This document analyzes factors that affect the prices of red wine. It establishes a regression model with the log price of red wine as the dependent variable, and average winter rainfall, seasonal temperature, and harvest rainfall as independent variables. The model explains 74% of variation in price. Hypothesis testing shows that all three weather factors significantly impact price individually and overall. Specifically, higher winter rainfall and temperature increase price, while more harvest rain decreases price. The analysis concludes weather is a major determinant of red wine prices and quality between vintages.

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ECONOMETRICS PROJECT
FACTORS AFFECTING THE PRICES OF
RED WINE
P.PRASHANTH
542, MBE-1ST
YEAR

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INTRODUCTION:
Red wine is a type of wine made from dark-coloured (black) grape
varieties. The actual colour of the wine can range from intense violet,
typical of young wines, through to brick red for mature wines and brown
for older red wines.
The juice from most purple grapes is greenish-white; the red colour
comes from anthocyan pigments (also called anthocyanins) present in
the skin of the grape; exceptions are the relatively uncommon teinturier
varieties, which produce a red colored juice. Much of the red-wine
production process therefore involves extraction of colour and flavour
components from the grape skin.
Bordeaux is a region of France and red Bordeaux wines have been
produced in the same place, and in much the same way, for hundreds of
years. Yet, there are differences in quality and price from year to year
that can sometimes be quite large.Until very recently, these quality
differences have been considered a great mystery.I show that the factors
that affect fluctuations in wine vintage quality can be explained in a
simple quantitative way. In short, I show that a simple statistical analysis
predicts the quality of a vintage, and hence its price, from the weather
during its growing season.
When a red Bordeaux wine is young it is astringent and most people will
find it unpleasant to drink. As a wine ages it loses its astringency.
Because Bordeaux wines taste better when they are older, there is an
obvious incentive to store them until they have comeof age. As a result,
there is an active market for both younger and older wines.
Traditionally, what has not been so obvious is exactly how good a wine
will be when it matures. This ambiguity leaves room for speculation, and
as a result, the price of the wine when it is first offered in its youth will
often not match the price of the wine when it matures. In this analysis

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we study how the price of mature wines may be predicted from data
available when the grapes are picked, and then to explore the effect that
this has on the initial and final prices of the wines.
OBJECTIVE OF THE PROJECT:
The main objective of this project to estimate the price and quality of red
wine.
-To understand that we should first determine the factors affecting the
price and quality of red wine.
-We later do a market research to understand the trends prevailing in the
market and then collect the required data
-Using any statistical software we do the specifications of the model and
and estimate the regression coefficients .
-Do hypothesis testing to check the validity of the independent variable
-Interpret the results.

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FACTORS AFFECTING THE PRICE OF RED WINE:
We have considered :
-Average seasonal temperature in the region: When the average daily
temperature is between 63 and 68 °F (17 and 20 °C) the wine will begin
flowering. Therefore, we have taken the average temperature of the
region.
-Winter rain: The climate characteristics of a wine region will have
significant influence on the viticulture in the area.The amount of rainfall
is another defining characteristics. On average, a grapevine needs around
28 inches (700 mm) of water for sustenance during the growing season.
-Harvest rain: Ripeness is the completion of the ripening process of
wine grapes on the vine which signals the beginning of harvest. The
harvesting of wine grapes (Vintage) is one of the most crucial steps in
the process of winemaking. The time of harvest is determined primarily
by the ripeness of the grape as measured by sugar, acid and tannin levels
with winemakers basing their decision to pick based on the style of wine
they wish to produce. The weather can also shape the timetable of
harvesting with the threat of heat, rain, hail, and frost which can damage
the grapes and bring about various vine diseases. In addition to
determining the time of the harvest, winemakers and vineyard owners
must also determine whether to utilize hand pickers or mechanical
harvester. Harvest plays a major role in determining the ripening process
for wine.

