scholarly journals Inflation and Economic Growth in Iran: Evidence from ARDL & Rolling Linear Models

2011 ◽  
Vol 3 (6) ◽  
pp. 383-388
Author(s):  
Yazdan Naghdi ◽  
Mohadese Soltantooye .
Keyword(s):  
Economic Growth ◽  
Linear Regression ◽  
Regression Models ◽  
Linear Models ◽  
Growth Models ◽  
Influential Factors ◽  
Linear Regression Models ◽  
Adjusted Model ◽  
The Relationship

Economists pay considerable attention to the influential factors on the economic growth in the framework of the growth models. In the same direction, the relationship between inflation and economic growth in Iran has been investigated during 1978-2008. First, an adjusted model has been designed based on (Barro) model and then the relationship between inflation and economic growth has been estimated using both ARDL and rolling linear regression models. The results derived from the both estimated models showed that the effect of inflation on economic growth is negative and Significance.

scholarly journals Economic Growth and Public Indebtedness in the Last Four Decades: Is Portugal different from the other PIIGS’ economies?

2015 ◽  
Vol 61 (6) ◽  
pp. 3-11 ◽  
Author(s):  
Ricardo Ferraz ◽  
António Portugal Duarte
Keyword(s):  
Economic Growth ◽  
Linear Regression ◽  
Public Debt ◽  
Regression Models ◽  
Time Horizon ◽  
Negative Relationship ◽  
The Other ◽  
Extended Time ◽  
Linear Regression Models ◽  
The Relationship

Abstract Portugal is a member of the group known by investors as ‘PIIGS’, countries characterised by having high public debt and weak economic growth. Using an extended time horizon, 1974–2014, this study seeks to empirically explore the relationship between economic growth and public debt in the PIIGS economies, particularly in the case of Portugal. Based on the estimation of linear regression models, it was concluded that in the last four decades there has been a negative relationship between economic growth and public debt in both cases, which is consistent with the literature. The negative relationship was even more pronounced in the case of the PIIGS than it was in the case of Portugal.

scholarly journals Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regression Models ◽  
Linear Regression Models ◽  
Winter Snow ◽  
The Relationship

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

BAYESIAN IDENTIFICATION OF OUTLIERS AND CHANGE-POINTS IN MEASUREMENT ERROR MODELS

2005 ◽  
Vol 08 (04) ◽  
pp. 433-449 ◽  
Author(s):  
FERNANDO A. QUINTANA ◽  
PILAR L. IGLESIAS ◽  
HELENO BOLFARINE
Keyword(s):  
Linear Regression ◽  
Outlier Detection ◽  
Change Point ◽  
Regression Models ◽  
Point Problem ◽  
Linear Regression Models ◽  
Outlier Identification ◽  
Change Point Problem ◽  
Error In Variables ◽  
The Relationship

The problem of outlier and change-point identification has received considerable attention in traditional linear regression models from both, classical and Bayesian standpoints. In contrast, for the case of regression models with measurement errors, also known as error-in-variables models, the corresponding literature is scarce and largely focused on classical solutions for the normal case. The main object of this paper is to propose clustering algorithms for outlier detection and change-point identification in scale mixture of error-in-variables models. We propose an approach based on product partition models (PPMs) which allows one to study clustering for the models under consideration. This includes the change-point problem and outlier detection as special cases. The outlier identification problem is approached by adapting the algorithms developed by Quintana and Iglesias [32] for simple linear regression models. A special algorithm is developed for the change-point problem which can be applied in a more general setup. The methods are illustrated with two applications: (i) outlier identification in a problem involving the relationship between two methods for measuring serum kanamycin in blood samples from babies, and (ii) change-point identification in the relationship between the monthly dollar volume of sales on the Boston Stock Exchange and the combined monthly dollar volumes for the New York and American Stock Exchanges.

scholarly journals Factors associated with parasite dominance in fishes from Brazil

2016 ◽  
Vol 25 (2) ◽  
pp. 225-230
Author(s):  
Cristina Fernandes do Amarante ◽  
Wagner de Souza Tassinari ◽  
Jose Luis Luque ◽  
Maria Julia Salim Pereira
Keyword(s):  
Linear Regression ◽  
Body Length ◽  
Regression Models ◽  
Mixed Model ◽  
Multiple Linear Regression Model ◽  
Mixed Effects ◽  
The Body ◽  
Linear Regression Models ◽  
Biological Aspects ◽  
The Relationship

Abstract The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.

