scholarly journals R-Software for the Assessment of Structural Changes in Fisheries Production of Andhra Pradesh and Tamil Nadu for Indian GDP

2021 ◽  
Vol 12 (6) ◽  
pp. 713-718
Author(s):  
M. Rajani ◽  
◽  
A. Balasubramanian ◽  
Keyword(s):  
Structural Change ◽  
Regression Models ◽  
Tamil Nadu ◽  
Andhra Pradesh ◽  
Structural Changes ◽  
Positive Trend ◽  
Linear Regression Models ◽  
R Software ◽  
Significant Structural Change ◽  
Chow Test

A Chow test was applied to analyze the structural changes in GDP of India with respect to Fisheries GDP obtained from the states of Andhra Pradesh and Tamil Nadu using Unrestricted and Restricted Linear Regression models in matrix notation for the period of 2000–01 to 2013–14. The GDP and Fisheries GDP data pertaining to the state of Andhra Pradesh and Tamil Nadu for the periods 2000–01 to 2006–07 and 2007–08 to 2013–14 were also collected for analyzing the structural changes between earmarked two periods as well as states from 2000–01 to 2013–14. In this study, the Chow test revealed that there was no structural change between the total GDP of India and Fisheries GDP with respect to the states Andhra Pradesh and Tamil Nadu during the periods 2001–2007 and 2008 –2014. However, significant structural changes could be observed between the GDP of India and Fisheries GDP obtained from the states of Andhra Pradesh and Tamil Nadu during the period 2000–2001 to 2013–14.However, there was a positive trend of structural change observed in the states Andhra Pradesh and Tamil Nadu with respect to the GDP of India and Fisheries GDP during the period 2000–2001 to 2012–14. Owing to theses, it is concluded that the contribution of fisheries with respect to the country’s GDP between the periods made a significant structural change however no many structural changes were observed in two time periods within the states.

scholarly journals Variables binaires et tests prédictifs contre les changements structurels

2009 ◽  
Vol 57 (3) ◽  
pp. 376-386 ◽  
Author(s):  
Jean-Marie Dufour
Keyword(s):  
Structural Change ◽  
Regression Models ◽  
Structural Changes ◽  
Rate Of Change ◽  
Linear Regression Models ◽  
Dummy Variables ◽  
Additional Information ◽  
Dummy Variable ◽  
Chow Test ◽  
Tests Prédictifs

ABSTRACT The main purpose of the paper is to illustrate the use of a dummy variable interpretation of the predictive Chow test against structural change. After describing how the predictive Chow test against structural change in linear regression models can be viewed as a test on the coefficients of a set of dummy variables, it is shown that these can provide useful additional information on the importance and timing of structural changes. Then, the approach is illustrated by applying it to a version of the St. Louis equation (in rate-of-change form) estimated over the period 1953/I-1976/IV: we detect some instability in the 1970's but find it is rather localized, being linked mainly to two quarters (1973/IV and 1975/III).

scholarly journals Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 22 ◽  
Author(s):  
Pierre Perron ◽  
Yohei Yamamoto
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Structural Changes ◽  
Error Variance ◽  
Regression Coefficients ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Finite Sample Properties ◽  
Joint Approach ◽  
Size Distortions

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power.

scholarly journals No Association between Daily Walking and Knee Structural Changes in People at Risk of or with Mild Knee Osteoarthritis. Prospective Data from the Multicenter Osteoarthritis Study

2015 ◽  
Vol 42 (9) ◽  
pp. 1685-1693 ◽  
Author(s):  
Britt Elin Øiestad ◽  
Emily Quinn ◽  
Daniel White ◽  
Frank Roemer ◽  
Ali Guermazi ◽  
...  
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Structural Change ◽  
Knee Osteoarthritis ◽  
Regression Models ◽  
Structural Changes ◽  
Magnetic Resonance Images ◽  
Cartilage Loss ◽  
Logistic Regression Models ◽  
Vigorous Intensity

Objective.We investigated the association between objectively measured daily walking and knee structural change, defined either as radiographic worsening or as cartilage loss, in people at risk of or with knee osteoarthritis (OA).Methods.Participants from the Multicenter Osteoarthritis Study (MOST) with Kellgren-Lawrence grades 0–2 and daily walking (measured with the StepWatch) at the 60-month visit were included. Participants had fixed-flexion, weight-bearing radiographs and knee magnetic resonance images (MRI) at 60 and 84 months. Radiographic worsening was read in both knees using the Osteoarthritis Research Society International grading, and MRI were read for 1 knee using the Whole-Organ MRI Score semiquantitative scoring. OR and 95% CI were calculated comparing those in the middle tertile against the lowest and highest tertiles of daily walking using logistic regression models and generalized estimating equations. Data on walking with moderate to vigorous intensity (min with > 100 steps/min/day) were associated to structural change using multivariate and logistic regression models.Results.The 1179 study participants (59% women) were 67.0 years old (± 7.6), with a mean (± SD) body mass index of 29.8 kg/m2 (± 5.3) who walked 6981 (± 2630) steps/day. After adjusting for confounders, we found no significant associations between daily walking and radiographic worsening or cartilage loss. More time spent walking at a moderate to vigorous intensity was not associated with either radiographic worsening or cartilage loss.Conclusion.Results from the MOST study indicated no association between daily walking and structural changes over 2 years in the knees of people at risk of or with mild knee OA.

