knowledge of he Board members. Vuchelen and Guierrez s applicaion of he direc ess focused on forecased growh raes, and i is appropriae o do so here as well. No surprisingly, he absolue level of mea producion demonsraes srong seasonaliy. Therefore, he analysis focuses on seasonal differences defined as he log-relaive change in producion from he same quarer of he prior year. For example, le a 5

6 equal he level of producion in quarer and f - equal he one-sep ahead forecas of producion for quarer. The variables of ineres are hus defined as he change in acual producion, A =ln(a /a -4 ), and he forecased change in producion, F - =ln(f - /a -4 ), such ha he acual and forecass are essenially he percen change in quarerly mea producion from he prior year. Organizing he daa in his manner provides saionary ime series ha are consisen wih hose used by indusry analyss (e.g. Hur) and in prior research (e.g., Kasens, Schroeder, and Plain). Empirical Resuls Accuracy Measures Forecas accuracy is evaluaed wih wo radiional measures: mean absolue error (MAE) and Theil s U. The resuls are presened in able. Since he forecass are in log-relaive changes, he MAE can be inerpreed as he average absolue error in percen. For insance, he MAE for beef producion forecass is 2.32% for one-quarer ahead forecass. Comparing across markes, he beef, pork, and urkey producion forecass all have MAE s in excess of 2%, while egg and milk MAE s are near %. All of he forecass, excep eggs, show a endency for accuracy o decline as he horizon lenghens. The MAE for beef producion nearly doubles from 2.32% o 4.53% as he horizon lenghens from one o hree quarers. The oher markes show some level accuracy degradaion across horizons. The lone excepion is eggs. Oddly, egg producion forecass are more accurae a hree quarers ahead han wo quarers ahead. While he difference is relaively small, i is surprising and raises quesions abou he performance of he longer-horizon forecass. Theil s U essenially normalizes forecas errors by he volailiy of no change forecas errors. Theil s U has a lower bound of zero for perfec forecass, i akes a value of uniy for naïve no change forecass, and i has no upper bound (Leuhold). A he one quarer horizon, all of he forecass provide performance superior o a no change naïve alernaive, excep for eggs. According o Theil s U, he one quarer ahead egg producion forecass resul in a larger error variance han a no change forecas. Naurally, as he forecas horizon lenghens i should be easier o ouperform he no change forecas wih he addiion of even modes informaion. No surprisingly hen, a he wo quarer ahead forecas, all of he forecass perform beer han he naïve alernaive. However, a he hree quarer horizon, he beef producion forecass are nearly as inaccurae as he using he acual producion growh from hree quarers prior i.e., he no change forecas. The relaive slippage in he beef producion forecass may be due o he longer producion cycle of cale, which makes i more difficul o predic quarer-o-quarer changes in producion. Accuracy measures indicae ha forecas precision declines a longer horizons, especially for beef. However, according o Theil s U, he forecass generally perform beer han a naïve no change forecas, he excepions being eggs a he one-quarer horizon and beef a he hree-quarer horizon. This casual comparison of forecas accuracy measures sugges ha longer-horizon forecass decline in accuracy, and here may paricularly be performance issues wih eggs and beef forecass. Sill, i is imporan o evaluae he longer-erm forecass in a saisical sense. We use he mehod proposed by Vuchelen and Guierrez o explicily evaluae he addiional informaion provided by he USDA s longer-horizon forecass. Direc Tess for Informaion The accuracy measures in able sugges ha he informaion conained in USDA producion forecass decline as he forecas horizon lenghens. However, accuracy measures alone do no ell 6

