Title page for ETD etd-1030102-095941


Type of Document Dissertation
Author Robledo, Carlos Walter
Author's Email Address crobled@lsu.edu
URN etd-1030102-095941
Title Dynamic Econometric Modeling of the U.S. Wheat Grain Market
Degree Doctor of Philosophy (Ph.D.)
Department Agricultural Economics and Agribusiness
Advisory Committee
Advisor Name Title
Hector O. Zapata Committee Chair
Dek Terrell Committee Co-Chair
Alvin Shupp Committee Member
Michael Salassi Committee Member
Steve Henning Committee Member
Barbara Apostolou Dean's Representative
Keywords
  • seasonal cointegration
  • cointegration
  • dynamic multipliers
  • unit roots
  • commodity market models
  • impulse responses
  • time series models
  • forecasts
  • structural models
  • U.S. wheat market
  • dynamic simulatenous equation models
  • seasonal unit roots
  • vector error correction models
  • seasonal vector error correction models
  • dynamic econometrics
Date of Defense 2002-11-25
Availability unrestricted
Abstract
Structural-time series models have not gained much ground in commodity market modeling despite the overwhelming popularity of time series approaches in forecasting and dynamic analyses. This dissertation contributes by applying developments in seasonal cointegration and structural-time series analysis (e.g., Zellner and Palm (1974); Hsiao (1997); Lee (1992); Franses and Kunst (1999); Ghysels and Osborn, 2001) to the study of agricultural commodity markets. The focus is on three research themes. The first theme investigates the role of cointegration and seasonal cointegration for market data, an issue considered timely because most applications assume deterministic seasonal components. The second issue breaks new ground in agricultural commodity modeling by introducing a new dynamic simultaneous equation model (DSEM) that accounts for seasonal cointegration. Lastly, the research compares the out-of-sample forecasting performance and impulse responses of four multi-equation models for the U.S. wheat market. The forecasting comparisons apply recent developments on testing for differences in mean-squared-errors.

The study adopts a structural model for the U.S. wheat market and estimates four econometric specifications: a vector error-correction model without seasonal cointegration (VECM), a VECM with seasonal cointegration (SVECM), a DSEM with cointegration (CDSEM), and a DSEM with seasonal cointegration (SCDSEM). The conclusions may be summarized as follows. First, quarterly data in the U.S. wheat market (1975:03-1999:04) have seasonal unit roots, therefore, a VECM or DSEM should be specified. Second, in a forecasting context, seasonally cointegrated VECMs perform uniformly better that their nonseasonal counterpart. DSEM with seasonal cointegration, however, perform better than VECMs at longer forecast horizons. Lastly, the impulse response analysis and dynamic multiplier comparisons lead to one salient conclusion, omission of seasonal cointegration components when significant generates unexpected response functions and dynamic multipliers.

Of particular interest for future research is an assessment of the small sample properties of impulse response functions for structural-time series models with seasonal cointegration. From a more pure economic perspective, a similar structural-time series analysis to other agricultural markets seems timely given the new finding that these models may outperform other multiple time series models that are often used in empirical work.

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