scholarly journals Reduced left atrial strain in magnetic resonance imaging is associated with abnormal P-waves in patients after acute myocardial infarction

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
M Wester ◽  
J Pec ◽  
C Fisser ◽  
K Debl ◽  
O Hamer ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Linear Regression ◽  
Left Atrial ◽  
Regression Models ◽  
Linear Regression Analysis ◽  
P Wave ◽  
P Value ◽  
Linear Regression Models ◽  
The Relationship

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): ReForM-B-Program Background Abnormal P-wave terminal force in lead V1 (PTFV1) is associated with atrial remodeling. The relationship between PTFV1 and atrial function after acute myocardial injury is insufficiently understood and may be elucidated by detailed feature tracking (FT) strain analysis of cardiac magnetic resonance images (CMR). Purpose We investigated the relationship between PTFV1 and left atrial (LA) strain (measured by CMR) in a patient cohort presenting with acute myocardial infarction (MI). Methods 56 patients with acute MI underwent CMR within 3-5 days after MI. PTFV1 was measured as the product of negative P-wave amplitude and duration in lead V1 (Fig. A). A PTFV1 >4000 ms*µV was defined as abnormal. CMR cine data were retrospectively analyzed using a dedicated FT software. LA strain (ε) and strain rate (SR) for atrial reservoir ([εs]; [SRs]), conduit ([εe]; [SRe]) and booster function ([εa]; [SRa]) were measured in two long-axis views (Fig. A). Results Patients with abnormal PTFV1 had significantly reduced LA conduit function εe and SRe (Fig. B + D). There was a significant negative correlation between the extent of PTFV1 and both εe and SRe (Fig. C + E). In univariate and multivariate regression models, both PTFV1 and age predicted atrial conduit function. In contrast, multiple clinical co-factors had no significant influence on εe (Table). Interestingly, linear regression models revealed only mild dependency of PTFV1 on conventional parameters of cardiac function such as left ventricular ejection fraction (p = 0.059; R²(adj.)=0.047), and no dependency on structural parameters such as LA area (p = 0.639; R²(adj.)=0.016), or LA fractional area change (p = 0.825; R²(adj.)=0.020). Conclusion Abnormal PTFV1 was associated with reduced LA function independent from numerous clinical co-factors in patients presenting with acute myocardial infarction. Table N = 56 Linear Regression Analysis Multiple Linear Regression Analysis (R2 (adj.)=0.376, p = 0.016) Variable B 95% CI P value R2 (adj.) B 95% CI P value PTFV1 [µV*ms] -1.628 17085.298 to 27210.854 0.013 0.092 -1.315 -2.614 to -0.016 0.047 Age [y] -425.775 24985.168 to 54634.995 0.002 0.145 -610.815 -982.78 to -238.849 0.001 Body mass indes [kg/m2] -185.653 -3259.187 to 47020.775 0.671 -0.015 -506.096 -1327.357 to 315.165 0.219 Creatinine kinase [U/l] -1.571 14806.991 to 24842.272 0.121 0.027 -1.791 -3.72 to 0.138 0.067 Male sex -893.28 10701.206 to 23504.066 0.802 -0.017 4275.631 -3842.517 to 12393.78 0.292 Estimated glomerular filtration rate [ml/min/1.73m2] 88.617 -4564.177 to 21395.361 0.202 0.012 -163.981 -331.343 to 3.381 0.054 Systolic blood pressure [mmHg] -2.001 14045.786 to 22037.253 0.095 0.038 29.331 -108.243 to 166.906 0.668 nt-pro brain natriuretic peptide [pg/ml] 24.629 -4060.804 to 30920.828 0.716 -0.016 1.015 -1.778 to 3.809 0.466 Univariate and multivariate linear regression models for left atrial conduit strain Abstract Figure

scholarly journals Regression models for estimating chick hatchling weight from some egg geometry traits

2018 ◽  
Vol 34 (3) ◽  
pp. 323-334
Author(s):  
Nadya Mincheva ◽  
Mitko Lalev ◽  
Magdalena Oblakova ◽  
Pavlina Hristakieva
Keyword(s):  
Linear Regression ◽  
Surface Area ◽  
Regression Models ◽  
Linear Models ◽  
Geometric Mean ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Linear Regression Models ◽  
Egg Weight ◽  
Mean Diameter