scholarly journals SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES

2015 ◽  
Vol 32 (6) ◽  
pp. 1376-1433 ◽  
Author(s):  
Junhui Qian ◽  
Liangjun Su
Keyword(s):  
Regression Models ◽  
Structural Changes ◽  
Equity Premium ◽  
Asymptotic Distributions ◽  
Regression Coefficients ◽  
Tuning Parameter ◽  
Linear Regression Models ◽  
Predictive Regression ◽  
Finite Samples ◽  
Fundamental Information

In this paper, we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso. We show that with probability tending to one, our method can correctly determine the unknown number of breaks, and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a data-driven method to determine the tuning parameter. Monte Carlo simulations demonstrate that the proposed method works well in finite samples. We illustrate the use of our method with a predictive regression of the equity premium on fundamental information.

scholarly journals Consequences of forced migration during early childhood on cognitive well-being in later childhood in Andhra Pradesh, India

2017 ◽  
Vol 3 (2) ◽  
pp. 16 ◽  
Author(s):  
Ashish Kumar Upadhyay ◽  
Swati Srivastava ◽  
Chhavi Paul
Keyword(s):  
Social Support ◽  
Early Childhood ◽  
Regression Models ◽  
Andhra Pradesh ◽  
Forced Migration ◽  
Well Being ◽  
Bivariate Analysis ◽  
Linear Regression Models ◽  
Vulnerable Children ◽  
Early Grade

Unlike its short-term impact on consumption and income, forced migration is expected to deliver a permanent shock to the overall well-being of households, specifically children in the stage of infancy. Studies on the effect of forced migration on child cognitive well-being are few in number. Therefore, the present study is intended to examine the consequences of forced migration during infancy on child cognition at later age. We hypothesized that the effect of forced migration on child cognitive well-being can be mitigated by social support. The study used longitudinal data from three waves of the Young Lives Study (YLS) conducted in 2002, 2006–2007, and 2009 in the state of Andhra Pradesh, India. We used bivariate and multivariate regression models to analyze the consequences of forced migration in early childhood on the cognitive well-being in later childhood. The information on forced migration was collected in Wave 1 (at age 1), whereas the information on the cognitive well-being of the children was collected in Wave 3 (at age 8). Child cognitive well-being was measured using scores obtained by the children on the Peabody Picture Vocabulary Test (PPVT), math, Early Grade Reading Assessment (EGRA), and memory tests. The results of the bivariate analysis show that the mean PPVT, math, EGRA, and memory scores obtained by children from the migrated households were lower than those obtained by children from the non-migrated households. Results of the multivariate linear regression models also show that children from the migrated households were statistically less likely to achieve higher scores on math (coefficient: -2.008, 95% C.I.-3.108, -0.908), EGRA (coefficient: -0.746, 95% C.I.-1.366, -0.126), and memory (coefficient: -0.503, 95% C.I. -0.834, -0.173) as compared to children from the non-migrated households. Our findings also indicate that the effect of forced migration on child cognitive well-being was not mitigated by social support. Findings of this study conclude that forced migration during infancy has a significant effect on child cognitive well-being at later age. Therefore, interventions should be made, paying attention to the most vulnerable children who were displaced during critical development ages.

India Higher Education Atlas: Andhra Pradesh, Goa, Karnataka, Kerala, Puducherry, Tamil Nadu, Telangana - Volume 5

2018 ◽  
Author(s):  
Teja Malladi ◽  
Dhananjayan Mayavel ◽  
Nilakshi Chatterji ◽  
Pratyush Tripathy
Keyword(s):  
Higher Education ◽  
Tamil Nadu ◽  
Andhra Pradesh

Evaluation of Gloriosa superba for Yield Attributing Characters and Quantification of Colchicine Originated from Different Agro Climatic Zones of Tamil Nadu and Andhra Pradesh

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Arun Kumar P. ◽  
Elangaimannan R.
Keyword(s):  
Seed Yield ◽  
Tamil Nadu ◽  
High Performance ◽  
Andhra Pradesh ◽  
Crop Improvement ◽  
Mean Value ◽  
Hplc Method ◽  
Gloriosa Superba ◽  
Climatic Zones ◽  
Colchicine Alkaloid

The study was conducted to evolve Gloriosa superba for yield characters and alkalodi content for selecting elite genotypes for comercial exploitatio n. The genotypes were sowm in Variyankaval village, Udayarpalayam taluk of Ariyalur district, Tamil Nadu. The highest mean value for fresh and dry seed yield was observed in Chittor local. The genotype Mulanur local has recorded the highest mean value for number of pods per plant and number of seeds per pod and Arupukotai local excelled the general mean for the traits seeds per pod, fresh and dry seed yield and also for tuber characters. An investigation was carried out to quantify the colchicine (alkaloid) present in tubers by High Performance Liquid Chromatography (HPLC) method. The genotypes collected from Arupukotai recorded the highest colchicine content (0.760 mg/g) followed by Chittoor (0.578 mg/g) and Mulanur (0.496 mg/g) and there by these three genotypes were utilized for further crop improvement.

Sign in / Sign up

Export Citation Format

Share Document