scholarly journals AUTOREGRESSIVE PROCESSES AND GRAIN YIELD FORECASTING IN THE NORTHERN KAZAKHSTAN

2018 ◽  
pp. 98-105
Author(s):  
T. A. Kussaiynov ◽  
A. A. Bulasheva ◽  
Zh. O. Zhakupova
Keyword(s):  
Time Series ◽  
Grain Yield ◽  
Crop Yields ◽  
Linear Trend ◽  
Statistical Properties ◽  
Time Series Models ◽  
Autoregressive Processes ◽  
Agricultural Economy ◽  
The Past ◽  
The North

Time series models are one of the most commonly used forecasting tools in the agricultural economy. In this case, the future values of the variable are function of the past values of the same variable. In other words, there are autoregressive processes. The dynamic of grain yields in the North-Kazakhstan and Kostanay regions of Kazakhstan demonstrate very similar statistical properties. In both cases, there is a positive linear trend, the cyclical development of the process is clearly discernible. Serious attention should also be given to the existence of a cycle in the dynamics of the dispersion level of crop yields. These stochastic features of the indicator should be taken into account in agricultural forecasting.

Approximations and boundary conditions for continuous-time threshold autoregressive processes

1994 ◽  
Vol 31 (4) ◽  
pp. 1103-1109 ◽  
Author(s):  
Rob J. Hyndman
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Continuous Time ◽  
Linear Time ◽  
Autoregressive Processes ◽  
Threshold Autoregressive ◽  
The Past ◽  
Time Threshold ◽  
Non Linear ◽  
The Way

Continuous-time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here which are useful for simulation and inference. Each of the approximating processes implicitly defines conditions on the thresholds, thus providing greater understanding of the way in which boundary conditions arise.

scholarly journals Transmisión de precios en los mercados regionales de ovino en España

2011 ◽  
Vol 3 (1) ◽  
pp. 71 ◽  
Author(s):  
L. Boshnjaku ◽  
B. Ben-Kaabia ◽  
José M. Gil
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Commodity Markets ◽  
Time Series Models ◽  
The North ◽  
Long Run ◽  
Stochastic Properties ◽  
Price Relationships

The analysis of price relationships in commodity markets provides an approximate idea on markets performance as well as allows the researcher to analyze price responses to unanticipated shocks. The objective of this paper is to explore price relationships in geographical separated markets in the Spanish lamb sector. The methodology used is based on the specification of multivariate time series models which are flexible enough to take into account the stochastic properties of data, the multivariate nature of price relationships and to distinguish between short- and long-run horizons. Results indicate that lamb markets in Spain are strongly related being Zafra the leading market. The influence of Zafra is substantial in the southern markets while in the North, the Lonja del Ebro could be considered as the most representative market.

scholarly journals Modeling of COVID-19 Outbreak Indicators in China Between January and June

2020 ◽  
pp. 1-9
Author(s):  
Senol Celik ◽  
Handan Ankarali ◽  
Ozge Pasin
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Bayesian Information Criterion ◽  
Linear Trend ◽  
Growth Curves ◽  
Information Criterion ◽  
Time Series Models ◽  
Cumulative Number ◽  
Mean Square ◽  
Nonlinear Growth

ABSTRACT Objectives: The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. Methods: The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. Results: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. Conclusion: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.

Influence of planting date and environment on Oklahoma wheat grain yield trend from 1963 to 1995

1998 ◽  
Vol 78 (1) ◽  
pp. 71-77 ◽  
Author(s):  
F. M. Epplin ◽  
T. F. Peeper
Keyword(s):  
Grain Yield ◽  
Linear Trend ◽  
Moving Average ◽  
Wheat Yield ◽  
Triticum Aestivum L ◽  
Planting Date ◽  
Wheat Grain ◽  
Explanatory Variables ◽  
The Past ◽  
Key Words Wheat

The five-year moving average (5YRMA) wheat (Triticum aestivum L.) grain yield per harvested hectare has declined by more than 18% over the past decade in Oklahoma. By contrast, world wheat yields have increased steadily over the same period. The Oklahoma wheat yield trend during the past decade is inconsistent with expectations. The objective of the research was to determine why the 5YRMA wheat grain yield per harvested hectare did not increase in Oklahoma during the past decade. Five types of potential explanatory variables were investigated: structural change (including government programs), fertilizer use, proportion grazed, planting date and environment. Regression analysis was used to determine that the consequences of improvements in technology, research and education programs, as measured by a linear trend variable, were positive. However, during the past decade, these advancements were overwhelmed by changes in planting date and environmental factors. Key words: Wheat, yield, trend, planting date, environment

scholarly journals Nonlinear time series models for the North Atlantic Oscillation

