scholarly journals Verification of the Returns to Scale of Production Type for the Russian Federation Regions

2019 ◽  
Vol 224 ◽  
pp. 06011
Author(s):  
Igor Kirilyuk ◽  
Oleg Senko
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Regression Model ◽  
Probability Distributions ◽  
Multivariate Time Series ◽  
Returns To Scale ◽  
Production Functions ◽  
Time Series Regression ◽  
Production Type ◽  
Statistical Validity

Monte-Carlo methods to asses a statistical validity of the relationship between coefficients of time series regression model were proposed. In economics such a relationship is present in the case when constant return to scale in production functions is assumed. The techniques being discussed here are virtually free from assumptions about underlying probability distributions and may be used in the case, when target variable or regressors are time series with random walk. This is achieved by comparing the regression model built on truly multivariate time series with those built on simulated time series with random walk. It has been shown that for the production functions of most Russian regions, the returns to scale significantly differs from a constant value at p<0.05.

scholarly journals Real-Time Prediction of the COVID-19 Epidemic in Thailand using Simple Model-Free Method and Time Series Regression Model

2021 ◽  
Vol 18 (14) ◽  
Author(s):  
Rati WONGSATHAN
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Regression Model ◽  
Regression Models ◽  
Gaussian Function ◽  
Logistic Function ◽  
Hyperbolic Tangent ◽  
Time Series Regression ◽  
Model Free ◽  
Tangent Function

The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision-making. This work is to model the ongoing COVID-19 spread in Thailand during the 1st and 2nd phases of the pandemic using the simple but powerful method based on the model-free and time series regression models. By employing the curve fitting, the model-free method using the logistic function, hyperbolic tangent function, and Gaussian function was applied to predict the number of newly infected patients and accumulate the total number of cases, including peak and viral cessation (ending) date. Alternatively, with a significant time-lag of historical data input, the regression model predicts those parameters from 1-day-ahead to 1-month-ahead. To obtain optimal prediction models, the parameters of the model-free method are fine-tuned through the genetic algorithm, whereas the generalized least squares update the parameters of the regression model. Assuming the future trend continues to follow the past pattern, the expected total number of patients is approximately 2,689 - 3,000 cases. The estimated viral cessation dates are May 2, 2020 (using Gaussian function), May 4, 2020 (using a hyperbolic function), and June 5, 2020 (using a logistic function), whereas the peak time occurred on April 5, 2020. Moreover, the model-free method performs well for long-term prediction, whereas the regression model is suitable for short-term prediction. Furthermore, the performances of the regression models yield a highly accurate forecast with lower RMSE and higher R2 up to 1-week-ahead. HIGHLIGHTS COVID-19 model for Thailand during the first and second phases of the epidemic The model-free method using the logistic function, hyperbolic tangent function, and Gaussian function  applied to predict the basic measures of the outbreak Regression model predicts those measures from one-day-ahead to one-month-ahead The parameters of the model-free method are fine-tuned through the genetic algorithm  GRAPHICAL ABSTRACT

DETECTION AND MODELING OF REGRESSION PARAMETER VARIATION ACROSS FREQUENCIES

1999 ◽  
Vol 3 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Hui Boon Tan ◽  
Richard Ashley
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Time Domain ◽  
Simple Technique ◽  
Parameter Variation ◽  
Missing Observations ◽  
Regression Parameter ◽  
Time Series Regression ◽  
Parameter Instability ◽  
The Time Domain

A simple technique for directly testing the parameters of a time-series regression model for instability across frequencies is presented. The method can be implemented easily in the time domain, so that parameter instability across frequency bands can be conveniently detected and modeled in conjunction with other econometric features of the problem at hand, such as simultaneity, cointegration, missing observations, and cross-equation restrictions. The usefulness of the new technique is illustrated with an application to a cointegrated consumption-income regression model, yielding a straightforward test of the permanent income hypothesis.

scholarly journals Assessment of varicella vaccine effectiveness in Germany: a time-series approach

2010 ◽  
Vol 139 (11) ◽  
pp. 1710-1719 ◽  
Author(s):  
M. HÖHLE ◽  
A. SIEDLER ◽  
H.-M. BADER ◽  
M. LUDWIG ◽  
U. HEININGER ◽  
...  
Keyword(s):  
Time Series ◽  
Vaccination Programme ◽  
Time Course ◽  
Vaccine Coverage ◽  
Multivariate Time Series ◽  
Varicella Vaccine ◽  
Vaccine Effectiveness ◽  
Childhood Vaccination ◽  
Time Series Regression ◽  
Sentinel Network

SUMMARYA multivariate time-series regression model was developed in order to describe the 2005–2008 age-specific time-course of varicella sentinel surveillance data following the introduction of a varicella childhood vaccination programme in Germany. This ecological approach allows the assessment of vaccine effectiveness under field conditions by relating vaccine coverage in cohorts of 24-month-old children to the mean number of cases per reporting unit in the sentinel network. For the 1–2 years age group, which is directly affected by the vaccination programme, a one-dose vaccine effectiveness of 83·2% (95% CI 80·2–85·7) was estimated which corresponds to previous approaches assessing varicella vaccine effectiveness in the field in the USA.

scholarly journals Internal and External Factors of Future Returns in the Banking Business: Time Series Analysis of Interrelationship

2017 ◽  
Vol 9 (1(J)) ◽  
pp. 82-89
Author(s):  
Kwabena A Kyei ◽  
Albert Antwi
Keyword(s):  
Time Series ◽  
Linear Relationship ◽  
Multivariate Time Series ◽  
Secondary Data ◽  
External Factors ◽  
Least Square ◽  
Time Series Regression ◽  
Banking Business ◽  
Log Linear ◽  
Internal And External Factors

The paper seeks to find the interrelationship between internal and external factors of future returns in the banking business. A multivariate time series regression models are fitted for the dependent variable: return on equity (ROE) against the lag one independent variables, namely: deposit, size, loan, capital, inflation, gross domestic product (GDP) and stock market capitalization (SMC), for ABSA bank; using secondary data, which span from 1998 to 2014 fiscal years. Logarithm transformation of the absolute value of the de-trended data and first differencing at lag one were the smoothing techniques applied to the data. Multivariate time series regression by the least square approach with special consideration of the stepwise method was used in fitting the models to the data. Results indicated that first, there is a positive linear relationship between ROE and loans, a negative linear relationship between ROE and inflation from the differencing techniques; and equally a negative log-linear relationship between ROE and capital as well as a positive log-linear relationship between ROE and ROA for the logarithm de-trend technique.

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