The prediction of chicks? weight before hatching is an important element of selection, aimed at improving the uniformity rate and productivity of birds. With this regards, our goal was to develop and evaluate optimum models for similar prediction in two White Plymouth Rock chickens lines - line L and line K on the basis of the incubation egg weight and egg geometry characteristics - egg maximum breadth (B), egg length (L), geometric mean diameter (Dg), egg volume (V), egg surface area (S). A total of 280 eggs (140 from each line) laid by 40-weekold hens were randomly selected. Mean arithmetic values, standard deviations and coefficients of variation of studied parameters were determined for each line. Correlation coefficients between the weight of hatchlings and predictors were the highest for egg weight, geometric mean diameter, volume and surface area of eggs (r=0.731-0.779 for line L; r=0.802-0.819 for line ?). Nine linear regression models were developed and their accuracy evaluated. The regression equations of hatchlings? weight vs egg length had the lowest coefficient of determination (0.175 for line K and 0.291 for line L), but when egg length and breadth entered the model together, its value increased significantly up to 0.541 and 0.665 for lines L and K, respectively. The weight of day-old chicks from line L could be predicted with higher accuracy with a model involving egg surface area apart egg weight (ChW=0.513EW+0.282S - 10.345; R2=0.620). In line ? a more accurate prognosis was attained by adding egg breadth as an additional predictor to the weight in the model (ChW=0.587EW+0.566? - 19.853; R2=0.692). The study demonstrated that multiple linear regression models were more precise that single linear models.

scholarly journals Pobreza e impunidad, factores relevantes en las variaciones del catolicismo en América Latina / Poverty and Impunity, Relevant Factors in Variations in Catholicism in Latin America

2021 ◽  
Author(s):  
Luis Antonio Andrade Rosas ◽  
Felipe Gaytán Alcalá ◽  
Carlos Alberto Jiménez-Bandala
Keyword(s):  
Economic Growth ◽  
Latin America ◽  
Linear Regression ◽  
20Th Century ◽  
Regression Models ◽  
Social Changes ◽  
Linear Regression Models ◽  
Negative Effect ◽  
America Latina ◽  
Relevant Factors

In the last 25 years the Catholic population in Latin America has decreased considerably, some studies attribute the increasing secularization to the economic and social changes that marked the end of the 20th century. In this sense, this work aims to analyze the incidence of socioeconomic factors in the reduction of Catholic membership. The methodology is based on econometric linear regression models. The main results show that the catholicity index and economic growth are not related; but the growth of poverty did have a negative effect, particularly when analyzed by region. Finally, by combining violence and corruption, impunity emerges as a significant factor in the variation of Catholicism.

scholarly journals A study on factors related to readership of scientific articles

2017 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Tony Xu ◽  
Shayan Khalili ◽  
Cynthia Deng
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Scientific Research ◽  
The Other ◽  
Linear Regression Models ◽  
Research Papers ◽  
Scatter Plots ◽  
The Subject ◽  
Moderate Effect ◽  
The Relationship

This paper analyzes the relationship between the number of Twitter and Mendeley readers with the article’s subject, publisher, journal, and title length. It also looks at which country has the greatest number of readers to see if researchers can garner more visibility by publishing an article relevant to issues in those countries. The purpose of this report is to help researchers improve the visibility and impact value of their research. The data was gathered from 550,000 scientific research papers published between January 1st and July 1st of 2016. Python’s built-in JSON library was used to extract the number of Twitter and Mendeley readers, as well as the article count for each factor. The correlation between readers per article and each factor was then visualized using bubble graphs, linear regression models, and scatter plots. This paper concludes that the length of the title is the strongest factor affecting readership. In particular, titles with lengths between 51 and 90 characters have the greatest number of readers. Moreover, articles relevant to issues in countries with a higher GDP have the highest overall readership. On the other hand, the publisher and the journal did not have a significant effect on readership, while the subject of the article had a moderate effect on readership.

scholarly journals Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature

2021 ◽  
Vol 1 (2) ◽  
pp. 68-75
Author(s):  
Muhammad Bayu Nirwana ◽  
Dewi Wulandari
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Change Point ◽  
Regression Models ◽  
Linear Regression Models ◽  
Likelihood Estimator ◽  
Sea Temperature ◽  
Independent Variables ◽  
Modeling Data ◽  
The Relationship

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021

scholarly journals Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions

2018 ◽  
Author(s):  
Guojun Gan
Keyword(s):  
Linear Regression ◽  
Life Insurance ◽  
Predictive Models ◽  
Regression Models ◽  
Linear Models ◽  
Insurance Companies ◽  
Linear Regression Models ◽  
Insurance Product ◽  
Valuation Process ◽  
Speed Up

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.

Sign in / Sign up

Export Citation Format

Share Document