2020 ◽  
Vol 6 (2) ◽  
pp. 141-157
Author(s):  
Thomas Önskog ◽  
Christian L. E. Franzke ◽  
Abdel Hannachi
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Nonlinear Models ◽  
Weather Conditions ◽  
Dominant Mode ◽  
Statistical Properties ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models, including both short and long lags, perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution, and the different timescales of the two phases. As a spin-off of the modelling procedure, we can deduce that the interannual dependence of the NAO mostly affects the positive phase, and that timescales of 1 to 3 weeks are more dominant for the negative phase. Furthermore, the statistical properties of the model make it useful for the generation of realistic climate noise.

Nonlinear time series models for the North Atlantic Oscillation

2020 ◽  
Author(s):  
Abdel Hannachi ◽  
Thomas Önskog ◽  
Christian Franzke
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Nonlinear Models ◽  
Weather Conditions ◽  
Dominant Mode ◽  
Statistical Properties ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

<p>The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models including both short and long lags perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution and the different time scales of the two phases. As a spinoff of the modelling procedure, we are able to deduce that the interannual dependence of the NAO mostly affects the positive phase and that timescales of one to three weeks are more dominant for the negative phase. The statistical properties of the model makes it useful for the generation of realistic climate noise.</p>

scholarly journals Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Harry Joe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Probability Generating Function ◽  
Likelihood Estimation ◽  
Model Fit ◽  
Likelihood Inference ◽  
Series Data ◽  
Time Series Models ◽  
Autoregressive Time Series ◽  
The Past

For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.

scholarly journals Realized habitats of early-stage North Sea herring: looking for signals of environmental change

2010 ◽  
Vol 68 (3) ◽  
pp. 537-546 ◽  
Author(s):  
Christine Röckmann ◽  
Mark Dickey-Collas ◽  
Mark R. Payne ◽  
Ralf van Hal
Keyword(s):  
Time Series ◽  
Environmental Change ◽  
North Sea ◽  
Early Stage ◽  
Interannual Variations ◽  
The Past ◽  
The North ◽  
The North Sea ◽  
Juvenile Stages ◽  
Newly Hatched Larvae

Abstract Röckmann, C., Dickey-Collas, M., Payne, M. R., and van Hal, R. 2011. Realized habitats of early-stage North Sea herring: looking for signals of environmental change. – ICES Journal of Marine Science, 68: . Realized habitats of North Sea herring for two larval and two juvenile stages were estimated over the past 30 years, using abundances from surveys tied to modelled estimates of temperature and salinity. Newly hatched larvae (NHL) were found mainly in water masses of 9–11°C, pre-metamorphosis larvae (PML) around 5–6°C, juveniles aged 0 in summer around 13–14°C, and juveniles aged 1 in winter around 4–5°C. The median salinity in which the NHL were distributed was 34.4–35.0 and 33.7–33.9, respectively, for PML and juveniles. Interannual variations in temperature and geographic variables in the North Sea were compared with the time-series of realized habitats. The realized temperature habitats of the NHL did not change over time, but the habitat of juveniles in summer may be associated with higher temperatures. Juveniles aged 1 in winter are found in waters colder than the average for the North Sea, a result also reflected in their geographic shift east into shallower water. The results suggest that juveniles could be limited by temperature, but may also track changes in food or predator distribution, and/or internal population dynamics. Time-series analysis of realized salinity habitats was not possible with the available data because of differences between model outputs.

scholarly journals International Real Estate Review

2015 ◽  
Vol 18 (4) ◽  
pp. 455-471
Author(s):  
William Miles ◽  
Keyword(s):  
Time Series ◽  
Real Estate ◽  
Portfolio Management ◽  
House Prices ◽  
Time Series Models ◽  
Fiscal Policies ◽  
Home Prices ◽  
The Past ◽  
The Uk

Since the recent turmoil in UK housing, there has been controversy over whether house prices in the past decade have entered a bubble. While there are numerous techniques employed to investigate the presence of bubbles, testing the significance of breaks in the dynamics of prices has been utilized in other research to detect such bubbles. This is important in itself, as changing parameters in housing time series models make forecasting and portfolio management more difficult. We examine thirteen regions of the UK as well as the national home prices. The results indicate that while there were some breaks over the 2000s, more regions (and the UK as a whole) experienced breaks over the late 1980s and early 1990s. These results indicate that while there have been large price swings over the past decade, the late 1980s/early 1990s, which followed sharp changes in housing, monetary and fiscal policies, appear to be the larger boom-bust episode.

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