‘Funny conditions we are holding ‘ is a statment of the obvious we have used for coevalss as a salutation. When the deep cold stopping points long and heavy snow and snowstorms give us the trembles we replace “ amusing ” with something stronger, such as “ awful ” , “ ghastly ” .
At times like these people ask what is go oning to the conditions.
So we go to the experts, who tells us, in linguistic communication appropriate to the topic, what happened yesterday, what is go oning today, and what might go on in the following few old ages. Weather and clime specializers all over the universe have ammassed a huge quanity of information. They can depict what is go oning around us. With orbiters they can calculate more accurately what might go on in the immediate hereafter. Their research has produced grounds of why past climatic alterations took topographic point.
There have been many climate fluctuations ovver th 10,000 old ages since Britain was last covered with an ice sheet. Progresss and retreats of ice in the northern hemisphere during the past 500,000 old ages can be accounted for by alterations in the heat from the Sun.
This was caused by changes in the Earth ‘s orbit at periods of 96,000, 40,000 and 20,000 old ages. Although that theory is widley accepted as a possible accounts for ice ages, it has non been proved. More than 50 theories have been put frontward, but merely a few have non been wholly dismissed.
Not long ago a new theory was published in the scientific discipline diary “ Nature ” . Harmonizing to Dr. Garry Hunt, of University College, intense radiations from the atomic detonation of a nearby supernova – a star – could do the devastation of portion or all of the ozone bed and in this manner trigger an ice age.
As for me, i like Autumn best of all. The yearss become shorter and the darks longer. It is n’t so hot in the day-time.
The trees are covered with xanthous and ruddy foliages. At the terminal of summer apples, pears, plums and other fruit become mature. In the South there are many oranges, Prunus persicas and tangerines. Autumn is plesant when it does non rain. General Autumn is a showery season of the twelvemonth. When it rains the conditions is awful. The sky is covered with heavy clouds. It drizzles. It is boggy and moisture.
The Gravity Model of Trade (see Feenstra, page 192-196) predicts that trade increases with the partner country’s GDP: the bigger your partner country’s GDP, the more you trade with that country. The model also predicts that trade decreases with trade costs, which can be proxied by the distance to the partner country, along with other variables. Specifically, the further away your partner, the less you trade with that country. In this assignment you will test these hypotheses.
Step 1: Collect your data. You will need data on trade flows, GDP, and distance between your country and its 20 largest trading partners. Note that this data is helpfully collected for all trading partners for the years 1948-2019 at CEPII (http://www.cepii.fr/cepii/en/bdd_modele/presentation.asp?id=8 However, the dataset too large to open in excel, thus I would only recommend it for students more comfortable with other statistical tools. All the documentation for the dataset can be found at the CEPII link above. You can also collect your own data from the following sources:
Trade Flows. Use the United Nations Comtrade database to download total imports and exports between your country and all of its trading partners in 2019. Once you have this data, calculate total trade as the sum of exports plus imports associated with each partner. Note that this data is in nominal U.S. dollars (See helpful comtrade hints).
Nominal GDP Data. Use the World Bank’s World Development Indicators (https://databank.worldbank.org/source/world-development-indicators ) to download nominal GDP in US dollars for each partner country in your dataset.
Distance data can be found from CEPII at the website:
http://www.cepii.fr/anglaisgraph/bdd/distances.htm . The file dist_cepii.xls is a spreadsheet file in Microsoft Excel format which contains distances between countries in kilometers.
The country codes are three-letter ISO codes. You can find a list of ISO codes here: https://www.iso.org/obp/ui/#search
Step 2: Choose a sample country to Visualize your data. Make a scatter plot of your data that allows you to visualize the size of the country (GDP) as a determinant of trade. It should have GDP (as a percentage of the GDP of all the partner countries in your dataset) on the horizontal axis and total trade (as a percentage of total trade of all the partner countries in your dataset) on the vertical axis.
Step 3: Estimate the coefficients of the gravity equation: (see attached file)
You should estimate coefficients b and c using multiple regression analysis, which can be done in a spreadsheet package or in a statistical software package. The following website has a nice summary of doing regression analysis in excel for those of you who aren’t familiar with this tool:
Write your report. Start with a general discussion of the gravity model, how you will test it, and what data you are using. Carefully report the results from your scatter plot and interpret the coefficients you estimate. Include your scatterplot and a Table of your coefficient estimates. Conclude your report by analyzing whether the gravity model seems to fit the trade data from your country. You should also turn in the statistical package output or spreadsheet that you used to analyze your results.