Following my post on academic journals, Miss Lambda asked me about journals in a very specific area, namely agricultural, environmental and energy economics. I found it interesting since I do not have any idea about journal that can be in that domain of research. So I looked at some journals form the French CNRS list (online here). Hence, I have been looking for words in the title of 26,000 articles, published in 29 journals, in Agricultural, Environmental and Energy Economics. I considered American Journal of Agricultural Economics (AJEE), Ecological Economics (EE), Journal of Environmental and Economic Management (JEEM), Climate Policy (CP), Energy Economics (EE), Energy Journal (EJ), Energy Policy (EP), Environment and Planning A, B, C, D (EP-A, B, C, D), Environmental and Resource Economics (ERE), Environmental Modeling and Assessment (EMA), European Review of Agricultural Economics (ERAE), Resource and Energy Economics (REE), Agricultural Economics (AE), AMBIO: A Journal of the Human Environment (AMBIO), Australian Journal of Agricultural and Resource Economics (AJARE), Canadian Journal of Agricultural Economics (CJAE), Climatic Change (CC), Ecological Modeling (EM), Energy Studies Review (ESR), Environment and Development Economics (EDE), Environmental Science and Policy (ESP), Environmental Values (EV), Food Policy (FP),
Global Environmental Change (GEC), Journal of Agricultural Economics (JAE), Society and Natural Resources (SNR), Water Resources Research (WR).
Now if we look at the principal component analysis, and projection of the journals on the first two axis, we have

On that graph, it is hard to say anything… The only thing is see is that on the lower part, we have journals focusing on modeling issues.
The top of the most common words in those journals is the following,
> colnames(MATRICE[,1:32]) [1] "climate" "analysis" "environmental" "change" [5] "energy" "model" "policy" "case" [9] "water" "economic" "management" "food" [13] "study" "development" "market" "agricultural" [17] "approach" "effects" "carbon" "global" [21] "impact" "production" "land" "china" [25] "assessment" "forest" "impacts" "urban" [29] "demand" "spatial" "emissions" "modeling"
If we look at their projections on the first two axis, we have (it looks like the second axis has been inverted here)

i=or if we focus on the top 30
Now, if we look at clusters, and use a hierarchical model, we obtain

So here, a dozen journals are extremely close. They are more focusing on agricultural issues. Note that Energy Economics and Energy Policy are in the same cluster, but quite far away from Energy Journal (which looks strange). We can also observe that Climatic Change alone, far away from all the other journals (actually, it is a journal were I just got a paper accepted… and since my areas of research are quite far away from agricultural economics, I can understand that).

. On verra la semaine prochaine que cette information peut s’écrire (moyennant quelques conditions de régularité)
avec

, i.e. dans le cas d’échantillons de 20 tirages de pile/face (avec une pièce non truquée)

correspondant à une collection de variables aléatoires
, i.i.d. de loi
, et de densité/loi de probabilité
.
observations apporte deux fois plus d’information qu’en avoir seulement
. On a alors quelque chose qui pourrait s’écrire
et non pas
, l’information est moins bonne, i.e.
de 




















, the year (of the marriage), and on column
, the age of the man when he gets married. Assume that those were rawdata, i.e. that we have the number of marriages of men of age
, we want to estimate (or predict) the age he will get married, if he gets married. With raw data, we can do it… The first step is to build up triangles (to have a cohort vs. age lecture of the data), and then to consider a model, e.g.
is a year effect, and
is a cohort effect.
and
, where now
denotes the cohort.
is the following





, but unfortunately, I do not think any interpretation is valid (unless demography did not change last century). For instance, the following sum





is the maximum of n random variables i.i.d. uniformly distributed on the unit interval
. I gave a hint last week about the cumulative distribution function for the maximum, i.e.
,



is a random variable with finite variance, then







as
. Here, it is then possible to get
, then
(see the prof of the central limit theorem we got a few days ago).
is the cumulative distribution of the
‘s (the random variables used to build up the maximum). This work since the 











, où
et
. On peut aussi l’écrire non pas sur la moyenne empirique, mais sur la somme (c’est cette forme que l’on a démontrée),
, alors
. Et de plus, si
, alors
. Bref, en bricolant un peu, on peut en déduire une approximation de la forme suivante
suffisamment grand. Empiriquement, si on s’amuse à sommer des variables de Bernoulli indépendantes (car on connaît très exactement la loi de la somme) on obtient, pour la fonction de probabilité



As people say in Montréal, “aujourd’hui, il fait frette”. And I have been surprised recently when some people told my that we would reach -35°C Sunday evening… I checked around, and I found -25°C on all weather forecast websites. But nowhere -35°C. I asked some friends, and they told me that those people were not really looking at the air temperature (as we observe on the thermometer), but they were looking at the wind chill, also called “felt air temperature on exposed skin due to the wind” (température ressentie).
defined as
is the air temperature (in °C), and
the wind speed (in km/h). Please don’t ask me how to interpret this power 0.16 (I already find difficult to explain a square root in an econometric equation). If we look at the past previous days we observe the following observations,
where points on top are temperature, while below we have felt temperature.So, basically, winters are even colder than what you might think..
, defined here as
denotes a dewpoint (see
Recently, @

.
i.i.d. On va faire une hypothèse forte, à savoir que leur loi est une loi normale
, où
est inconnu.


est la densité de cette variable. Or on sait que
suit une loi du chi-deux à
degrés de liberté, dont la densité est la loi gamma suivante







i.i.d. with distribution
. Here we note
is a random variable. The idea is to assume that 
. Thus, we need to compute the distribution of
which is here extremely simple (due to properties of the Gaussian distribution), i.e.

as an estimator of given our sample data (and thus, we also have a confidence interval since we know the distribution of
. Note, first, that \theta has support
. So we need a distribution on that support. Why not a beta distribution ? E.g.











