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 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.

Abstract P211: Association Between Urbanization-related Change in Diet, the Gut Microbiota and Microbiome-related Metabolites in a Rapidly Urbanizing Environment in China

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Annie Green Howard ◽  
Anthony Fodor ◽  
Shan Sun ◽  
Yiqing Wang ◽  
Matthew Tsilimigras ◽  
...  
Keyword(s):  
Microbial Community ◽  
Linear Regression ◽  
Gut Microbiota ◽  
Regression Models ◽  
Rate Of Change ◽  
Linear Regression Models ◽  
Food Component ◽  
Animal Source ◽  
Gut Microbial Community ◽  
Animal Source Food

Introduction: Numerous studies suggest that there are rural-urban differences in gut microbial community, partially due the Westernization of diet. However, most studies are based on short-term follow-up and cannot assess how the transition from traditional to Western diets over time shapes human gut microbiota and whether these microbiota changes subsequently impact the metabolomic profile of the host. Hypothesis: We hypothesize that trajectories in diet shape components of the gut microbial community, with strong associations for microbiota and microbiome-mediated metabolites. Methods: We used growth mixture models to estimate individual-specific intercepts (current % calories from animal source food in 2015) and slopes (change in % calories from animal source food from 1991-2015) in 1,468 adults from the population-based China Health and Nutrition Survey (CHNS) across 20 years of urbanization-related change. We then tested the association between the gut microbiota genera in 2015 with these intercepts and slopes in linear regression models, allowing for interactions between the intercepts and slopes. In the subset of the sample with metabolomic measures collected (n = 500), we then tested whether bacteria associated with change in diet (FDR corrected p-values < 0.05 from the joint test of both main effect and interaction) were associated with microbiome-related metabolites using linear regression models. Results: Controlling for current % calories from animal source food, we found 57 gut microbiota measures that were significantly associated with the rate of change in % animal source calories, with the majority (19%) coming from the order Clostridiales. For 55 gut microbiota measures, we found evidence that the association between current diet and gut microbiota was modified by the rate of change in diet. Thirty-two microbiome-related metabolites, over 50% of which were involved in the benzoate metabolism or food component pathways, were associated with these gut microbiota measures. Conclusions: Our findings suggest that long-term change in calories consumed from animal source foods may shape the composition of the gut microbial community. Further, our metabolome findings suggest that pathways linking diet and the microbiome through microbial metabolites may be particularly relevant.

Abstract: A Note on Dummy Variables and the Chow Test: Their Equivalence and Uses in Testing

1977 ◽  
Vol 12 (4) ◽  
pp. 675-675
Author(s):  
Marcellus S. Snow
Keyword(s):  
Analysis Of Covariance ◽  
Variable Technique ◽  
Test Analysis ◽  
Dummy Variables ◽  
Dummy Variable ◽  
Chow Test ◽  
Variable Approach ◽  
Regression Parameters ◽  
Regression Framework ◽  
Dummy Variable Approach

A general proof using matrices is given proving the equivalence of the Chow test (analysis of covariance) and an appropriate adaptation of the dummy variable technique. Implications of hypothesis testing in the linear regression framework are reviewed for each method. The dummy variable approach is found to have the following advantages: (a) it is more convenient in testing hypotheses regarding the equality of subvectors of the parameter vectors from separate regressions, in particular not requiring the running of new regressions as the Chow test approach sometimes does; and (b) a more general form of hypothesis can be tested, namely that corresponding regression parameters differ by a constant other than zero.

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 Modelling of copper(II) binding to pentapeptides related to atrial natriuretic factor using the 3χv connectivity index / Modeliranje vezivanja bakra(II) za pentapeptide povezane s atrijalnim natriuretičkim faktorom pomoću indeksa povezanosti 3χv

2015 ◽  
Vol 66 (2) ◽  
pp. 165-170 ◽  
Author(s):  
Ante Miličević ◽  
Nenad Raos
Keyword(s):  
Atrial Natriuretic Factor ◽  
Regression Models ◽  
Molecular Graph ◽  
Connectivity Index ◽  
Linear Regression Models ◽  
Dummy Variable ◽  
Natriuretic Factor ◽  
Peptide Interactions ◽  
Phenylalanine Residue ◽  
Atrial Natriuretic

Abstract Using molecular graph theory we studied the binding of NSFRY-NH2 and 12 related pentapeptide amides to Cu(II) as a model system for atrial natriuretic factor (ANF) peptide interactions with copper. Linear regression models based on the valence connectivity index of the 3rd order (3χv) reproduced experimental stability constants (log β) for 1N, 2N, 3N, and 4N coordinated complexes with the standard error of 0.30-0.39 log β units. We developed separate models for seven tyrosinic (N=28) and five non-tyrosinic peptides (N=20), and a common model for both kinds of peptides (N=48) with an indicator (dummy) variable. The results indicate additional aromatic stabilisation in 4N complexes due to metal cation-π interactions with tyrosine but not with the phenylalanine residue. We have also amended the log K and log K* values to correct miscalculations published by Janicka-Klos et al. in 2013

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.

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