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Licensed Unlicensed Requires Authentication Published by De Gruyter October 7, 2020

Price Discovery in the United States Dairy Industry

Yuliya V. Bolotova

Abstract

The organized Exchange spot (cash) cheese market is a private industry institution that has historically performed a primary price discovery function in the U.S. dairy industry. In addition to affecting cheese prices in contract cheese market, the Exchange spot cheese prices have influenced prices paid for milk at the farm level that are set within the system of Federal Milk Marketing Orders (FMMOs). The effects that the Exchange spot cheese market has on FMMOs milk pricing attract increased attention in light of the recent concerns about increasing milk price volatility and its effect on the dairy farm profitability. While the design of milk pricing within FMMOs has evolved over the last three decades, the effects of the Exchange spot cheese prices on FMMOs milk pricing have intensified. The research analyzes the conduct and performance of the Exchange spot cheese market during three FMMOs milk pricing regimes (Minnesota-Wisconsin price regime, Basic Formula Price and modern Multiple Component Pricing regime), with a particular focus on the Exchange spot cheese price behavior.

JEL Classification: K2; L1; Q1

Corresponding author: Yuliya V. Bolotova, A Former Assistant Professor of Agribusiness, Department of Agricultural Sciences, College of Agriculture, Forestry and Life Sciences, Clemson University, Clemson, SC 29634, USA, E-mail:

Appendix

Regression Diagnostics

  • The spot cheese price series is a stationary series. The null hypothesis of a presence of the unit root was rejected using a standard Dickey-Fuller unit root test for models with 1–10 lags. For example, the Dickey-Fuller test statistic for the model with one lag is −5.30, which exceeds in absolute value the critical value of −2.75 (10% significance level). Furthermore, the correlograms of autocorrelation function (ACF) and partial autocorrelation function (PACF) indicate that autocorrelation substantially declines at the second lag value. This evidence supports the stationarity of the spot cheese price series.

  • To determine the number of lagged cheese prices to be included in the conditional mean equation, statistical significance of the estimated coefficients for alternative number of lagged cheese prices as well as the presence of autocorrelation are evaluated. In the case of both econometric models, no autocorrelation is detected in the models with two lagged cheese prices; both lags exhibit statistical significance. In addition, the Akaike and Schwarz information criteria (AIC and SC) are evaluated for the model specifications with different lag structures. Both AIC and SC are minimized, when the first lag and the second lag of the cheese price are included in the conditional mean equation.

  • The ARCH test is used to test for the presence of autoregressive conditional heteroscedasticity (Hamilton 1994; Kennedy 2003). The ARCH test is based on the Lagrange multiplier principle. The LM statistic is N*R2, where N is the number of observations and R2 is a goodness-of-fit statistic from the regression of the squared residuals obtained from the OLS regression on a constant and m lagged values of the squared residuals. This statistic has an asymptotic Chi-square distribution with m degrees of freedom. The null hypothesis of homoscedasticity is rejected at a p-value equal to 0.0500 (5% significance level) in the case of both econometric models (Table 4). The ARCH test outcomes suggest that the ARCH model may be suitable for describing the error process.

  • To determine the order of the ARCH process (conditional variance equation), the magnitude and statistical significance of the estimated coefficients for the lagged squared error terms in the variance equation are evaluated. In the case of both econometric models, the estimated coefficient for the first lag of the squared error term has expected positive sign (the stationarity condition is satisfied). In the case of Model 2, the estimated coefficient for the first lag of the squared error term is statistically significant from zero using a one-tailed t-test (T statistic is 1.50; the T-statistic critical value at the 10% significance level is 1.28). In the case of Model 1, the estimated coefficient for the first lag of the squared error term is close to being statistically significant from zero using a one-tailed t-test (T statistic is 1.17, the T-statistic critical value at the 10% significance level is 1.28). In addition, the Akaike and Schwarz information criteria (AIC and SC) are evaluated for the model specifications with different lag structures. Both AIC and SC are minimized when the first lag of the squared error term is included in the variance equation. Therefore, ARCH(1) model is used in econometric analysis.

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Received: 2019-01-18
Accepted: 2020-09-15
Published Online: 2020-10-07

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