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Abstract
We pursue two distinct approaches to measuring the stability and validity of conventional
approaches to measuring acreage response. Our focus is on corn and soybeans,
which have perhaps been the most significantly impacted of the main commodities
by market and policy changes. As noted, much of the change impacting commodity
markets has been triggered by bio–energy policies, with ethanol from corn being
the most prominent renewable fuel targeted by these policies. These policies have
included ethanol tariffs and tax–credits for gasoline blenders. We first consider the
structural stability of a standard acreage response model of the form often estimated
in the empirical literature (see, for example, the seminal paper of Chavas and Holt
(1990)). We apply structural change tests capable of identifying structural changes
occurring at unknown break points and at the ends of a data series. The latter approach
to testing is especially important in this application since the most substantial
changes in markets have occurred since the 2007 Energy Independence Act. We then
consider an analogous empirical evaluation of acreage response using panel data made
up of annual observations taken at the crop reporting district (CRD) in the major
corn producing states (i.e., the Corn Belt). We apply the newly developed inferential
technique suggested by Cameron, Gelbach, and Miller (2011) that permits one
to account for multi–dimensional clustering in panel data. Implications for modeling
acreage response under changing market and policy conditions are discussed.