7 he whole sory. I is imporan o direcly es he informaion conained a longer horizons versus he shorer erm forecass: Are he hree quarer ahead forecass providing informaion no conained in he one-quarer ahead forecass? To sar answering his quesion, equaion (4) is esimaed for all markes o es for informaion conen versus a no change naïve forecas. The esimaion resuls for equaion (4) are presened in able 2. In able 2, a raional one-sep ahead forecas is esed under he null hypohesis of θ=0, κ=λ=. While raionaliy is imporan in evaluaing he opimaliy of forecass, he hrus of his research is o gauge he informaion added a each horizon. In equaion (4), an informaive, bu no necessarily raional, one-sep ahead forecas simply requires rejecion of he null hypohesis ha λ=0. One-sep ahead pork, broiler, urkey and milk forecass fail o rejec he null raionaliy hypohesis. Examining he individual coefficiens in hese markes, we can see ha he inercep, θ, is close o zero and he slope coefficiens, κ and λ, are relaively close o one. No surprisingly, in hese four markes, he one-sep ahead forecas provides informaion beyond he naïve forecas wih λ 0 and no saisically differen from one. So, a leas for pork, broilers, urkeys and milk, one-sep ahead producion forecass appear o be raional and informaive. Beef and egg forecass provide quie differen resuls. The one-sep ahead beef forecass rejec he null raionaliy hypohesis, θ=0, κ=λ=; however, he one-sep ahead forecas is informaive, by λ 0. In fac, he λ esimae is no saisically differen from one (p-value =0.573). The rejecion of he raionaliy condiions sem from κ> (p-value = 0.025). Imporanly, κ> and λ=, implies ha he one-sep ahead beef producion forecas, F, does no efficienly use he informaion conained in he curren producion growh, A. Hence, a rejecion of he raional null hypohesis, even hough he one-sep ahead forecas is informaive (λ 0). Conras he beef resuls o ha of eggs, and he advanage of he empirical mehodology becomes clear. The one-sep ahead forecass for egg producion also rejecs he raional null hypohesis, bu i also clearly indicaes ha he one-sep ahead forecas is providing no informaion (λ=0) relaive o jus using he curren quarers growh in producion, A. This resul is consisen wih a Theil s U of.052 presened in able. The individual coefficien esimaes for eggs sugges ha θ>0, 0<κ<, and λ=0. So, he egg producion forecass are downward bias (θ>0), fail o use all of he informaion in A (0<κ<) and mos elling, hey provide no informaion beyond A (λ=0). A forecas user would no benefi from using F insead of A as a one-quarer ahead forecas of egg producion. The resuls for he wo-quarer ahead forecass in able 3 are fairly consisen wih hose in able 2. In paricular, he null hypohesis of raionaliy, γ=0, and δ=η=ε=, is again rejeced for beef (pvalue =0.00) and eggs (p-value = 0.054). Perhaps more imporanly, for all markes, he wo woquarer ahead forecas provides some informaion relaive o he one-quarer head forecas, i.e., ε 0. For beef, 0<ε<, while for all oher markes, he null ha ε= canno be rejeced. While i is useful o know abou forecas raionaliy, he srengh of he direc ess mehod is discovering ha he USDA s wo-quarer ahead forecass are indeed providing useful informaion beyond ha conained in he one-quarer ahead forecass. For all of he wo quarer ahead forecass, he forecas user is 7

8 will gain from using he USDA s forecas versus relying on a naïve forecas or jus he one quarer ahead forecas. The null hypohesis of no informaion conen for hree quarer ahead forecass is rejeced in pork, broilers, and milk a he 5% level and urkeys a he 0% level (Table 4). Only he beef producion forecass fail o rejec he null hypohesis of no informaion (ρ=0). However, unlike he woquarer ahead forecass (Table 3), raionaliy (φ=0, and κ=ψ=ω=ρ=) is rejeced a he 0% level in all he markes excep urkeys and milk. In boh urkeys and milk, he hree-quarer ahead forecas provides informaion, (ρ>0), and none of he slope coefficiens are saisically differen from one. Relaive o he oher markes, his is an impressive forecasing performance for a hree-quarer horizon. The oher markes, beef, pork, broilers, and eggs, rejec raionaliy a he 0% level. Looking a he individual coefficiens, beef has a ρ=0, so i is neiher raional nor does i provide any informaion. On he oher hand, a hree-quarer ahead, pork forecass do add informaion (ρ>0), bu hey are no fully raional (ρ=). In broilers and eggs, rejecion of raionaliy condiions sems from boh a bias in he inercep erm and one or more of he slope coefficiens saisically differing from one. Sill, in boh of hese forecas series, he hree-quarer ahead forecas is adding informaion beyond he one- and woahead forecass (ρ 0). This is imporan informaion for a forecas user who may be more concerned abou he informaional conen of he longer-horizon forecass han he academic noion of raionaliy. Collecively, he resuls sugges ha he USDA producion forecass are no always raional, bu hey usually provide imporan informaion a longer horizons. Turkey and milk producion forecass are especially good. A each forecas horizon, he forecass are raional and provide incremenal informaion no available in shorer-horizon forecass. Egg and beef forecass are paricularly problemaic. Raionaliy is rejeced a he 0% level a each horizon for eggs and beef. One-quarer ahead egg producion forecass provide no informaion beyond he prior quarer s acual value. Likewise, he hree-quarer ahead beef producion forecass fail o provide any incremenal informaion. Pork and broiler producion forecass, while no raional a he hree quarers ahead, provide new informaion a each horizon. So, excep for eggs a he one quarer horizon and beef a he hree quarer horizon, he USDA producion forecass are providing unique informaion a each inerval over heir muliple horizon forecass. Summary and Conclusions Agribusinesses rely on longer-erm and muliple horizon forecass when making business plans and sraegic decisions. To accommodae hese needs, he USDA provides muliple horizon producion forecass for beef, pork, broilers, urkey, eggs, and milk. In his paper, we evaluae he informaional conen of he muliple horizon forecass using he direc es proposed by Vuchelen and Guierrez. The direc es esablishes he conribuion of longer-horizon forecass relaive o he informaion already conained in shorer-horizon forecass. The resuls largely indicae ha he one, wo, and hree quarer ahead forecass all add unique informaion o he overall forecas se. The excepions are egg forecass a he one quarer horizon and he hree quarer ahead beef producion forecass. While forecass beyond one quarer ahead 8

