scholarly journals ECONOMETRIC ANALYSIS OF THE VOLUME OF MUTUAL TRADE OF THE EAEU MEMBER STATES

2021 ◽  
Author(s):  
Mariya Tsvil ◽  
Ella Guleva ◽  
Margarita Zubkova
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Multiplicative Model ◽  
Econometric Analysis ◽  
Time Series Models ◽  
Member States ◽  
Quarterly Data ◽  
Mutual Trade

The article provides econometric time series models for the volumes of mutual trade of the EAEU member states based on quarterly data from the 1st quarter of 2017 to the 3rd quarter of 2021. An exponential smoothing model and a multiplicative model are built. Also, a forecast was made for the volume of mutual trade in the IV quarter of 2021

scholarly journals ECONOMETRIC ANALYSIS OF VOLUMES OF IMPORT OF THE REPUBLIC OF BELARUS

2018 ◽  
Author(s):  
Мария Цвиль ◽  
Mariya Tsvil ◽  
Алла Поливанова ◽  
Alla Polivanova ◽  
Ксения Полянина ◽  
...  
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Multiplicative Model ◽  
Econometric Models ◽  
Econometric Analysis ◽  
Quarterly Data ◽  
The Republic

The article obtained econometric models of time series in terms of imports of the Republic of Belarus according to quarterly data in the period from 2013 to 2018. Exponential smoothing model and multiplicative model are constructed. Also, a forecast was made of import volumes in terms of value for the 4th quarter of 2018.

scholarly journals ECONOMETRIC ANALYSIS OF EXPORT VOLUMES OF OILSEEDS AND GRAIN CROPS OF THE RUSSIAN FEDERATION

2021 ◽  
Author(s):  
Mariya Tsvil ◽  
Maria Kobeleva ◽  
Anastasia Ponomareva
Keyword(s):  
Time Series ◽  
Russian Federation ◽  
Multiplicative Model ◽  
Econometric Models ◽  
Econometric Analysis ◽  
Quarterly Data ◽  
Grain Crops ◽  
The Russian Federation

The article presents econometric models of time series presented on the basis of quarterly data on the export volumes of oilseeds and cereals of the Russian Federation in the period from 01.01.2017 to 31.09. 2021. According to the obtained multiplicative model, the forecast of export volumes for 12 commodity groups for the 4th quarter of 2021 is presented

scholarly journals Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking

2015 ◽  
Vol 31 (4) ◽  
pp. 627-647 ◽  
Author(s):  
Ángel Cuevas ◽  
Enrique M. Quilis ◽  
Antoni Espasa
Keyword(s):  
Time Series ◽  
Business Cycles ◽  
Real Time ◽  
Time Series Models ◽  
National Accounts ◽  
Spanish Economy ◽  
Short Term ◽  
Quarterly Data ◽  
New Instrument ◽  
Term Monitoring

Abstract In this article we propose a methodology for estimating the GDP of a country’s different regions, providing quarterly profiles for the annual official observed data. Thus the article offers a new instrument for short-term monitoring that allows the analysts to quantify the degree of synchronicity among regional business cycles. Technically, we combine time-series models with benchmarking methods to process short-term quarterly indicators and to estimate quarterly regional GDPs ensuring their temporal and transversal consistency with the National Accounts data. The methodology addresses the issue of nonadditivity, explicitly taking into account the transversal constraints imposed by the chain-linked volume indexes used by the National Accounts, and provides an efficient combination of structural as well as short-term information. The methodology is illustrated by an application to the Spanish economy, providing real-time quarterly GDP estimates, that is, with a minimum compilation delay with respect to the national quarterly GDP. The estimated quarterly data are used to assess the existence of cycles shared among the Spanish regions.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
pp. 6-20
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin ◽  
Abu Kholdun Al Mahmood
Keyword(s):  
Time Series ◽  
Medical Science ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

scholarly journals Demand For Electricity In Lebanon

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Salah Abosedra ◽  
Abdallah Dah ◽  
Sajal Ghosh
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Span ◽  
Time Series Models ◽  
Detailed Data ◽  
Economic Variables ◽  
Univariate Time Series ◽  
Modeling Techniques ◽  
Socio Economic Variables ◽  
Future Demand

This paper estimates the demand for electricity in Lebanon by employing three modeling techniques namely OLS, ARIMA and exponential smoothing for the time span January 1995 to December 2005. In- sample forecasts reveal that the forecasts made by ARIMA (0,1,3) (1,0,0)12 is superior in terms of lowest RMSE, MSE and MAPE criteria, followed by exponential smoothing and OLS. Therefore, the planners in Lebanon could utilize linear univariate time-series models for forecasting future demand of electricity until detailed data on various socio-economic variables are available, which, in the future, may result in other modeling techniques being superior to estimate the demand for electricity in the country.

scholarly journals Modeling and Forecasting of COVID-19 New Cases in the Top 10 Infected African Countries Using Regression and Time Series Models

