scholarly journals Splendide Mendax: False Label Claims About High and Rising Alcohol Content of Wine

2015 ◽  
Vol 10 (3) ◽  
pp. 275-313 ◽  
Author(s):  
Julian M. Alston ◽  
Kate B. Fuller ◽  
James T. Lapsley ◽  
George Soleas ◽  
Kabir P. Tumber
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Consumer Preferences ◽  
Series Data ◽  
Alcohol Content ◽  
Demand Shifts ◽  
Expert Ratings ◽  
Jel Classifications

AbstractAre wine alcohol labels accurate? If not, why? We explore the high and rising alcohol content of wine and examine incentives for false labeling, including the roles of climate, evolving consumer preferences, and expert ratings. We draw on international time-series data from a large number of countries that experienced different patterns of climate change and influences of policy and demand shifts. We find systematic patterns that suggest that rising wine alcohol content may be a nuisance by-product of producer responses to perceived market preferences for wines having more-intense flavours, possibly in conjunction with evolving climate. (JEL Classifications: D22, L15, L66, Q18, Q54).

On the Constancy of Hedonic Wine Price Coefficients over Time

2019 ◽  
Vol 14 (2) ◽  
pp. 182-207 ◽  
Author(s):  
Benoît Faye ◽  
Eric Le Fur
Keyword(s):  
Time Series ◽  
Literature Review ◽  
Time Series Data ◽  
Hedonic Model ◽  
Series Data ◽  
Extensive Literature ◽  
Long Run ◽  
The Stability ◽  
Over Time ◽  
Jel Classifications

AbstractThis article tests the stability of the main hedonic wine price coefficients over time. We draw on an extensive literature review to identify the most frequently used methodology and define a standard hedonic model. We estimate this model on monthly subsamples of a worldwide auction database of the most commonly exchanged fine wines. This provides, for each attribute, a monthly time series of hedonic coefficients time series data from 2003 to 2014. Using a multivariate autoregressive model, we then study the stability of these coefficients over time and test the existence of structural or cyclical changes related to fluctuations in general price levels. We find that most hedonic coefficients are variable and either exhibit structural or cyclical variations over time. These findings shed doubt on the relevance of both short- and long-run hedonic estimations. (JEL Classifications: C13, C22, D44, G11)

scholarly journals Climate Change Driven Food Inflation in Ethiopia?

2018 ◽  
Vol 8 (1) ◽  
pp. 13-22
Author(s):  
Berhe Gebregewergs Hagos
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Average Price ◽  
Ceteris Paribus ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Lag Model ◽  
Food Inflation

The research dealt with the relationships between temperature variability and price of food stuffs in Tigrai using 84 months collected time series data thereby applied a Univariate econometric tool and finite Distributed Lag Model in defining the variables and outcome of the study. As a result, the econometric regression analysis witnessed that a 1oC temperature rise contributed the average price of food stuffs such as barley price rose up by 80 percent, maize 186 percent, sorghum close to 275 percent, wheat 60 percent, and 170 percent in white Teff over the years, ceteris paribus.

Terrorism and Wine Tourism: The Case of Museum Attendance

2018 ◽  
Vol 13 (4) ◽  
pp. 375-383 ◽  
Author(s):  
Olivier Gergaud ◽  
Florine Livat ◽  
Haiyan Song
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Terrorist Attacks ◽  
Series Data ◽  
Regression Estimation ◽  
Sample Period ◽  
Wine Tourism ◽  
The Impact ◽  
The City ◽  
Jel Classifications

AbstractIn this article, we use attendance data from La Cité du Vin, a wine museum in the city of Bordeaux, to assess the impact of the recent wave of terror that affected France on wine tourism. We use recent count regression estimation techniques suited for time series data to build a prediction model of the demand for attendance at this museum. We conclude that the institution lost about 5,000 visitors over 426 days, during which 14 successive terrorist attacks took place. This corresponds to almost 1% of the total number of visitors in the sample period. (JEL Classifications: L83, Z30)

A daily burned area mapping method using AVHRR time-series data

2021 ◽  
Author(s):  
Shuhan Lou ◽  
Yuanhong Liao ◽  
Yufu Liu ◽  
Yuqi Bai
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Time Series Data ◽  
Mapping Method ◽  
Series Data ◽  
Burned Area ◽  
Burnt Area ◽  
Area Data

<p>The study of complex interactions between fire and atmospheric dynamics of the earth system is drawing increasing attention in recent years, especially when fire seasons are extended due to global warming, where the historical daily burnt area data played a pivotal role in analyzing wildfire regimes change. Existing products could not fully meet the temporal requirements: daily burnt area data in global fire emissions database (GFED4) starts from mid-2000 using MODIS while ESA Fire Climate Change Initiative (FireCCILT10) Dataset from 1982 to 2017 is provided on a monthly grid.</p><p>Advanced Very High Resolution Radiometer (AVHRR) series of sensors are widely used to develop pre‐MODIS daily historical records. However, compared to MODIS, the AVHRR sensor has a lower radiometric and geometric quality and is missing Short Wave Infrared (SWIR) band. To address the data quality problem, this research study presents a time-series mapping method for daily burned area using AVHRR composite. Daily fire-sensitive indices are calculated to develop a time-series data composite which is masked by the burnable surface of GLASS_GLC land cover product. Then, Continuous Change Detection and Classification (CCDC) time-series model, which originally implemented on Landsat data monitoring land cover change, is revised to detect an abrupt change in the time-series data composite and remove noise, ensuring temporal consistency. The image of a time-series breakpoint is further classified using a spatial contextual method to distinguish biomass burning from other forest degradation change like a landslide and is used to generate burned area probability map.</p><p>The methodology is verified in California, US, where fuel aridity increased during 1984–2015 driven by anthropogenic climate change. The samples are collected based on the National Monitoring Trends in Burn Severity（MTBS）Burned Areas Boundaries Dataset from 1984 – 2018 and California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) fire perimeters from since 1950. Primary results show that the proposed method can effectively detect burned area on daily basis with CCDC algorithm reducing the complexity of change detection.</p>

scholarly journals Effect of Climate Variables on Yield of Major Crop in Samastipur District of Bihar: A time Series Analysis

2020 ◽  
Vol 65 (4) ◽  
Author(s):  
Meera Kumari
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Crop Yield ◽  
Minimum Temperature ◽  
Time Series Data ◽  
Annual Rainfall ◽  
Series Data ◽  
Climate Variables ◽  
Climatic Variable ◽  
Climate Variable

Climate change influences crop yield vis-a-vis crop production to a greater extent in Bihar. Climate change and its impacts are well recognizing today and it will affect both physical and biological system. Therefore, this study has been planned to assess the effect of climate variables on yield of major crops, adaptation measures undertaken in Samastipur district of Bihar. Secondary data on yield of maize and wheat crops were collected for the period from 1999-2019 to describe the effects of climate variable namely rainfall, maximum and minimum temperature on yield of maize and wheat. Analysis of time series data on climate variables indicated that annual rainfall was positively related to yields while maximum and minimum temperature had a negative but significant impact on maize and wheat yields. It actually revealed that other factors, such as; type of soil, soil fertility and method of farming may also be responsible for crop yield. Trend in cost as well as income of farmers indicated that income and cost of cultivation has no significant relationship with climate variable. On the basis of above observation it may be concluded that level of income of farmers changed due to change in the other factors rather than change in climatic variable over the period under study as cost of cultivation increases with increased in the price of input over the period but not due to change in climatic variable.

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