![]() They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Investors based in different locations allocate their savings across destination countries via a rational inattention logit demand system, for which we find a. n basically indexes observations (rows): n 1 is the first row, n 2 is the second, and so on. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. Stata has two system variables that always exist as long as data is loaded, n and N. ![]() The project leading to this publication has received funding from the french government under the “France 2030” investment plan managed by the French National Research Agency (reference: ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University – A*MIDEX. It has benefited from support from the World Bank’s Umbrella Facility for Trade trust fund financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. df1 <- ame (province c (1, 1, 1, 2, 2, 2), house c (1, 2, 3, 4, 5, 6), lat c (-76.6, -76.5, -76.4, -75.4, -80.9, -85.7), lon c (39.2, 39.1, 39.3, 60.8, 53.3, 40.2)) Using the geosphere library I can find the distance between two houses. This paper is part of World Bank’s ongoing work on Deep Trade Agreements and a background paper for the flagship report on Global Public Procurement and Development Impact. COMPUTE GEODIST FOR EACH ROW STATA SERIESWe are also grateful to Mario Larch and Federico Trionfetti for insightful exchanges and suggestions, and to Serena Cocciolo, Julien Gourdon, Asif Islam, Bill Maloney, Gustavo Piga, and participants to the World Bank Virtual Seminar Series on Deep Trade Agreements for useful comments. COMPUTE GEODIST FOR EACH ROW STATA CODEBelow is a simplified version of the code that will yield the exact same results as above.We are grateful to the editor and two anonymous referees for comments that enabled us to improve this article substantially. Further in the latest versions of Stata we can combine sort and by into a single statement. We can make use of the “*” wildcard to indicates that we wish to use all the variables. If you have a lot of variables in the dataset, it could take a long time to type them all out twice. Finally, we list the observations for which _N is greater than 1, thereby identifying the duplicate observations. Then we use all of the variable in the by statement and set set n equal to the total number of observations that are identical. In this example we sort the observations by all of the variables. Now let’s use _N to find duplicate observations.īy id score x1 x2 y1 y2 z1 z2: generate n = _N Let’s use _n to find out if there are duplicate id numbers in the following data:Īs it turns out, observations 6 and 7 have the same id numbers and but different score values. To list the highest score for each group use the following: To list the lowest score for each group use the following: Now n1 is the observation number within each group and n2 is the total number of observations for each group. Of course, to use the by command we must first sort our data on the by variable. For a given year, for each address, I would like to calculate the sum distance between that address to other addresses but conditioning they are in the same devision (for example with id000361105, calculate distances from id000361105 to id001547108, from id000361105 to id002896207, from id000361105 to id013078100, since these ids are. Using _n and _N in conjunction with the by command can produce some very useful results. Let’s see how _n and _N work.Īs you can see, the variable id contains observation number running from 1 to 7 and nt is the total number of observations, which is 7. _N is Stata notation for the total number of observations. _n is 1 in the first observation, 2 in the second, 3 in the third, and so on. The horizontal axis for each graph is in the units of time that your VAR is estimated in, in this case quarters hence, the impulseresponse graph shows the effect of a shock over a 20-quarter period. ![]() _n is Stata notation for the current observation number. The impulseresponse graph places one impulse in each row and one response variable in each column. Stata has two built-in variables called _n and _N. ![]()
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