DETERMINING THE AVERAGE PRICE OF NEW CARS IN RUSSIA USING A DYNAMIC MODEL

2020 ◽  
Vol 1 (3) ◽  
pp. 32-38
Author(s):  
V. P. NEVEZHIN ◽  
Keyword(s):  
Time Series ◽  
Middle Class ◽  
Seasonal Changes ◽  
Statistical Data ◽  
Econometric Model ◽  
Average Price ◽  
Short Term ◽  
Passenger Car ◽  
Automotive Market ◽  
Car Market

The article deals with the issues of sales in the passenger car market and the factors that influence these sales. The main trends of the automotive market of new cars are studied. The reasons that contribute to the growth of prices for new cars are determined. On the basis of statistical data on car prices for the last four years, an econometric model of the time series is constructed, taking into account seasonal changes in sales. The obtained model and its application possibilities for obtaining a forecast of the average price of a middle-class passenger car in the short term are considered. Based on this model, a forecast of the average price for four periods is made, which is confirmed by the actual values of prices for middle-class cars in 2019.

scholarly journals Credit Cycles: Econometric Analysis and Evidence for Russia

2014 ◽  
pp. 113-122
Author(s):  
N. V. Artamonov ◽  
D. V. Artamonov ◽  
V. A. Artamonov
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Interest Rates ◽  
Russian Federation ◽  
Statistical Data ◽  
Econometric Analysis ◽  
Short Term ◽  
Credit Cycles ◽  
Real Gdp

One of the principal problem in contemporary macroeconomics is concerned with factors increasing or decreasing economic dynamics. The mainstream approach is based on neoclassical assumptions, but recently new approaches appear mostly based on new Keynesian concepts. In present time the influence of monetary market and credit instruments become more and more significant. Credit resources of banking and financial structures can affect and distort to reallocation of resources for national and even for global economic. In present paper an empiric and econometric analysis for some macroeconometric and monetary indices for Russian Federation is done. An econometrical models describing the influence of credit variables onto real GDP is estimated. It is shown that in short-term periods changes in credit variables do influence significantly onto GDP. It is shown that on short-term periods changes in money aggregate M2 brings influence (through credit variables) onto national output. As well it is shown that changes in short-term interest rate brings significant negative influence onto real output. Impulse response functions for GDP on shocks of credit variables, monetary base and short-term interest rate are evaluated. For the present study of credit cycles and their impact to real business cycles statistical data (quarterly time series) on the following factors for Russian Federation are collected: nominal and real GDP, monetary base M2, short-term interest rate, long-term interest rate (10-year treasuries bill rate), total debt outstanding. All time series are seasonally adjusted and collected for the period 2004 Q1 - 2013 Q2. All interest rates are adjusted for inflation (i.e. we deal with real interest rates). The investigation of long-term relationship for the factors under consideration are based on integration. It is important to note that in the present paper all econometric models are estimated on "pure" statistical data, while in many research papers on business and credit cycles all evaluations and inferences are based on "filtered" time series (mostly filtered by Hodrick-Prescott's method). In present paper "causality" always means "Granger causality". All estimations are made in gretl, an open-source multiplatform econometric software.

scholarly journals Empirical Econometric Model of an Enterprise

2016 ◽  
Vol 16 (1) ◽  
pp. 232-247
Author(s):  
Jerzy Witold Wiśniewski
Keyword(s):  
Time Series ◽  
Empirical Model ◽  
Statistical Data ◽  
Econometric Model ◽  
Good Decision ◽  
Service Business ◽  
Fixed Assets ◽  
Endogenous Variables ◽  
Zero Values ◽  
Output Equation

Abstract This work will present an empirical econometric model describing an enterprise within the category of medium-sized companies (according to European Union classification). The company, code-named ENERGY, carries out a manufacturing, commercial, and service business activity. The statistical data used was in the form of quarterly time series, containing 24 statistical observations from the years 2008–2013. A hypothetical model of the enterprise is a system of interdependent equations. The econometric model is composed of seven stochastic equations. The empirical model is missing the equation describing investments in the enterprise. It results from the fact, that during the years 2008–2013 the company suffered meagre investments. Investment output equation, therefore, does not provide any relevant systemic information for the management, since most statistical information in the time series assumes zero values. An empirical model of the company ENERGY is a system of interdependent equations, with statistically significant feedback between labour efficiency (EFEMP) and the average pay per 1 employee (APAY). Additionally, there is recurrence of the relationships between the fixed assets (FIXAS), employment volume (EMP), and the size of the net sales income (SNET). The empirical equations of the model are characterized by a description accuracy of individual endogenous variables. The model also has good decision-making and forecasting qualities.

scholarly journals Time-Series Analysis of Seasonal Changes of the O Inversion Polymorphism of Drosophila subobscura

Genetics ◽  
1996 ◽  
Vol 142 (1) ◽  
pp. 179-187 ◽  
Author(s):  
Francisco Rodríguez-Trelles ◽  
Gonzalo Alvarez ◽  
Carlos Zapata
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Time Series Analysis ◽  
Seasonal Changes ◽  
Climatic Factors ◽  
Extreme Temperature ◽  
Drosophila Subobscura ◽  
Short Term ◽  
Series Analysis

We have studied seasonal variation (spring, early summer, last summer and autumn) of inversion polymorphisms of the O chromosome of Drosophila subobscura in a natural population over 15 years. The length of the study allowed us to investigate the temporal behavior (short-term seasonal changes and long-term directional trends) of the O arrangements by the powerful statistical method of time series analysis. It is shown that the O inversion polymorphisms varied on two different time scales: short-term seasonal changes repeated over the years superimposed on long-term directional trends. All the common arrangements (O3+4+7,  OST,  O3+4 and O3+4+8) showed significant cyclic seasonal changes, and all but one of these arrangements (O3+4+7) showed significant long-term trends. Moreover, the degree of seasonality was different for different arrangements. Thus, O3+4+7 and OST showed the highest seasonality, which accounted for ∼61 and 47% of their total variances, respectively. The seasonal changes in the frequencies of chromosome arrangements were significantly associated with the seasonal variation of the climate (temperature, rainfall, humidity and insolation). In particular, O3+4+7 and OST, the arrangements with the greatest seasonal component, showed the strongest association with all climatic factors investigated, especially to the seasonal changes of extreme temperature and humidity.

scholarly journals Short-term household load forecasting based on Long- and Short-term Time-series network

2021 ◽  
Vol 7 ◽  
pp. 58-64
Author(s):  
Xifeng Guo ◽  
Ye Gao ◽  
Yupeng Li ◽  
Di Zheng ◽  
Dan Shan
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Short Term

Forecast of passenger car market structure and environmental impact analysis in China

2021 ◽  
Vol 772 ◽  
pp. 144950
Author(s):  
Dong Guo ◽  
Wei Yan ◽  
Xingbang Gao ◽  
Yujiao Hao ◽  
Yi Xu ◽  
...  
Keyword(s):  
Environmental Impact ◽  
Market Structure ◽  
Impact Analysis ◽  
Passenger Car ◽  
Environmental Impact Analysis ◽  
Car Market

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

Sign in / Sign up

Export Citation Format

Share Document