scholarly journals Ragweed Pollen Concentration Predict Seasonal Oculo-rhinitis and Asthma Severity in Subjects Allergic to Ragweed.

2021 ◽  
Author(s):  
Maira Bonini ◽  
GIANNA MONTI ◽  
MATTEO PELAGATTI ◽  
VALENTINA CERIOTTI ◽  
ELISABETTA RE ◽  
...  
Keyword(s):  
Symptom Severity ◽  
Asthma Severity ◽  
Temporal Variations ◽  
Ragweed Pollen ◽  
P Value ◽  
Pollen Concentration ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Distributed Lag Models ◽  
Amb A 1

Abstract Objectives: 1. To investigate the correlation between ragweed pollen concentration and conjunctival, nasal and asthma symptoms severity in patients allergic to ragweed using ambient pollen exposure in the Milan area during the 2014 ragweed season; 2. to calculate the pollen / symptom thresholds and 3. to assess the effectiveness of ragweed Allergen Immuno Therapy (AIT).Patients: 66 subjects allergic to Amb a 1 enrolled in the study and were divided into two cohorts: AIT treated (24) and non-AIT treated (42).Measurements: Pollen counts and daily symptom/medication patient diaries. Autoregressive Distributed Lag Models were used to develop predictive models of daily symptoms and to evaluate the short-term effects of temporal variations in pollen concentration on the onset of symptoms. Results: We found significant correlations between ragweed pollen load and the intensity of symptoms, for all three symptom categories respectively, both in non-AIT treated (𝛕= 0.341, 0.352, 0.721 and ρ = 0.48, 0.432, 0.881, p-value < 0.001) and in AIT treated patients (O= 0.46, 0.610, 0.66 and ρ = 0.692, 0.805, 0.824; p-value < 0.001). In both cohorts, we observed a positive correlation between the number of symptoms reported and drug use. Mean symptom levels were significantly greater in non-AIT treated than in AIT treated patients (p < 0.001) for all symptom categories. Pollen concentration thresholds for three symptom severity levels were calculated.Conclusions: Ragweed pollen concentration is predictive of symptom severity in ragweed (Amb a 1) allergy patients. AIT treated patients had significantly reduced mean symptom levels compared to non-AIT patients.

ASYMPTOTIC BEHAVIOR OF THE CUSUM OF SQUARES TEST UNDER STOCHASTIC AND DETERMINISTIC TIME TRENDS

2011 ◽  
Vol 27 (4) ◽  
pp. 913-927 ◽  
Author(s):  
Bent Nielsen ◽  
Jouni S. Sohkanen
Keyword(s):  
Asymptotic Behavior ◽  
Least Squares ◽  
Time Trends ◽  
Asymptotic Theory ◽  
Ordinary Least Squares ◽  
Nuisance Parameters ◽  
Forecast Errors ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Distributed Lag Models

We generalize the cumulative sum of squares (CUSQ) test to the case of nonstationary autoregressive distributed lag models with deterministic time trends. The test may be implemented with either ordinary least squares residuals or standardized forecast errors. In explosive cases the asymptotic theory applies more generally for the least squares residuals-based test. Preliminary simulations of the tests suggest a very modest difference between the tests and a very modest variation with nuisance parameters. This supports the use of the tests in explorative analysis.

scholarly journals Correction: Predicting the influence of climate on grassland area burned in Xilingol, China with dynamic simulations of autoregressive distributed lag models

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245828
Author(s):  
Ali Hassan Shabbir ◽  
Jiquan Zhang ◽  
James D. Johnston ◽  
Samuel Asumadu Sarkodie ◽  
James A. Lutz ◽  
...  
Keyword(s):  
Dynamic Simulations ◽  
Area Burned ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Distributed Lag Models

scholarly journals The Contribution of Commercial Banks to GDP Growth in Nigeria Using Autoregressive Distributed Lag Model

2019 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Godson Obinna Ogujiofor ◽  
Clara Adebukola Adenomon
Keyword(s):  
Commercial Banks ◽  
P Value ◽  
Gdp Growth ◽  
Annual Data ◽  
Ardl Model ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Long Run ◽  
Lending Rates ◽  
Adf Test

In most econometrics literature, the Autoregressive Distributed Lag (ARDL) model is often applied in many economic analyses to study short and long run relationships. This is because ARDL model can deal with economic variables that are integrated of different order (I(0), I(1) or combination of both) and also it is robust where there is single long-run relationship between the underlying variables in a simple sample size. This study applied the ARDL model to examine the contributions of commercial Banks to GDP growth in Nigeria. To achieve this, annual data covering 1981 to 2015 for loans and advances, savings, lending rates and GDP of Financial Institutions were collected from CBN bulletin. The ADF test revealed that the variables are I(1) except for lending rate which was of I(0) order. The ARDL(1,1,1,2) model revealed that loans and advances, and lending rates are significantly positively related to GDP in Nigeria but savings was not significant in the model. The model revealed some evidence of short run relationships while the ecm(-1) was -0.6156 (P-value=0.0038&lt;0.05) which means that the rate of the speed of adjustment to equilibrium is 61.56% annually. The estimated model is free from serial correlation, multicollinearity, heteroscedasticity while the model is stable and the residuals are normally distributed. The study recommends that savings and savings culture should be encouraged in Nigeria since economic theory states that savings and investment are related in any economic development.

scholarly journals Cointegration Testing and Dynamic Simulations of Autoregressive Distributed Lag Models

2018 ◽  
Vol 18 (4) ◽  
pp. 902-923 ◽  
Author(s):  
Soren Jordan ◽  
Andrew Q. Philips
Keyword(s):  
Error Correction ◽  
Political Science ◽  
Applied Econometrics ◽  
Cointegration Test ◽  
Dynamic Simulations ◽  
Error Correction Models ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Distributed Lag Models ◽  
Cointegration Testing

In this article, we introduce dynamac, a suite of commands designed to assist users in modeling and visualizing the effects of autoregressive distributed lag models and in testing for cointegration. We discuss the bounds cointegration test proposed by Pesaran, Shin, and Smith (2001, Journal of Applied Econometrics 16: 289–326), which we have adapted into a command. Because the resulting models can be dynamically complex, we follow the advice of Philips (2018, American Journal of Political Science 62: 230–244) by introducing a flexible command designed to dynamically simulate and plot a variety of types of autoregressive distributed lag models, including error-correction models.

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