scholarly journals Polymodeling of time series structures, neighborhood of residuals distribution, wavelet transformation for meso-dynamics assessment

2021 ◽  
Vol 20 (10) ◽  
pp. 1951-1972
Author(s):  
Valerii K. SEMENYCHEV ◽  
Galina A. KHMELEVA ◽  
Anastasiya A. KOROBETSKAYA
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Middle Term ◽  
Monthly Data ◽  
Russian Economy ◽  
Regular Time ◽  
Modeling And Forecasting ◽  
Heavy Tailed ◽  
Auto Regression ◽  
Adequate Analysis

Subject. The article provides the results of meso-dynamics analysis of main twelve industries, based on monthly data for 82 Russian regions, from January 2005 till December 2020. Objectives. The purpose of the study is to address the problem of balanced and stable spatial development of Russia’s regions and Russia, which requires modeling of adequate tools and forecasting nonlinear mesodynamics. Methods. The study follows the econophysics methodology. Results. We consider additive and multiplicative interactions of regular time series components between each other and the residuals, thus expanding the scope of tools application for the variety of considered industries and their models. Using the common and new trend models, we analyze structural changes, introduce the topological measure of proximity to the neighborhood of residuals with heavy-tailed distribution, which is estimated by median values of trends and cycles for regular components. The traditional time series decomposition (by trend, cycle, seasonality, and residual) is supplemented by our unique complex of wavelet transformations, which forms the models of cycles, using auto regression. We obtained representative and time-synchronized analytical estimates of regular components of industries’ dynamics for meso- and macro-indicators of the Russian economy that have higher accuracy than the known results for the accuracy of modeling and forecasting. Conclusions. The offered methodology and tools enable a more adequate analysis of non-linear dynamics of regions’ middle-term development. They help shift to growth point identification, create the atlas of economic industrial cycles, analyze stages of bifurcations and scenario predictive planning.

Statistical analysis of structural changes in agriculture

2020 ◽  
pp. 54-62
Author(s):  
Maria Vyacheslavovna Kagirova
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Agricultural Sector ◽  
Industrial Relations ◽  
Land Resources ◽  
Agricultural Enterprises ◽  
Russian Economy ◽  
Low Efficiency

Digital transformations in the Russian economy lead to significant internal changes in the formation of industrial relations in all types of activities and, in particular, in the agricultural sector. This arouses particular interest in the study of structural shifts in agricultural sectors that have occurred under the influence of external economic factors and changes in applied technologies. The study, based on the analysis of long-term time series and panel data, revealed significant structural changes in the production of agricultural products in general and by types of products in the context of producer categories and regions of the Russian Federation. Methods for identifying trends in time series made it possible to establish the increasing role of large and medium-sized agricultural enterprises in providing food to the country’s population during the redistribution of land resources towards the household sector with low efficiency of their use. In agricultural production, the use of digital technologies is currently of a point nature, which leads to the emergence of leading regions with innovative large-format production identified in this study. The analyzed structural changes will make it possible to determine the directions in clarifying the system of indicators for monitoring digitalization processes in agriculture.

scholarly journals Modeling and Forecasting of Tourism Demand in Malaysia

2020 ◽  
Vol 03 (12) ◽  
Author(s):  
Siti Syahirah Abdul Halim ◽  
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
The Philippines ◽  
Series Method ◽  
Monthly Data ◽  
Foreign Tourist ◽  
The United Kingdom ◽  
Modeling And Forecasting ◽  
Time Series Method ◽  
Tourist Demand

The pattern of foreign tourist demand to Malaysia is analysed and forecasted using time series method and non-linear technique. There are nine selected countries that contribute a lot to tourist arrivals to Malaysia, namely Australia, Brunei, China, Indonesia, India, Japan, the Philippines, South Korea, and the United Kingdom. Box-Jenkins time series method and Singular Spectrum Analysis are conducted and compared to study the best model to forecast the foreign tourist demand to Malaysia. Monthly data of tourism arrival in 1990 to 2014 were used and the forecasting were compared with 2015. Based on the results obtained, the forecasting model of Box-Jenkins time series method is the best model based on the percentage accuracy in forecasting the tourist demand to Malaysia.

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

Diversionary Politics and Territorial Disputes: Evidence from Turkish Airspace Incursions

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Marius Mehrl ◽  
Ioannis Choulis
Keyword(s):  
Time Series ◽  
Foreign Policy ◽  
Empirical Support ◽  
Full Scale ◽  
Interstate Conflict ◽  
Time Series Models ◽  
Territorial Disputes ◽  
Territorial Dispute ◽  
Monthly Data

Abstract Diversionary theories of interstate conflict suggest that domestic problems push leaders to initiate hostilities against foreign foes in order to garner support. However, the empirical support for this proposition is mixed as critics point out that leaders should not start conflicts that can be extremely costly for them, potentially even removing them from office. We propose that while leaders may not initiate new conflicts, they do tap into existing territorial disputes when facing internal disapproval. That is, they engage in material acts of foreign policy showing domestic audiences that they defend or emphasize their country's claim while being unlikely to result in full-scale armed confrontations. To test this claim, we use monthly data, covering the period 2013–2020, on leader approval and incursions into contested airspace from Turkey's long-standing territorial dispute with Greece. Results from time-series models offer support for our expectation.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

scholarly journals Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1832
Author(s):  
Mariano Méndez-Suárez
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Marketing Mix ◽  
Structural Equations ◽  
Superior Performance ◽  
Parameter Estimates ◽  
Significance Analysis ◽  
Autocorrelation Structure ◽  
Multichannel Retailing ◽  
Black Friday

Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing mix modeling, sampling bootstrapping shows inconsistencies that arise because the series has an autocorrelation structure and contains seasonal events, such as Christmas or Black Friday, especially in multichannel retailing, making the significance analysis of the PLS-SEM model unreliable. The alternative proposed in this research uses maximum entropy bootstrapping (meboot), a technique specifically designed for time series, which maintains the autocorrelation structure and preserves the occurrence over time of seasonal events or structural changes that occurred in the original series in the bootstrapped series. The results showed that meboot had superior performance than sampling bootstrapping in terms of the coherence of the bootstrapped data and the quality of the significance analysis.

A Standard Aggregative Model for the Jordanian Economy

1985 ◽  
Vol 17 (1) ◽  
pp. 67-88
Author(s):  
W. M. Mikhail
Keyword(s):  
Time Series ◽  
Simple Model ◽  
Structural Changes ◽  
Time Series Model ◽  
National Accounts ◽  
Macroeconomic Variables

The simple model presented in this paper is an econometric time-series model which was designed to use the available Jordanian national accounts statistics. It aims at explaining the structural changes in the Jordanian economy in the 1970s as well as projecting values of certain macroeconomic variables for the year 1985, that being the terminal year in the current 5-year plan.

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