The Impact of Aggregate Level Alcohol Consumption on Homicide Rates: A Time Series Analysis

2021 ◽  
pp. 0306624X2110287
Author(s):  
TK Vinod Kumar
Keyword(s):  
Time Series ◽  
Alcohol Consumption ◽  
Moving Average ◽  
Policy Implications ◽  
Autoregressive Moving Average ◽  
Aggregate Level ◽  
Homicide Rates ◽  
Time Series Analyses ◽  
The Impact ◽  
The Relationship

Consumption of alcohol has an impact on violent crimes and homicides. The study examines the association between aggregate level consumption of spirit and homicide rates in the State of Kerala in India. Time-series analyses were conducted by building Autoregressive Moving Average with Exogenous Variables (ARMAX) models and OLS Regression models to explain the relationship between the monthly rate of consumption of alcoholic spirits and homicide rates. The study concludes that consumption of alcoholic spirits has a statistically significant impact on the total homicide rates and the male and female homicide rates. The study has significant policy implications being one of the first studies examining the relationship between alcohol consumption and homicide rates in India and suggesting methods to address challenges of adverse public health consequences associated with alcohol consumption.

Measuring Value Change in Western Industrialized Societies: The Impact of Unemployment

1991 ◽  
Vol 85 (3) ◽  
pp. 905-920 ◽  
Author(s):  
Harold D. Clarke ◽  
Nitish Dutt
Keyword(s):  
Time Series ◽  
Economic Conditions ◽  
Aggregate Level ◽  
Value Change ◽  
Short Term ◽  
Cross Sectional ◽  
The Past ◽  
Value Priorities ◽  
Time Series Analyses ◽  
The Impact

During the past two decades a four-item battery administered in biannual Euro-Barometer surveys has been used to measure changing value priorities in Western European countries. We provide evidence that the measure is seriously flawed. Pooled cross-sectional time series analyses for the 1976–86 period reveal that the Euro-Barometer postmaterialist-materialist value index and two of its components are very sensitive to short-term changes in economic conditions, and that the failure to include a statement about unemployment in the four-item values battery accounts for much of the apparent growth of postmaterialist values in several countries after 1980. The aggregate-level findings are buttressed by analyses of panel data from three countries.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

scholarly journals The impact of alcohol consumption on homicide: A time-series analysis of three Nordic countries

2018 ◽  
Vol 17 (3) ◽  
pp. 352-369
Author(s):  
Martti Lehti ◽  
Reino Sirén
Keyword(s):  
Time Series ◽  
Alcohol Consumption ◽  
Time Series Analysis ◽  
19Th Century ◽  
Statistical Association ◽  
Political Factors ◽  
Homicide Rates ◽  
Early 19Th Century ◽  
The Impact ◽  
Over Time

The article explores the statistical association between annual alcohol consumption and homicide mortality in Finland, Sweden and Norway from the early 19th century to 2013. The results show statistically significant impacts on overall and male homicide mortality in Finland and on male homicide mortality in Sweden. In Norway, we found no significant impacts. The results suggest that changes in the level of alcohol consumption have had a stronger impact on homicide rates in Finland, characterized by a heavier drinking culture, than in Norway or Sweden. The strength of the association between alcohol consumption and homicide levels seems also to vary over time and to be conditioned by economic and socio-political factors.

On-Line Structural Damage Feature Extraction Based on Autoregressive Statistical Pattern of Time Series

2014 ◽  
Author(s):  
Ming-Hui Hu ◽  
Shan-Tung Tu ◽  
Fu-Zhen Xuan ◽  
Zheng-Dong Wang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Structural Damage ◽  
Power Plants ◽  
Damage Identification ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Statistical Pattern ◽  
On Line ◽  
The Relationship

The main aim of this paper is to demonstrate an autoregressive statistical pattern analysis method for the on-line structural health monitoring based on the damage feature extraction. The strain signals obtained from sensors are modeled as autoregressive moving average (ARMA) time series to extract the damage sensitive features (DSF) to monitor the variations of the selected features. One algebra combination of the first three AR coefficients is defined as damage sensitive feature. Using simple theory of polynomial roots, the relationship between the first three AR coefficient and the roots of the characteristic equation of the transfer function is deduced. Structural damage detection is conducted by comparing the DSF values of the inspected structure. The corresponding damage identification experiment was investigated in X12CrMoWVNbN steel commonly used for rotor of steam turbine in power plants. The feasibility and validity of the proposed method are shown.

