scholarly journals The role of transition regime models for corn prices forecasting

2022 ◽  
Vol 60 (2) ◽  
Author(s):  
Vinícius Phillipe de Albuquerquemello ◽  
Rennan Kertlly de Medeiros ◽  
Diego Pitta de Jesus ◽  
Felipe Araujo de Oliveira
Keyword(s):  
Market Structure ◽  
Structural Breaks ◽  
Linear Trend ◽  
Econometric Models ◽  
Price Forecasting ◽  
Transition Regime ◽  
Forecasting Accuracy ◽  
Threshold Autoregressive ◽  
Corn Prices

Abstract: Given the relevance of corn for food and fuel industries, analysts and scholars are constantly comparing the forecasting accuracy of econometric models. These exercises test not only for the use of new approaches and methods, but also for the addition of fundamental variables linked to the corn market. This paper compares the accuracy of different usual models in financial macro-econometric literature for the period between 1995 and 2017. The main contribution lies in the use of transition regime models, which accommodate structural breaks and perform better for corn price forecasting. The results point out that the best models as those which consider not only the corn market structure, or macroeconomic and financial fundamentals, but also the non-linear trend and transition regimes, such as threshold autoregressive models.

Time Varying Trade Intensities and the Deutsche Telekom IPO / Zeitvariable Handelsintensitaten und die Deutsche Telekom IPO

2000 ◽  
Vol 220 (6) ◽  
Author(s):  
Reinhard Hujer ◽  
Joachim Grammig ◽  
Stefan Kokot
Keyword(s):  
Initial Public Offering ◽  
Structural Breaks ◽  
Duration Model ◽  
Time Varying ◽  
Threshold Autoregressive ◽  
Public Offering ◽  
Autoregressive Conditional Duration ◽  
Autoregressive Conditional Duration Model

SummaryWe apply the Threshold Autoregressive Conditional Duration Model (TACD) as proposed by Zhang, Russell, and Tsay (1999) to model the after market trading duration process associated with the initial public offering of the Deutsche Telekom AG share in November of 1996. Special emphasis is devoted to the empirical specification of intra-day seasonality and to the detection of non-stationarity and structural breaks in the trading process.

scholarly journals Selecting a Model for Forecasting

Econometrics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 26
Author(s):  
Jennifer L. Castle ◽  
Jurgen A. Doornik ◽  
David F. Hendry
Keyword(s):  
Structural Breaks ◽  
Simulation Experiment ◽  
Structural Break ◽  
Static Model ◽  
Forecast Period ◽  
Forecast Performance ◽  
Significance Level ◽  
Forecasting Models ◽  
Theoretical Results

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either in the first forecast period or just before. Theoretical results are derived for a three-variable static model, but generalized to include dynamics and many more variables in the simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world, namely that variables should be retained if their noncentralities exceed unity, still applies in settings with structural breaks. This provides support for model selection at looser than conventional settings, albeit with many additional features explaining the forecast performance, and with the caveat that retaining irrelevant variables that are subject to location shifts can worsen forecast performance.

scholarly journals The role of CSP in the electricity system of South Africa – technical operation, grid constraints, market structure and economics

2017 ◽  
Author(s):  
Christoph Kost ◽  
Chris Friebertshäuser ◽  
Niklas Hartmann ◽  
Thomas Fluri ◽  
Peter Nitz
Keyword(s):  
South Africa ◽  
Market Structure ◽  
Electricity System ◽  
Technical Operation

scholarly journals Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models

2015 ◽  
Vol 7 (5) ◽  
pp. 127-136 ◽  
Author(s):  
Yumurtaci Aydo mu Hacer ◽  
Ekinci Aykut ◽  
Erdal Halil ◽  
Erdal Hamit
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Forecasting Accuracy ◽  
Ensemble Models ◽  
Bagging Ensemble

scholarly journals Influence of the ambient humidity on the concentration of natural deposition-mode ice-nucleating particles

2016 ◽  
Vol 16 (2) ◽  
pp. 927-932 ◽  
Author(s):  
M. L. López ◽  
E. E. Ávila
Keyword(s):  
Relative Humidity ◽  
Linear Trend ◽  
Ground Level ◽  
Ambient Air ◽  
Air Masses ◽  
Ambient Relative Humidity ◽  
Thermodynamic Conditions ◽  
Rainy Days ◽  
Deposition Mode

Abstract. This study reports measurements of deposition-mode ice-nucleating particle (INP) concentrations at ground level during the period July–December 2014 in Córdoba, Argentina. Ambient air was sampled into a cloud chamber where the INP concentration was measured at a temperature of −25 °C and a 15 % supersaturation over ice. Measurements were performed on days with different thermodynamic conditions, including rainy days. The effect of the relative humidity at ground level (RHamb) on the INP concentration was analyzed. The number of INPs activated varied from 1 L−1 at RHamb of 25 % to 30 L−1 at RHamb of 90 %. In general, a linear trend between the INP concentration and the RHamb was found, suggesting that this variability must be related to the effectiveness of the aerosols acting as INPs. From the backward trajectories analysis, it was found that the link between INP concentration and RHamb is independent of the origin of the air masses. The role of biological INPs and nucleation occurring in pores and cavities was discussed as a possible mechanism to explain the increase of the INP concentration during high ambient relative humidity events. This work provides valuable measurements of deposition-mode INP concentrations from the Southern Hemisphere where INP data are sparse so far.

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