2020 ◽  
Author(s):  
Alemayehu Argawu
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Models ◽  
Study Finding ◽  
Exponential Smoothing ◽  
Time Series Models ◽  
African Countries ◽  
Mass Gatherings ◽  
Preparedness Planning ◽  
Best Fit

Background: COVID-19 total cases have reached 1,083,071 (83.5%) in the top 10 infected African countries (South Africa, Egypt, Morocco, Ethiopia, Nigeria, Algeria, Ghana, Kenya, Cameroon, and Cote-dIvoire) from Feb 14 to Sep 6, 2020. Then, this study aimed to model and forecast of COVID-19 new cases in these top 10 infected African countries. Methods: In this study, the COVID 19 new cases data have been modeled and forecasted using curve estimation regression and time series models for these top 10 infected African countries from Feb 14 to Sep 6, 2020. Results: From July to August, the prevalence of COVID-19 cumulative cases was declined in South Africa, Cote dʹIvoire, Egypt, Ghana, Cameron, Nigeria, and Algeria by 31%, 26%, 22%, 20%, 14%, 12%, and 4%, respectively. But, it was highly raised in Ethiopia and Morocco by 41%, and 38% in this period, respectively. In Kenya, it was raised only by 1%. In this study, the cubic regression models for the ln(COVID-19 new cases) data were relatively the best fit for Egypt, Ethiopia, Kenya, Morocco, Nigeria and South Africa. And, the quadratic regression models for the data were the best fit for Cameroon, Cote-dIvoire and Ghana. The Algeria data was followed the logarithmic regression model. In the time series analysis, the Algeria, Egypt, and South Africa COVID-19 new cases data have fitted the ARIMA (0,1,0), ARIMA (0,1,0), and ARIMA (0,1,14) models, respectively. The Cameroon, Cote-dIvoire, Ghana, and Nigeria data have fitted the simple exponential smoothing models. The Ethiopia, Kenya, and Morocco data have followed the Damped trend, Holt, and Brown exponential smoothing models, respectively. In the analysis, the trends of COVID-19 new cases will be declined for Algeria and Ethiopia, and the trends will be constantan for Cameroon, Cote-dIvoire, Ghana and Nigeria. But, it will be raised slightly for Egypt and Kenya, and significantly for Morocco and South Africa from September 7 to October 6, 2020. Conclusion: This study was conducted with the current measures; the forecasts and trends obtained may differ from the number of cases that occur in the future. Thus, the study finding should be useful in preparedness planning against further spread of the COVID-19 epidemic in African countries. And, the researcher recommended that as many countries continue to relax restrictions on movement and mass gatherings, and more are opening their airspaces, and the countries other public and private sectors are reopening. So, strong appropriate public health and social measures must be instituted on the grounds again.

scholarly journals Forecasting Migration Flows for New Zealand: Arrivals of New and Returning New Zealanders & Departing by Destinations

1970 ◽  
Author(s):  
Xintao Zhao ◽  
Ram SriRamaratnam ◽  
Dirk Van Seventer
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Linear Time ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Time Series Regression ◽  
Quarterly Data ◽  
Migration Flows ◽  
Exponential Smoothing Method ◽  
New Zealanders

The purpose of this paper was to outline the methods and to report results of an econometric attempt to forecast New Zealand migration flows. Flows were decomposed into eight components: two relating to arrivals and six components relating to departures by several destinations. Linear time series regression and the Holt­Winters exponential smoothing method were applied to quarterly data from June 1978 to June 2008 or from March 1990 to June 2008. Within­sample mean absolute percentage errors were presented and full­sample estimates from June 1978 to September 2010 or from March 1990 to September 2010 were used to forecast migration flows for each component for the next two years.

scholarly journals Forecasting Wheat Production Using Time Series Models in Pakistan

2019 ◽  
Vol 8 (2) ◽  
pp. 172-177
Author(s):  
M Asif Masood ◽  
Irum Raza ◽  
Saleem Abid
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Linear Quadratic ◽  
Wheat Production ◽  
S Curve ◽  
Double Exponential Smoothing

The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

Does Inflation Vary between Cities?

1984 ◽  
Vol 16 (4) ◽  
pp. 513-527 ◽  
Author(s):  
G L Clark
Keyword(s):  
Time Series ◽  
Urban Areas ◽  
Time Series Models ◽  
Theoretical Argument ◽  
Price Equilibrium ◽  
Price Inflation ◽  
Quarterly Data ◽  
Spatial Price ◽  
Stochastic Time Series ◽  
Stochastic Time

Apart from occasional anecdotal observation, there has been little systematic study of the patterns of local inflation. The strongest theoretical argument is that local price inflation should be no different from national inflation. Alternatively, it has been suggested that spatial price equilibrium is implausible and that spatial price interdependence is more likely. Quarterly data for some sixteen large US urban areas over the period 1950–1980, are analyzed via stochastic time-series models. It is concluded in this paper that, although inflation does vary, if only slightly, between cities and with respect to the nation, in some instances national inflation may fairly represent local inflation.

Sign in / Sign up

Export Citation Format

Share Document