Yksi asia on, että nämä asioita voivat olla testovuotoja. Tällä kertaa, että esimerkiksi asioita voivat olla erittäin tärkeää asioita. Katsotaan, mitä testovuotojen asioita on tärkeää ja mitä kokemukset voivat olla testovuotojen asioita, että se on either reject the model, or fails to reject the model. Asioita testovuotojen asioita on, että kun on yksi asioita, ja model with one that more than zero of x is for freedom, then that model implies constraints on your covariances. For example, in this example from previous video, I calculated an over identification test. So we have six variances and covariances, we have five free parameters. So they imply a constraint that covariances between x and y divided by covariances between x and m must equal covariances between m and y divided by variance of m. And how we construct a test is that we take this difference here and we compare the difference to zero. If it's slightly greater than zero, then we conclude that that can't be attributed to chance only and the model should be rejected. It's not an adequate representation of our data. In practice, we use the Kaisquart test. Let's go through five examples on model testing to understand what model tests, how they work and what they tell us. So our example data is here, we have thousand observations. The number of observations doesn't really matter, but I generate the 1,000 just to have sufficient statistical power to clearly reject incorrect models. We have three variables, x, y and z, that are roughly have the same variances. They are correlated to the same decrease, so this is the covariance matrix. And let's first estimate the recursion model here. So the recursion model is saturated, means that the decrease in freedom are zero, nothing is being tested. So we have six unique units of information from the data, three variances, three covariances and we are estimating six things. We estimate three, two variances, one error variance and one covariance and two recursion paths. So nothing is being tested. This is saturated model. The fact that the chi-square test does not reject this model, does not mean that this model is correct for the data, because we could equally swap y and x and have x as the dependent variable y as the predictor and the model would still fit perfectly because it's saturated model, zero decrease of freedom. It cannot be tested. Now let's take a look at the model that actually can be tested. So if we remove this exogenous covariance here, we gain one degree of freedom. So every time you take a parameter out from the model, you gain decrease in freedom and that has a testable implication. This model is rejected. It has one degree of freedom, it is over identified, but we need to understand what is being tested. So can we, based on this model, claim that x and z cannot be causes of y because chi-square test reject this model? We cannot. When we test the model, we need to understand what are the constraints that are being tested. So this model allows the free estimation of x and y relationship, z and y relationship, but it constrains the z and x relationship to be zero. So the only constraint that this model implies is that x and z are uncorrelated and if we can take a look at these sample covariances, model implied covariances, calculate using the tracing rules and residual covariances, which are the difference between the implied and the sample covariances, we can see that the correlation between x and z is not well explained by the model. Then when a correlation is not, or covariances is not well explained by the model, we need to look at, okay, so what covariance does the model imply? It implies zero covariance. Why is there zero covariance? Well, there's zero covariance because we constrain it to be so. So this is what is being tested, is whether x and z are uncorrelated and that has no link into the claim that x and z would not be or would be causes of y. So it's completely uninformative of the claim that these are causes of y. Unfortunately, these kind of models are sometimes appearing published research or papers that I get to review. The reason is that some SCM software require that you manually add these correlations. So it's possible that the researcher draws a rigorous model and forgets to add these covariance. Sometimes when we see path diagrams in published papers, these covariance with exogenous variables are not included in the model diagram, even though from the decrease of freedom you can see that they were actually estimated from the data or they were treated as fixed in case, in which case they are not tested at all. Another scenario where this can occur that these x's are correlated to be unconstrained is that you have exogenous variables that are observed like this case. We have x and z, we have actual scores and then you have latent variables that are unobserved and are measured with indicators. I'll talk about those in a separate set of videos. In some statistical software those two sets of variables are constrained to be uncorrelated by default and then you would see that all these observed variables are uncorrelated with all the latent variables and what is being tested is not whether there's a causal relationship from the x variables to the y, but whether those two sets of variables are mutually uncorrelated. I have never seen a paper that actually justifies that kind of constraint so whenever you see a constraint between exogenous variables that is quite likely to be just the data analysis mistake. Let's take a look at the next model. What if we reverse the causality? We say that y is a cause of x and z and not the other way around. Well, this is also a saturated model. Nothing else being tested and it fits the data perfectly. If we remove this covariance here, then we gain one degree of freedom. So we have one degree of freedom here and what is actually being tested. It's also now important to understand. So one could make a claim that because this model does not fit the data, then y cannot be a cause of x or cannot be a cause of z. Well, that claim is not valid. The reason why it's not valid is that the only thing here that is being tested is does the model explain the correlation between x and z well? We can see from the residuals that that covariance is not well explained by the model. So what are we testing is does the model explain that covariance well and nothing else? Then we need to consider so what is the implied correlation and what implies correlation between x and z and it is the common cause y. So with this kind of model configuration what you would be testing is whether the correlation between x and z can be fully attributed to the common cause y. These models again are something that a researcher might estimate accidentally. There is hardly ever reason to constrain these to be uncorrelated but in some statistical software that do SCM you will need to add that correlation manual because it's not added by default. So it's very important to understand what exactly is being estimated and what exactly is being tested before you make any grand claims about what the test result implies. Let's take a look at the final example, model 5. So model 5 is a bit more complicated and this is actually a model that is very common in the literature and it's actually used for two different purposes. It is a full media model so we say that z influences x, x influences y and it's rejected by the chi-square so we know that the full media SM model is not adequate representation of the data. The next thing we need to again understand what part of the data the model doesn't explain and we can see that it is the correlation between z and y and what is being tested. It's actually what is being tested is whether it's not, whether this is a partial media SM model. So many researchers would argue that because the full media SM model is rejected, therefore the data must follow a partial media SM model. Another group of researchers might argue that this is actually an endogeneity test. So z is an instrumental variable, x is a potential endogenous variable and