Forecasting Economic Indicators of Transport Activity

2021 ◽  
pp. 70-78
Author(s):  
D. Biryukov ◽  
O. Rod'kina ◽  
Ruslan Vakulenko ◽  
D. Lapaev
Keyword(s):  
Correlation Analysis ◽  
Economic Activity ◽  
Regression Models ◽  
Economic Indicators ◽  
Transport Sector ◽  
Linear Regression Models ◽  
Transport Activity ◽  
Statistical Accuracy ◽  
Predictive Regression ◽  
And Storage

The article discusses the methodological and practical aspects of forecasting the economic indicators of the transport sector at the level of a transport company and the type of economic activity. The development of forecasting methodology at the present time is analyzed. The necessity, features and main directions of development of the forecasting methodology for the type of economic activity are revealed. The methodological basis for forecasting the development of the transport sector is investigated and characterized. A method for forecasting transportation and storage as a type of economic activity under conditions of uncertainty is proposed and tested. Based on the results of the correlation analysis, subsets of predicted indicators and factors were formed that were optimal for constructing the corresponding linear regression models. Predictive regression models have been developed, their significance and statistical accuracy have been confirmed.

scholarly journals INFLUENCE OF THE VEGETATION PERIOD ON THE SPREAD OF ECONOMICALLY IMPORTANT VIRAL DISEASES IN BURLEY TOBACCO

2019 ◽  
Vol 17 (1) ◽  
pp. 28-33
Author(s):  
Y. Yonchev ◽  
N. Keranova
Keyword(s):  
Correlation Analysis ◽  
Regression Models ◽  
Positive Impact ◽  
Vegetation Period ◽  
The Other ◽  
Viral Diseases ◽  
Strong Positive Correlation ◽  
Linear Regression Models ◽  
Burley Tobacco ◽  
Positive Effect

The present study explores the influence of the vegetation period of Burley tobacco on the spread of viruses such as TMW, PVY-Complex, CMV / PVY-Complex, TSWV and CMV. To establish this relation, a correlation analysis is applied and the proven effects are represented by linear regression models. In 2014, the number of days from replanting has a strong positive impact on the percentage of plants infected by PVY-Complex (0.985**) as well as by TMV (0.781*). For 2015, the very strong effect was only seen on CMV / PVY-Complex (0.976**). In 2016, the duration of the period had a positive effect on the spread of CMV / PVY-Complex (0.868*), CMV (0.904 **) and TSWV (0.966**). In 2017 there is a very strong positive correlation between PVY-Complex (0.885*), CMV (0.948**) and TSWV (0.955**) on one hand and the planting period on the other. As a result of the conducted study over the entire four-year period, it has been proven that during the first two years the increase in the vegetation period leads to an increase in the incidence of PVY complex. During the second half of the analyzed period, CMV and TSWV are proved to be affected by the length of the time from the replanting.

scholarly journals Water Resource Carrying Capacity Based on Water Demand Prediction in Chang-Ji Economic Circle

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 16
Author(s):  
Ge Wang ◽  
Changlai Xiao ◽  
Zhiwei Qi ◽  
Xiujuan Liang ◽  
Fanao Meng ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Correlation Analysis ◽  
Water Resource ◽  
Carrying Capacity ◽  
Regression Models ◽  
Water Shortage ◽  
Linear Regression Models ◽  
Gray Correlation ◽  
Multiple Linear Regression Models

In view of the large spatial difference in water resources, the water shortage and deterioration of water quality in the Chang-Ji Economic Circle located in northeast China, the water resource carrying capacity (WRCC) from the perspective of time and space is evaluated. We combine the gray correlation analysis and multiple linear regression models to quantitatively predict water supply and demand in different planning years, which provide the basis for quantitative analysis of the WRCC. The selection of research indicators also considers the interaction of social economy, water resources, and water environment. Combined with the fuzzy comprehensive evaluation method, the gray correlation analysis and multiple linear regression models to quantitatively and qualitatively evaluate the WRCC under different social development plans. The developmental trends were obtained from 2017 to 2030 using four plans designed for distinct purposes. It can be seen that the utilization of water resource is unreasonable now and maintains a poor level under a business-as-usual Plan I. Plan II and Plan III show that resource-based water shortage is the most critical issue in this region, and poor water quality cannot be ignored either. Compared with Plan I, the average index of WRCC in Plan IV increased by 51.8% and over 84% of the regions maintain a good level. Strengthening sewage treatment and properly using transit water resources are more conducive to the rapid development of Chang-Ji Economic Circle.

scholarly journals SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES

2015 ◽  
Vol 32 (6) ◽  
pp. 1376-1433 ◽  
Author(s):  
Junhui Qian ◽  
Liangjun Su
Keyword(s):  
Regression Models ◽  
Structural Changes ◽  
Equity Premium ◽  
Asymptotic Distributions ◽  
Regression Coefficients ◽  
Tuning Parameter ◽  
Linear Regression Models ◽  
Predictive Regression ◽  
Finite Samples ◽  
Fundamental Information

In this paper, we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso. We show that with probability tending to one, our method can correctly determine the unknown number of breaks, and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a data-driven method to determine the tuning parameter. Monte Carlo simulations demonstrate that the proposed method works well in finite samples. We illustrate the use of our method with a predictive regression of the equity premium on fundamental information.