9 are no always raional, hey are generally informaive, where each horizon forecas conains some unique informaion. The resuls have some ramificaions for agribusiness planners and USDA forecasers. Firs for he USDA, milk and urkey forecass are quie good boh raional and informaive and he mehodology should be mainained and perhaps replicaed in oher commodiies. Conversely, egg producion forecass are generally no raional and conain lile informaion a one quarer horizon. The USDA should review heir forecasing procedures for eggs in an aemp o isolae he overall poor forecasing performance in his secor. Beef forecass end no o be raional, bu hey are informaive a he one and wo quarer horizons. I is possible ha he longer producion lags in he beef indusry make forecasing beyond wo quarers especially difficul. Alhough, here does no appear o be a srong correspondence beween producion cycles and performance as shown by good performance in a long producion cycle (milk) and a shor producion cycle (urkeys). Agribusiness managers who uilize USDA producion esimaes should ake some comfor in he resuls: he USDA s muliple horizon forecass generally do conain unique informaion. Alhough he forecass are no always echnically raional, hey may conain imporan hins as o wheher producion in a paricular indusry is going o conrac or expand. As agribusiness planners form expecaions for fuure producion levels, especially a longer horizons, he USDA forecass clearly conain informaion ha may improve hose imporan business decisions. References Bailey, D.V., and B.W. Brorsen. Trends in he Accuracy of USDA Producion Forecass for Beef and Pork. Journal of Agriculural and Resource Economics 23(998): Brown, B.W., and S. Maial. Wha Do Economiss Know? An Empirical Sudy of Expers Expecaions. Economerica 49(98): Carer, C.A., and C.A. Galopin. Informaional Conen of Governmen Hogs and Pigs Repors. American Journal of Agriculural Economics 75(993):7-78. Garcia, P., S.H. Irwin, R.M. Leuhold and L. Yang. The Value of Public Informaion in Commodiy Fuures Markes. Journal of Economic Behavior and Organizaion 32(997): Green, J. Livesock, Dairy, and Poulry Analys, World Agriculural Oulook Board, USDA, personal communicaion on March 3, Granger, C.W.J., and P. Newbold. Forecasing Economic Time Series (2nd ed.). New York: Academic Press, 986. Kasens, T. L., T.C. Schroeder, and R. Plain. Evaluaion of Exension and USDA Price and Producion Forecass. Journal of Agriculural and Resource Economics 23(): Hansen, L.P. Large Sample Properies of Generalized Mehod of Momens Esimaors. Economerica 50(982): Hansen, L.P., and R.J. Hodrick. Forward Exchange Raes as Opimal Predicors of Fuure Spo Raes: An Economeric Analysis. Journal of Poliical Economy 88(980): Harvey, D.I., S.J. Leybourne, and P. Newbold. Tess for Forecas Encompassing. Journal of Business and Economic Saisics 6(998):

