scholarly journals A time-series analysis of relevant pollutants in Hamilton (Ontario) and induced mortality

2016 ◽  
Author(s):  
Yu Fan Zeng ◽  
Adree Khondker
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
The United States ◽  
Dose Response Relationship ◽  
Current Time ◽  
Predictive Regression ◽  
The Past ◽  
Series Analysis ◽  
Time Series Analyses ◽  
The Relationship

The relationship between air pollution and public health has gained increasing attention in the past decade. Many time-series analyses have been conducted worldwide, including in all the major cities of the United States, Europe, and Asia. However, the most current time-series analysis study of Ontario, Canada dates back to 2012 and includes only a single city, calling the need of a more recent study at a provincial scale. As a result, we propose to conduct time-series analyses of major Ontario cities and then use a hierarchical model to pool the results and construct a dose-response relationship and generate a predictive regression.

scholarly journals A time-series analysis of relevant pollutants in Hamilton (Ontario) and induced mortality

2016 ◽  
Author(s):  
Yu Fan Zeng ◽  
Adree Khondker
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
The United States ◽  
Dose Response Relationship ◽  
Current Time ◽  
Predictive Regression ◽  
The Past ◽  
Series Analysis ◽  
Time Series Analyses ◽  
The Relationship

The relationship between air pollution and public health has gained increasing attention in the past decade. Many time-series analyses have been conducted worldwide, including in all the major cities of the United States, Europe, and Asia. However, the most current time-series analysis study of Ontario, Canada dates back to 2012 and includes only a single city, calling the need of a more recent study at a provincial scale. As a result, we propose to conduct time-series analyses of major Ontario cities and then use a hierarchical model to pool the results and construct a dose-response relationship and generate a predictive regression.

Exchange rates, Canadian lumber imports, and United States prices: a time-series analysis

1988 ◽  
Vol 18 (12) ◽  
pp. 1587-1594 ◽  
Author(s):  
Joseph Buongiorno ◽  
Jean-Paul Chavas ◽  
Jussi Uusivuori
Keyword(s):  
United States ◽  
Time Series ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
The United States ◽  
Monthly Data ◽  
Softwood Lumber ◽  
The Past ◽  
Series Analysis

Softwood lumber imports by the United States from Canada more than doubled during the past 10 years. The objective of this paper was to investigate two possible reasons for this change: (i) the increase in value of the U.S. dollar relative to the Canadian dollar, and (ii) the rise in the price of softwood lumber in the United States. The method used was time-series analysis, leading to measures of feedback and long-term multipliers between imports, exchange rate, and U.S. price. The results, based on monthly data from January 1974 to January 1986, suggested that 68% of the rise in Canadian imports during this period was due to the rise in the price of softwood lumber in the United States. The exchange rate, however, was not found to have a significant effect on imports. The findings also indicate that the increase in imports has not led to a decline in the price received by U.S. producers.

scholarly journals Time-series analysis for porcine reproductive and respiratory syndrome in the United States

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0195282 ◽  
Author(s):  
Andréia Gonçalves Arruda ◽  
Carles Vilalta ◽  
Pere Puig ◽  
Andres Perez ◽  
Anna Alba
Keyword(s):  
United States ◽  
Time Series ◽  
Time Series Analysis ◽  
The United States ◽  
Series Analysis

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

scholarly journals Market Response, Competitive Behavior, and Time Series Analysis

1980 ◽  
Vol 17 (4) ◽  
pp. 470-485 ◽  
Author(s):  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Model Building ◽  
Competitive Reaction ◽  
Marketing Model ◽  
Series Analysis ◽  
Marketing Variables ◽  
Major Airlines ◽  
Market Variables ◽  
The Relationship

The author's principal objective is to present a framework for market analysis which specifically models primary demand, competitive reaction, and feedback effects of the market variables. The approach is an extension of earlier work by Clarke and by Lambin, Naert, and Bultez on the relationship among the elasticities of the marketing variables. The author develops this framework and formulates an approach for empirical applications based on principles of time series analysis. In particular, Granger's well-known causality definition is used in conjunction with Box-Jenkins analysis to find the nonzero elements in the marketing model. These principles are applied empirically to the case of a city pair of the U.S. domestic air travel market, where three major airlines compete on the basis of flight scheduling and advertising. The analysis reveals that flight scheduling has a market-expansive or a competitive effect, depending on the competitor, and that advertising does not have a significant impact on performance. In addition, several patterns of competitive reactions are found. The author offers observations on the theoretical and empirical aspects of this approach to marketing model building.

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