Asymmetric price transmission in the Chinese pork and pig market

2018 ◽  
Vol 120 (1) ◽  
pp. 120-132 ◽  
Author(s):  
Xiaoxia Dong ◽  
Colin Brown ◽  
Scott Waldron ◽  
Jing Zhang
Keyword(s):  
Moving Average ◽  
Price Transmission ◽  
Policy Implications ◽  
Autoregressive Moving Average ◽  
Key Factors ◽  
Content Type ◽  
Threshold Autoregressive ◽  
Asymmetric Price Transmission ◽  
Moving Average Models ◽  
The Impact

Purpose The purpose of this paper is to analyze price transmission in the Chinese pork market between 1994 and 2016 and examine any incidence and causes of asymmetric price transmission. Design/methodology/approach The approach uses threshold autoregressive models, asymmetric error correction models and autoregressive moving average models to examine the price transmission using monthly pig and pork prices from 1994 to 2016. Findings While a symmetric price transmission between pork and pig prices was identified for the period between June 1994 and June 2007, an asymmetric price transmission response between pork and pig prices was found for the period July 2007 to June 2016. Key factors behind the asymmetric price transmission include the chicken price and China’s provisional purchasing and stockpiling policy which is having a counter-productive impact on prices. Originality/value The paper contributes to the literature by examining price transmission in two different periods: 1994 to 2007 where prices are lower and more stable; and 2007 to 2016 where prices are higher and volatile. The paper examines the impact of production and market policies on price transmission in the Chinese pork and pig market, with several policy implications.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Relationship between Changes over Time in Factors, Including the Impact of Meteorology on Photochemical Oxidant Concentration and Causative Atmospheric Pollutants in Kawasaki

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 446
Author(s):  
Akinori Fukunaga ◽  
Takaharu Sato ◽  
Kazuki Fujita ◽  
Daisuke Yamada ◽  
Shinya Ishida ◽  
...  
Keyword(s):  
Moving Average ◽  
Atmospheric Pollutants ◽  
Important Indicator ◽  
Photochemical Oxidant ◽  
Oxidant Concentration ◽  
Using Data ◽  
Changes Over Time ◽  
The Impact ◽  
The Relationship ◽  
The City

To clarify the relationship between changes in photochemical oxidants’ (Ox) concentrations and their precursors in Kawasaki, a series of analyses were conducted using data on Ox, their precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), and meteorology that had been monitored throughout the city of Kawasaki for 30 years from 1990 to 2019. The trend in air temperature was upward, wind speed was downward, and solar radiation was upward, indicating an increasing trend in meteorological factors in which Ox concentrations tend to be higher. Between 1990 and 2013, the annual average Ox increased throughout Kawasaki and remained flat after that. The three-year moving average of the daily peak increased until 2015, and after that, it exhibited a slight decline. The amount of generated Ox is another important indicator. To evaluate this, a new indicator, the daytime production of photochemical oxidant (DPOx), was proposed. DPOx is defined by daytime averaged Ox concentrations less the previous day’s nighttime averaged Ox concentrations. The trend in DPOx from April to October has been decreasing since around 2006, and it was found that this indicator reflects the impact of reducing emissions of NOx and VOCs in Kawasaki.

scholarly journals Comparative Time Series Analysis of Perceptual Responses to Electroacoustic Music

2012 ◽  
Vol 29 (4) ◽  
pp. 359-375 ◽  
Author(s):  
Freya Bailes ◽  
Roger T. Dean
Keyword(s):  
Time Series ◽  
Sound Intensity ◽  
Electroacoustic Music ◽  
Affective Content ◽  
Musical Expertise ◽  
Perceived Change ◽  
Time Series Analyses ◽  
Time Perceptions ◽  
The Relationship ◽  
Acoustic Measure

this study investigates the relationship between acoustic patterns in contemporary electroacoustic compositions, and listeners' real-time perceptions of their structure and affective content. Thirty-two participants varying in musical expertise (nonmusicians, classical musicians, expert computer musicians) continuously rated the affect (arousal and valence) and structure (change in sound) they perceived in four compositions of approximately three minutes duration. Time series analyses tested the hypotheses that sound intensity influences listener perceptions of structure and arousal, and spectral flatness influences perceptions of structure and valence. Results suggest that intensity strongly influences perceived change in sound, and to a lesser extent listener perceptions of arousal. Spectral flatness measures were only weakly related to listener perceptions, and valence was not strongly shaped by either acoustic measure. Differences in response by composition and musical expertise suggest that, particularly with respect to the perception of valence, individual experience (familiarity and liking), and meaningful sound associations mediate perception.

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