scholarly journals Analysis of development of raw cow milk prices in the conditions of the Slovak Republic

10.5219/1196 ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 906-914
Author(s):  
Ivana Váryová ◽  
Zuzana Poláková ◽  
Iveta Košovská ◽  
Alexandra Ferenczi Vaňová ◽  
Renáta Krajčírová
Keyword(s):  
Time Series ◽  
Slovak Republic ◽  
Future Trend ◽  
Stationary Time Series ◽  
Cow Milk ◽  
Moderate Increase ◽  
Monthly Data ◽  
Market Report ◽  
Milk And Dairy Products

The paper is focused on the evaluation of the price development of raw cow milk in the Slovak Republic. The aim of the paper is to analyse the development of average prices of the raw Q class cow´s milk in 2006 – 2018 and to forecast the trend of these prices by June 2019. Monthly data from the Market Report of Milk and Dairy Products issued by the Agricultural Information Department – ATIS, as part of the Agricultural Paying Agency, were the base of our information resource. These data were analyzed by using the statistical software called SAS. Box-Jenkins methodology was used to model the future trend of average purchase prices of the raw Q class cow´s milk, designed for modeling stationary and non-stationary time series and time series with seasonal components. During the period of 2006 – 2018 the Slovak dairy market showed significant changes in the prices of raw Q class cow´s milk. Three crisis periods of the dairy sector have been identified, during which the milk price has fallen below 0.30 € per kilogram. Long-term low prices of raw cow milk led to the liquidation of primary milk producers. In the next forecast period, by February 2019 a moderate increase in the average purchase price of raw Q class cow´s milk is expected, followed by a decrease by June 2019.

Following the infection dynamics of the tropical trematode Oligogonotylus mayae in its intermediate and definitive hosts for 13 years

2020 ◽  
Vol 94 ◽  
Author(s):  
A.L. May-Tec ◽  
N.A. Herrera-Castillo ◽  
V.M. Vidal-Martínez ◽  
M.L. Aguirre-Macedo
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Southern Oscillation ◽  
Fish Host ◽  
Snail Host ◽  
Monthly Data ◽  
Infection Dynamics ◽  
Infection Parameters ◽  
Over Time

Abstract We present a time series of 13 years (2003–2016) of continuous monthly data on the prevalence and mean abundance of the trematode Oligogonotylus mayae for all the hosts involved in its life cycle. We aimed to determine whether annual (or longer than annual) environmental fluctuations affect these infection parameters of O. mayae in its intermediate snail host Pyrgophorus coronatus, and its second and definitive fish host Mayaheros urophthalmus from the Celestun tropical coastal lagoon, Yucatan, Mexico. Fourier time series analysis was used to identify infection peaks over time, and cross-correlation among environmental forcings and infection parameters. Our results suggest that the transmission of O. mayae in all its hosts was influenced by the annual patterns of temperature, salinity and rainfall. However, there was a biannual accumulation of metacercarial stages of O. mayae in M. urophthalmus, apparently associated with the temporal range of the El Niño-Southern Oscillation (five years) and the recovery of the trematode population after a devasting hurricane. Taking O. mayae as an example of what could be happening to other trematodes, it is becoming clear that environmental forcings acting at long-term temporal scales affect the population dynamics of these parasites.

scholarly journals Analýza vybraných indikátorov hodnotenia zmeny klímy na území SR (pre účely podpory rozvoja záchranných služieb)

2021 ◽  
Vol 20 (2) ◽  
pp. 16-24
Author(s):  
Iveta Marková ◽  
◽  
Mikuláš Monoši
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Air Temperature ◽  
Drought Index ◽  
Slovak Republic ◽  
Atmospheric Precipitation ◽  
Annual Total ◽  
State Of The Environment ◽  
Climatic Elements

The development of climate change is evaluated based on trends in long-term time series (1951 - 2018) of individual climatic elements, comparing values of individual years with the standard period in climatology 1961 - 1990 (SAŽP, 2019). The aim of the article is to evaluate climate elements, namely the production of greenhouse gases, average annual air temperature, annual total atmospheric precipitation, drought index and annual soil temperature (soil index). Data presented in the article are obtained from public reports on the state of the environment in the Slovak Republic and other related documents. In 1881 - 2018, Slovakia underwent significant changes in all monitored climatic elements. The most crucial changes occurred in 2017 and 2018.

scholarly journals Expressions of climatic change in Slovak Republic

2020 ◽  
pp. 145-156
Author(s):  
Iveta Marková ◽  
Mikuláš Monoši
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Air Temperature ◽  
Drought Index ◽  
Slovak Republic ◽  
Atmospheric Precipitation ◽  
Normal Period ◽  
State Of The Environment ◽  
Climatic Elements

The development of climate change is evaluated on the basis of trends in a long-term time series (1951–2018) of individual climatic elements by comparing values from individual years with the normal period in climatology of 1961–1990. The aim of the article is to present the manifestations of climate change in Slovakia (since its inception) according to selected indicators: (1) average annual air temperature, (2) soil temperature, (3) total atmospheric precipitation and (4) drought index over the last decade. The data presented in the article were obtained from public reports on the state of the environment in the Slovakia and other related documents. Slovakia, during the years 1881–2018, underwent significant changes in all monitored climatic elements. The most significant changes were in 2017 and 2018.

