The Demand for New Cars -- Econometric Model for Short-Term Forecasting

1975 ◽  
Vol 26 (4) ◽  
pp. 884
Author(s):  
N. Ghosh ◽  
A. C. Armstrong
Keyword(s):  
Econometric Model ◽  
Short Term ◽  
Short Term Forecasting

Forecasting Short-Term Freight Transportation Demand: Poisson STARMA Model

1998 ◽  
Vol 1645 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Rodrigo A. Garrido ◽  
Hani S. Mahmassani
Keyword(s):  
Econometric Model ◽  
Short Term ◽  
Transportation Demand ◽  
Modeling Methodology ◽  
Diagnostic Checking ◽  
Proposed Model ◽  
Stochastic Framework ◽  
Freight Transportation Demand ◽  
Short Term Forecasting ◽  
Spatial And Temporal Characteristics

A framework for analyzing, describing, and forecasting freight flows for operational and tactical purposes is presented. A dynamic econometric model is proposed. This model incorporates the spatial and temporal characteristics of freight demand within a stochastic framework. The model was applied in an actual context and its performance was compared with standard time series models (benchmark) for forecasting ability. The proposed model outperformed the benchmark from the econometric viewpoint. Extensive diagnostic checking and sensitivity analysis confirmed the robustness of the modeling methodology for short-term forecasting applications.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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