scholarly journals Modeling and Model Comparison for Industrial Production Index of Turkey, Brazil and G7 Countries

2018 ◽  
Vol 6 (04) ◽  
Author(s):  
Nihan Öksüz Narinç
Keyword(s):  
Industrial Production ◽  
Goodness Of Fit ◽  
Model Comparison ◽  
Information Criterion ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
G7 Countries ◽  
Production Index ◽  
Industrial Production Index ◽  
Expected Values

In this study, it was aimed to modeling and model comparison for the industrial production index values of Turkey, Brazil and G7 countries among the years 1990-2017. The curve estimation methods (linear, quadratic, qubic, and hyperbolastic) and some non-linear time series models (Weibull, Negative Exponential, Brody, Gompertz, Logistic, Von Bertalanffy, Richards) were used for modeling the longitudinal data of monthly industrial production index values. The most fitted Gompertz model for all three data sets was determined according to the criteria of goodness of fit (coefficient of determination, mean square error, Akaike's information criterion, Bayesian information criterion), using the process between 1990-2008 (up to the 2008 crisis). After the 2008-2009 crisis, Brazil and G7 countries' industrial production index values were well below their expected values. In contrast, Turkey's expected values and the actual values for the industrial production index have been fairly close. Considering these results, it can be said that Turkey was less affected in terms of the effects of the 2008-2009 economic crisis than other countries. Industrial production index values of Turkey at 100th anniversary of the founding of the Republic of Turkey in 2023, and other important dates in 2041 and 2050 were estimated to be 177.62, 353.49 and 485.63, respectively.

Industrial Output in the USSR and Russia, 1861—2012. Part II. Crises and Cycles

2013 ◽  
pp. 138-153 ◽  
Author(s):  
S. Smirnov
Keyword(s):  
Industrial Production ◽  
Turning Points ◽  
Russian Empire ◽  
Industrial Output ◽  
Production Index ◽  
Russian Industry ◽  
Industrial Production Index ◽  
Cyclical Volatility

Calculation of the aggregated "consensus" industrial production index has made it possible to date cyclical turning points and to measure the depth and length of the main industrial recessions in Russian Empire/USSR/Russia for the last century and a half. The most important causes of all these recessions are described. The cyclical volatility of Soviet/Russian industry is compared to that of American one.

ANALYSIS OF THE RELATIONSHIP BETWEEN ECONOMIC GROWTH, INFLATION AND UNEMPLOYMENT IN TURKEY WITH THE ARDL BOUNDS TESTING APPROACH

2021 ◽  
Vol 6 (15) ◽  
pp. 299-312
Author(s):  
Özlem KARADAĞ AK
Keyword(s):  
Economic Growth ◽  
Industrial Production ◽  
Consumer Price Index ◽  
Bounds Testing ◽  
Short Term ◽  
Production Index ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Industrial Production Index ◽  
Cointegration Relationship

The aim of this study is to examine the effects of economic growth and inflation on unemployment for the period 2005:1- 2020:9 in Turkey by using ARDL (Auto Regressive Distributed Lag) model. In the study, firstly unit root tests were carried out to determine whether economic growth (ind) and inflation (cpi) have long and short-term effects on unemployment (unemp). Then, the ARDL method was used to determine whether there is a long-term relationship between the series in the model where the unemployment rate is the dependent variable, the Industrial Production Index representing economic growth and the Consumer Price Index (CPI) representing inflation. Instead of GDP, the Industrial Production Index was preferred both to harmonize with the monthly data and to make a production-based analysis. As a result of the analysis, it was determined that there was a statistically significant cointegration relationship between the variables, and the short-term relationship was analyzed with the error correction model (ECM). As a result of the analysis, it has been determined that there is a cointegration relationship between unemployment, inflation rate and economic growth in Turkey. According to the results of the analysis, negative between unemployment and industrial production index; It is seen that there is a positive relationship between unemployment and inflation.

scholarly journals Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ramadan A. ZeinEldin ◽  
Muhammad Ahsan ul Haq ◽  
Sharqa Hashmi ◽  
Mahmoud Elsehety ◽  
M. Elgarhy
Keyword(s):  
Goodness Of Fit ◽  
Continuous Distribution ◽  
Information Criterion ◽  
Value Theory ◽  
Estimation Methods ◽  
Parameter Distribution ◽  
Monte Carlo Simulation Study ◽  
Lifetime Distributions ◽  
Lomax Distribution ◽  
Inverse Lomax Distribution

In this article, we propose and study a new three-parameter distribution, called the odd Fréchet inverse Lomax (OFIL) distribution, derived by combining the odd Fréchet-G family and the inverse Lomax distribution. Since Fréchet is a continuous distribution with wide applicability in extreme value theory, the new model contains these properties as well as the characteristics of the inverse Lomax distribution which make it more flexible and provide a good alternative for some well-known lifetime distributions. We initially present a linear representation of its functions and discussion on density and hazard rate function. Then, we study its various mathematical properties. Different estimation methods are used to estimate parameters of OFIL. The Monte Carlo simulation study is carried out to compare the efficiencies of different methods of estimation. The maximum likelihood estimation (MLE) method is used to estimate the OFIL parameters by considering three practical data applications. We show that the related model is the best in comparisons based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and other goodness-of-fit measures.