scholarly journals CRYPTOCURRENCY MARKET TRENDS AND FUNDAMENTAL ECONOMIC INDICATORS: CORRELATION AND REGRESSION ANALYSIS

2021 ◽  
Vol 3 (38) ◽  
pp. 249-261
Author(s):  
O. Baranovskyi ◽  
M. Kuzheliev ◽  
D. Zherlitsyn ◽  
K. Serdyukov
Keyword(s):  
Correlation Analysis ◽  
Financial Markets ◽  
Financial Market ◽  
Regression Models ◽  
Economic Indicators ◽  
Financial Assets ◽  
The World ◽  
Dollar Price ◽  
Market Trends ◽  
The Impact

Abstract. The first cryptocurrency was born in 2008. Already today, virtual financial assets and tokens are a significant part of trading in global financial markets. The cryptocurrency market capitalization currently exceeds 600 billion U.S. dollars. However, there is a lot of discussion about cryptocurrency functions and the correlation between Bitcoin prices and the basic economic indices. Therefore, the purpose of the paper is to define the statistical substantiation of the influence of fundamental economic indicators on the market of virtual financial assets and the possibility of using cryptocurrency as the investment assets. This article is based on the theoretical principles and methods of econometric analysis; the system approach methods to define the main vehicles and trends of the international financial market. The study presents correlation analysis, regression models with paired and multiple variables. For these models, R-Studio instruments are the main tools of quality estimation and results interpretation. The article shows the results of the correlation analysis of Bitcoin’s U.S. dollar price dynamics and changes in the main stock, monetary market indicators, cryptocurrencies market tendency, levels of the United States fundamental economic indicators for the period from 2014 to 2021. Traditional multifactorial regression models are used to determine the level and the impact of individual indicators of the world stock market at the U.S. dollar price of Bitcoin. A comparison of the level of volatility of key investment financial assets in the market of cryptocurrencies and stock markets is carried out. The authors determine the level of correlation dependence and make a regression model of the impact of fundamental economic indicators and stock market trends on the dynamics of U.S. dollar prices for key cryptocurrencies. The article presents conclusions on trends and problems of using cryptocurrencies as an investment asset, considering volatility and profitability. Implementation of the results allows to clarify the economic essence of cryptocurrencies as a specific financial vehicle, as well as improving the existing models of investment management, considering the statistical characteristics of the virtual financial assets. The main direction of further research is to build models of medium-term prediction of prices for the main cryptocurrencies as an investment asset in conditions of changes in global financial markets, which must consider the fundamental economic indicators of the world economy and trends on key stock and commodity markets. Keywords: virtual financial asset, cryptocurrency, bitcoin, econometric model, financial market, economic indicator, investment asset. JEL Classification D53, E44, G15, C58 Formulas: 3; fig.: 3; tabl.: 3; bibl.: 31.

scholarly journals Study of the dependencies between more important pomological indicators in Cornel-Tree genotypes

2019 ◽  
Vol 42.1 ◽  
pp. 7153-7161
Author(s):  
Argir Zhivondov ◽  
Neli Keranova ◽  
Svetla Pandova
Keyword(s):  
Linear Regression ◽  
Correlation Analysis ◽  
Regression Models ◽  
Linear Models ◽  
Fruit Weight ◽  
Linear Regression Models ◽  
Complex Effect ◽  
Cornus Mas ◽  
The Subject ◽  
Positive Effect

The object of this study is nine genotypes of Cornus mas L.: Kazanlashki pear-shaped, Pancharevski cylindrical, Shumenski oblong, Yaltenski, Vratsa-Castel Sandryan, Atkov cornel-tree, Tsarigradski yellow and Yellow Hadjiiski, distributed in the territory of Bulgaria. The objective of the study is the analysis of the impacts between more important pomological indicators and their presentation through linear models. The impacts between weight, length and width of the fruit, length of the stem, weight, length and width of the stone more important pomological indicators were researched by applying correlation analysis. The proven dependencies were evaluated and modelled by linear regression models presenting the complex effect of the tested signs on the weight of the fruit. The length of the fruit (0.907), its width (0.746), and the length of the stem (0.605), the stone weight (0.755), its length (0.787) and its width (0.605) had positive effect on fruit weight. After a regression equation was worked out, it was found that 90% of the dispersion of the dependent variable could be explained by the alteration of the irrigation, soil cultivation, pruning, which are not the subject of this study.

scholarly journals Correlation between fibre length characteristics and mechanical properties of Tectona grandis wood

2012 ◽  
Vol 4 (2) ◽  
pp. 258-263
Author(s):  
D. N. Izekor ◽  
J. A. Fuwape
Keyword(s):  
Mechanical Properties ◽  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Regression Models ◽  
Fibre Length ◽  
Tree Height ◽  
Tectona Grandis ◽  
Linear Regression Models ◽  
Similar Class ◽  
The Relationship

This study considered the relationship between fibre length characteristics and mechanical properties of Tectona grandis wood aged 15, 20 and 25-year. Six trees of even aged and similar class diameter were used for the study. Wood samples used for the study were systematically collected from three portions at 10, 50 and 90% of the tree height. The test samples were prepared along the radial positions from the pith to the bark. The relationship between fibre length and mechanical properties were examined using linear regression models and correlation coefficient. The results obtained from the correlation analysis carried out to examine the linear relationship between fibre length and mechanical properties of T. grandis wood were 0.924, 0.929 and 0.940 for MOR, MOE and CS parallel to grain. The relationship was highly significant (p < 0.05). Also the correlation coefficient (r) between fibre length and mechanical properties of T. grandis wood were highly significant (p < 0.001). Therefore, fibre length characteristics can be used as an index in predicting the mechanical properties of T. grandis wood.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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