COINTEGRATING THE LONG-RUN RELATIONSHIP OF ECONOMIC VARIABLES

2021 ◽  
Vol 14(63) (1) ◽  
pp. 139-152
Author(s):  
Constantin Duguleana
Keyword(s):  
Time Series ◽  
Practical Importance ◽  
Main Tool ◽  
Stationary Time Series ◽  
Correction Model ◽  
Long Run ◽  
Time Variables ◽  
Relationship Of ◽  
Cointegration Relationship

"The economic non-stationary time series often have long-run relationships. The cointegration relationship of time variables describes the continuous adaptation to their equilibrium in the long-run. This paper presents the ways of analysing and modelling the cointegration of time series. The Error Correction Model, as a main tool, and the Engle-Granger method are used to estimate the cointegration in the case of the long-run relationship between the quarterly GDP and the Final Consumption in Romania during the period 1995 – 2019. The practical importance of applying the cointegrating model consists in knowing the effect of GDP in the long term. "

Long-term correlations in short, non-stationary time series: An application to international R&D collaborations

2019 ◽  
Vol 13 (2) ◽  
pp. 583-592 ◽  
Author(s):  
Lorenzo Righetto ◽  
Alessandro Spelta ◽  
Emanuele Rabosio ◽  
Fabio Pammolli
Keyword(s):  
Time Series ◽  
Stationary Time Series ◽  
Stationary Time

Exchange rates, Canadian lumber imports, and United States prices: a time-series analysis

1988 ◽  
Vol 18 (12) ◽  
pp. 1587-1594 ◽  
Author(s):  
Joseph Buongiorno ◽  
Jean-Paul Chavas ◽  
Jussi Uusivuori
Keyword(s):  
United States ◽  
Time Series ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
The United States ◽  
Monthly Data ◽  
Softwood Lumber ◽  
The Past ◽  
Series Analysis

Softwood lumber imports by the United States from Canada more than doubled during the past 10 years. The objective of this paper was to investigate two possible reasons for this change: (i) the increase in value of the U.S. dollar relative to the Canadian dollar, and (ii) the rise in the price of softwood lumber in the United States. The method used was time-series analysis, leading to measures of feedback and long-term multipliers between imports, exchange rate, and U.S. price. The results, based on monthly data from January 1974 to January 1986, suggested that 68% of the rise in Canadian imports during this period was due to the rise in the price of softwood lumber in the United States. The exchange rate, however, was not found to have a significant effect on imports. The findings also indicate that the increase in imports has not led to a decline in the price received by U.S. producers.

scholarly journals Has the Real-Time Reliability of Monthly Indicators Changed over Time?

2019 ◽  
pp. 1-7
Author(s):  
Mark Bognanni
Keyword(s):  
Time Series ◽  
Real Time ◽  
Economic Data ◽  
Short Term ◽  
Monthly Data ◽  
The Real ◽  
Data Release ◽  
Macroeconomic Indicators ◽  
Over Time

Economic data are routinely revised after they are initially released. I examine the extent to which the real-time reliability of six monthly macroeconomic indicators important to policymakers has remained stable over time by studying the time-series properties of their short-term and long-term revisions. I show that the revisions to many monthly economic indicators display systematic behaviors that policymakers could build into their real-time assessments. I also find that some indicators’ revision series have varied substantially over time, suggesting that these indicators may now be less useful in real time than they once were. Lastly, I find that substantial revisions tend to occur indefinitely after the initial data release, a result which suggests a certain degree of caution is in order when using even thrice-revised monthly data in policymaking.

scholarly journals GENERALIZED APPROACH TO HURST EXPONENT ESTIMATING BY TIME SERIES

2018 ◽  
Vol 8 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Lyudmyla Kirichenko ◽  
Tamara Radivilova ◽  
Vitalii Bulakh
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Hurst Exponent ◽  
Hypothesis Test ◽  
Interval Estimation ◽  
Stationary Time Series ◽  
Similar Time ◽  
Self Similar ◽  
Short Time

This paper presents a generalized approach to the fractal analysis of self-similar random processes by short time series. Several stages of the fractal analysis are proposed. Preliminary time series analysis includes the removal of short-term dependence, the identification of true long-term dependence and hypothesis test on the existence of a self-similarity property. Methods of unbiased interval estimation of the Hurst exponent in cases of stationary and non-stationary time series are discussed. Methods of estimate refinement are proposed. This approach is applicable to the study of self-similar time series of different nature.

Modeling and Predicting Total Electron Content by Semi-Parametric Autoregressive (AR) Model

2012 ◽  
Vol 457-458 ◽  
pp. 705-709
Author(s):  
Xiu Hai Li
Keyword(s):  
Time Series ◽  
Total Electron Content ◽  
Stationary Time Series ◽  
Ar Model ◽  
Electron Content ◽  
Short Term ◽  
Total Electron ◽  
Modeling Methodology ◽  
Seasonal Time Series

Based on dynamic data system(DDS) modeling methodology, after transformed a seasonal time series for total electron content(TEC) of the ionosphere into a stationary time series by differencing technique, stationary TEC values are modeled by the autoregressive(AR) model. In order to correct model’s systematic errors, authors proposed that AR model is improved by non-parameters introduced to AR model and the ionospheric TEC is predicted using the improved AR model which is called semi-parametric AR model. Preliminary results show that the semi-parametric AR model has a good performance than one of the AR model for short-term TEC prediction while, for relatively long-term TEC prediction, the performance of the semi-parametric AR model is no less than one of AR model.

scholarly journals PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)

2018 ◽  
Vol 7 (1) ◽  
pp. 96-109
Author(s):  
Helmi Panjaitan ◽  
Alan Prahutama ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Data ◽  
Moving Average ◽  
Step Function ◽  
Local Economy ◽  
Stationary Time Series ◽  
Series Data ◽  
Short Term

Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting

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