Introducing a sinusoidal equation to describe lactation curves for cumulative milk yield and composition in Holstein cows

2020 ◽  
Vol 87 (2) ◽  
pp. 220-225
Author(s):  
Navid Ghavi Hossein-Zadeh ◽  
Hassan Darmani Kuhi ◽  
James France ◽  
Secundino López
Keyword(s):  
Linear Model ◽  
Milk Yield ◽  
Milk Fat ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Holstein Cows ◽  
Coefficient Of Determination ◽  
Production Traits ◽  
Lactation Curve

AbstractThe aim of the work reported here was to investigate the appropriateness of a sinusoidal function by applying it to model the cumulative lactation curves for milk yield and composition in primiparous Holstein cows, and to compare it with three conventional growth models (linear, Richards and Morgan). Data used in this study were 911 144 test-day records for milk, fat and protein yields, which were recorded on 834 dairy herds from 2000 to 2011 by the Animal Breeding Centre and Promotion of Animal Products of Iran. Each function was fitted to the test-day production records using appropriate procedures in SAS (PROC REG for the linear model and PROC NLIN for the Richards, Morgan and sinusoidal equations) and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination $\lpar {R_{{\rm adj}}^2 } \rpar $, root mean square error (RMSE), Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). $R_{{\rm adj}}^2 $ values were generally high (>0.999), implying suitable fits to the data, and showed little differences among the models for cumulative yields. The sinusoidal equation provided the lowest values of RMSE, AIC and BIC, and therefore the best fit to the lactation curve for cumulative milk, fat and protein yields. The linear model gave the poorest fit to the cumulative lactation curve for all production traits. The current results show that classical growth functions can be fitted accurately to cumulative lactation curves for production traits, but the new sinusoidal equation introduced herein, by providing best goodness of fit, can be considered a useful alternative to conventional models in dairy research.

scholarly journals Method to generate growth and degrowth models obtained from differential equations applied to agrarian sciences

2018 ◽  
Vol 39 (6) ◽  
pp. 2659 ◽  
Author(s):  
André Luiz Pinto dos Santos ◽  
Guilherme Rocha Moreira ◽  
Cicero Carlos Ramos de Brito ◽  
Frank Gomes-Silva ◽  
Maria Lindomárcia Leonardo da Costa ◽  
...  
Keyword(s):  
Differential Equations ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Growth Curves ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Absolute Deviation ◽  
Marquardt Algorithm ◽  
Maturation Rate ◽  
Biological Interpretation

This study aims to propose a method to generate growth and degrowth models using differential equations as well as to present a model based on the method proposed, compare it with the classic linear mathematical models Logistic, Von Bertalanffy, Brody, Gompertz, and Richards, and identify the one that best represents the mean growth curve. To that end, data on Undefined Breed (UB) goats and Santa Inês sheep from the works of Cavalcante et al. (2013) and Sarmento et al. (2006a), respectively, were used. Goodness-of-fit was measured using residual mean squares (RMS), Akaike information criterion (AIC), Bayesian information criterion (BIC), mean absolute deviation (MAD), and adjusted coefficient of determination . The models’ parameters (?, weight at adulthood; ?, an integration constant; ?, shape parameter with no biological interpretation; k, maturation rate; and m, inflection point) were estimated by the least squares method using Levenberg-Marquardt algorithm on the software IBM SPSS Statistics 1.0. It was observed that the proposed model was superior to the others to study the growth curves of goats and sheep according to the methodology and conditions under which the present study was carried out.

scholarly journals Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Loshini Thiruchelvam ◽  
Sarat C. Dass ◽  
Rafdzah Zaki ◽  
Abqariyah Yahya ◽  
Vijanth S. Asirvadam
Keyword(s):  
Air Quality ◽  
Goodness Of Fit ◽  
Model Comparison ◽  
Prediction Models ◽  
Moving Average ◽  
Pollution Index ◽  
Information Criterion ◽  
Air Pollutant ◽  
Dengue Case ◽  
Arima Models

This study investigated the potential relationship between dengue cases and air quality – as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were –800.66, –796.22, and –790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.

Sign in / Sign up

Export Citation Format

Share Document