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DATA:
The data for determining the price and quality of red wine has been
gathered from various sources such as liquidasset.com and American
association of wine economics. The data is collected from the year 1962
to 1990.
They are many factors which determine the price of the red wine, such
as winter rainfall, harvest rain, average temperature of the region,
population of the country, age of the wine etc. In this study, we have
considered the log prices of the red wine as the dependent variable and
for independent variables we have taken winter rainfall, harvest rain and
average seasonal temperature in the region.

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THE REGRESSION MODEL:
The analysis of this model is done on a multiple regression model.
We have considered the log-linear equation as shown below:
log Yi= β0+β1X1+β2X2+β3X3+µi
Where , Yi=price of the red wine
X1=average winter rain in the region (mm)
X2= average seasonal temperature of the region (Celsius)
X3=average harvest rain in the region (mm)
The regression result is as follows:

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Based on the regression ,we can say that the model explains 74.00%
variation in determining the price of the red wine. The adjusted R
squared value is about 70.69%. The standard error of the estimate of the
regression is 34.35% .
HYPOTHESIS TESTING TO CHECK THE INDIVIDUAL
SIGNIFICANCE OF THE FACTORS AFFECTING THE PRICE
OF THE RED WINE:
-Hypothesis 1:
H01: β1=0; X1: average winter rainfall doesn’t individually affect the
price, Yi.
H11:β1≠0;X1: average winter rainfall individually significantly affects
the price, Yi.
We test our hypothesis at 5% level of significance.
p-value =0.036 is less than 0.05 hence, we reject the H01. And we
conclude that winter rainfall significantly affects the price of wine.
Also β1=0.001282 (coefficient) shows that there is a direct relationship
between the price and the average winter rainfall.
-Hypothesis 2:
H02:β2=0;X2:the average seasonal temperature of the region doesn’t
significantly affect the price , Yi.

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H12:β20;X2: the average seasonal temperature of the region individually
significantly affects the price, Yi.
At 5%level of significance we check the hypothesis.
P-value=1.11E-06 is less than 0.05 hence, we reject H02. And we
conclude that average seasonal temperature significantly affects the
price of wine.
Also β2=0.7123(coefficient) shows that there is a direct relationship
between the price and average seasonal temperature.
-Hypothesis 3:
H03:β3=0;X3 , and average harvest rain doesn’t individually affect
significantly affect the price, Yi.
H13:β3≠0;X3 average harvest rain does individually significantly affect
the price , Yi.
We test the hypothesis at 5% level of significance.
p-value=0.0010 less than 0.05, we reject H03 and H13 , the average
harvest rain individually affects significantly affects the price, Yi.
Β3=-0.00362 shows that there is inverse relationship between the price
of wine and the harvest rainfall.

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TEST FOR OVERALL SIGNIFICANCE:
H0:β1=β2=β3=0; model is not overall significant.
H1:β1≠β2≠β3≠0;model is overall significant.
F-value= (R2/k-1)/ (1-R2)/(n-k)=21.90
At 5% level of significance , we test the hypothesis since p-
value=6.25E-07 is less than 0.05 we can reject our null hypothesis and
say that our model is overall significant.
ECONOMIC SIGNIFICANCE OF THE ESTIMATED
PARAMETERS:
*β1=0.001282
This means that with 1mm increase in the winter rainfall the price of red
wine will be increased 0.1282 percent, as the log of the prices for the
wine is considered
*β2=0.712319
This means that with 1 celsius increase in the average temperature of the
region the price of the wine will be increased by 71.23percent.
*β3=-0.00362
This means that with 1mm increase in the harvest rain the prices of the
wine will decrease by 0.3 percent.

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CONCLUSION:
There is much variability in the prices of red wines, as shown,
much of it can be explained. A statistical analysis of the weather
in which a vintage is grown, and consideration of its age, can be
used to describe much of the variability in prices across red wine.
We have seen how the change in winter rainfall, average
temperature of the region and harvest rain affect the change in
prices . They are many more factors that determine the prices of
wine but we have seen that about 74% of variability has been
explained with these variables.

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