10 Harvey, D.I., and P. Newbold. Tess for Muliple Forecas Encompassing. Journal of Applied Economerics 5(2000): Holden, K., and D.A. Peel. On Tesing for Unbiasedness and Efficiency of Forecass. The Mancheser School 5(990): Leuhold, R.M. On he Use of Theil s Inequaliy Coefficiens. American Journal of Agriculural Economics 57(975): Mills, T.C., and G.T. Pepper. Assessing he Forecasers: An Analysis of he Forecasing Records of he Treasury, he London Business School and he Naional Insiue. Inernaional Journal of Forecasing 5(999): Mincer, J., and V. Zarnowiz. The Evaluaion of Economic Forecass. Economic Forecass and Expecaions. Naional Bureau of Economic Research, New York, 969. Naional Agriculural Saisics Service. Abou NASS. hp:// imporn.hm. June 4, 200. Pons, J. The Accuracy of IMF and OECD Forecass for G7 Counries. Journal of Forecasing 9(2000): Sanders, D.R., and M.R. Manfredo. USDA Producion Forecass for Pork, Beef, and Broilers: An Evaluaion. Journal of Agriculural and Resource Economics 27(2002): Sanders, D.R., and M.R. Manfredo. Predicing Pork Supplies: An Applicaion of Muliple Forecas Encompassing. Journal of Agriculural and Applied Economics. 36(2004): Sumner, D.A., and R.A.E. Mueller. Are Harves Forecass News? USDA Announcemens and Fuures Marke Reacions. American Journal of Agriculural Economics 7(989):-8. Unied Saes Deparmen of Agriculure. World Agriculural Supply and Demand Esimaes. muliple issues, Vuchelen, J., and M-I Guierrez. A Direc Tes of he Informaion Conen of he OECD Growh Forecass. Inernaional Journal of Forecasing 2(2005):03-7. Table. Forecas Accuracy Measures, USDA Producion Forecass. MAE Theil s U Marke -ahead 2-ahead 3-ahead -ahead 2-ahead 3-ahead Beef Pork Broilers Turkeys Eggs Milk Noe: For n observaions, he MAE = A -F -k /n, and Theil s U = ( (A -F -k) 2 /n) 0.5 /( (A -A -k ) 2 ) 0.5, where A is he realized observaion a ime, and F -k is he k-sep ahead forecas for ime. 0

11 Table 2. One-Sep Ahead Direc Tess, A θ + u. = + κa + λ( F A ) Coefficien Esimaes Hypohesis Tess Marke θ κ λ Θ=0, κ=λ= λ=0 Beef (0.004) a (0.83) * (0.262) 0.00 b c Pork (0.004) Broilers (0.005) Turkeys (0.004) 0.92 (0.0) (0.35) 0.9 (0.2) (0.68) (0.9) (0.35) Eggs (0.002) * 0.75 (0.09) * (0.27) * Milk (0.002) (0.26) (0.258) a Sandard errors are in parenhesis. b P-value from Chi-squared es for saed resricion. c P-value from wo-ailed -es on saed resricion. * Indicaes θ esimae is saisically differen from zero a he 5% level; κ or λ are saisically differen from one a he 5% level.

12 2 Table 3. Two-Sep Ahead Direc Tess, A + 2 = γ + δa + η( F A ) + ε ( F F ) + u 2 Coefficien Esimaes Hypohesis Tess Marke γ δ η ε γ=0, δ=η=ε= ε=0 Beef (0.007) a (0.25) (0.285) (0.92) * 0.00 b c Pork (0.006) (0.59) (0.2) (0.69) Broilers 0.05 (0.0) (0.244) (0.285) (0.293) Turkeys (0.006) (0.202).079 (0.20) (0.20) Eggs 0.0 (0.005) * 0.52 (0.228) * (0.272) * (0.235) Milk (0.005) (0.285) (0.38) (0.39) a Sandard errors are in parenhesis. b P-value from Chi-squared es for saed resricion. c P-value from wo-ailed -es on saed resricion. * Indicaes γ esimae is saisically differen from zero a he 5% level; δ, η, or ε are saisically differen from one a he 5% level. 2

13 Table 4. Three-Sep Ahead Direc Tess, 2 A φ + κa + ψ F A ) + ω( F F = ( ) + ρ( F F ) + u 3 Coefficien Esimaes Hypohesis Tess Marke φ κ ψ ω ρ φ=0, κ=ψ=ω=ρ= ρ=0 Beef (0.0) a.00 (0.35) (0.363) (0.283) 0.85 (0.229) * b c Pork (0.009) 0.82 (0.237) (0.287) (0.25) 0.44 (0.55) * Broilers (0.02) * (0.275) * 0.47 (0.37) * 0.47 (0.373) (0.260) * Turkeys (0.008).02 (0.286) (0.303) (0.299) 0.90 (0.220) Eggs 0.05 (0.005) * (0.256) * (0.30) (0.246) 0.60 (0.90) * Milk (0.007) (0.40) (0.459) (0.384) (0.290) a Sandard errors are in parenhesis. b P-value from Chi-squared es for saed resricion. c P-value from wo-ailed -es on saed resricion. * Indicaes φ esimae is saisically differen from zero a he 5% level; κ, ψ, ω, or ρ are saisically differen from one a he 5% level. 3