this is a test of whether the error term of x correlates with error term of y. So that's an endogeneity test and this is actually a fairly common endogeneity test. Or we could just argue that this is a test whether z and the error term of y are correlated like so or we could argue that there's a feedback loop between y and z. There are like tens of different models that you can construct using just three variables. So we cannot use the rejection of full media SM model as a criterion for acceptance of another model because there are multiple different models that would explain the data well. So what exactly is being tested here is whether the full media SM model fully explains the covariance between z and y and nothing else. If that full media SM model is rejected and if we consider alternative models then we need to consider based on theory what is the most plausible alternative model. Is it more plausible that there is a full mediation or is it more plausible that x and y actually have a common cause that is not included in our model or some other configuration. Then we go based on that. So we should not automatically say that because the full media SM model is rejected therefore partial media SM model should hold for the data. Let's summarize these five models that we tested. So we have the regressor model zero decrease of freedom cannot be tested. We have the regressor model where the x variables uncorrelated that can be tested. We have the common cause model where x and z are correlated. We have common cause with uncorrelated errors and then we have the full media SM model. So we can see that based on this summary we can clearly see that testing a model does not tell us anything about directionality because comparing the first model against the third model where we just reverse the paths and it doesn't tell us anything. Both fit the data perfectly. Some people, some researchers are tempted to conclude that because we could actually compare model two, model four and model five because they all have one degree of freedom and then conclude that the full media SM model here is much more plausible for the data than this regressor model. That is an invalid conclusion. This regressor model only tests whether these x and z are uncorrelated. It does not test whether x affects y. X to y is freely estimated. So you are actually testing of whether you are omitting something in the model. You are not testing whether there is something in the model that shouldn't be there. So we are testing constraints, we are not testing for unnecessary things and this is important to understand. So to summarize these tests tell us whether the current models explain the covariances and really not much more and then you need to have a really strong theoretical reasoning to argue that another model will be better. They are important to understand which covariances are not explained. So you need to look at the residuals from the model. So if the model fails, it means some of the covariances are not well explained by the model, you need to understand which ones before you make any claims about the model itself. Then we have these don't tell us anything about the specific alternative model. So model tests are only about the model at hand and they cannot tell us anything about any potential alternative model. So the fact that full media SM model is rejected does not imply that partial media SM model should be accepted. And finally they tell us nothing about the direction of effects. So direction on effect of effects can only be influenced or assessed by having a research design that allows you to measure effects over time just having cross-sectional data set where you estimate the effect in different ways will not do that for you.


So now, if you look at this title, it's on incomes, inequality and poverty. Actually, just by chance, usually we think of poverty as being explained by inequality and income. And Eric and Ali, in the early days of the late 90s, they actually analyzed the correlation between poverty and inequality in Africa. And in that relationship, actually, inequality and poverty are strongly, positively correlated. So actually, if inequality is rising, you can expect that poverty is also rising. And even incomes are growing. Also, poverty is actually going down. I also can't help noticing that poverty also affects incomes and inequality. So actually, analyzing that relationship is quite a complex task. But this is not what we are going to do. This is not what we are doing here. What we are doing here is we are taking income, inequality and poverty as in different aspects of well-being. And we are trying to see how they have evolved over time. Actually, over a long time, over a period of about 100 years, and this 100 years, actually from about 1900 to 2012. That's a period that we are looking at. And this period is long enough we chose it that way to permit identification of persistent determinants of poverty. And also, to also try and see whether you can identify potential sources of structural change, which actually drive both incomes inequality and also poverty. And in the analysis, we focus much more on the recent period for which we have more comprehensive data. And just some advertisement down here. We depart from previous literature in two respects. One, we actually use several data sets rather than just one data set. And we use several methods to compute poverty line, which is key actually in estimation of poverty. We use the utility consistent method that we actually taught by UNWinder. There was a conference for us to learn that method. But we recognize that money metric is not enough to measure poverty. So we also use money metrics. And the motivation, as I will point out later, is actually the market failure argument. If the markets are working, the only thing that we need is income. So just beginning our long-term analysis. Now there were dramatic changes in Kenya during the 20th century, from the 1900s as I said, to 2012. And during that time, there were shifts in the structure of output. So the share of agriculture moved from around 75%. The share of agriculture in GDP moved from 75% around 1900 to 25% basically. And so there was actually, there's a structural transformation in the economy over this period, which still continues to drive incomes, income distribution, and also poverty. And later on, I will say more about structural change. Now, so we want to focus on this period, okay, 1914 and those 15 years. This is actually when the British began to exercise their control of the Kenyan economy. And during that period, the Kenyan inland was not integrated with the outside world. And actually even the groups in Kenya also themselves at that time were not integrated. But one thing which we noticed is that the standard of living throughout the country in various regions were comparable. Very little differences between, across regions. And actually the equalizing factor was the abundance of land. Land was abundant, so everyone actually would get something, get a decent livelihood as of that time. But actually land scarcity now is a dividing factor. So you can see one factor now can play different roles over different time periods. Then another event, which happened during this time, was the completion of the railway from Mombasa to Kisumu and Uganda. And there's also a major impact in opening up the countryside for settlements, both by the natives who were there at the time, the colonialists who came, and people who migrated to come and look for new opportunities there. And this is the next period, this is still the same period. As the inland continued to be such a long-run form of sector employment emerged, and a class also of traders also emerged. So there were an answer there and also business people, and then began production of cash crops. And so actually the economy began to be modernized. And at that time some inequality now began to emerge in a number which were not there before. So actually this period was as small as the origin of the inequalities that we see today. And actually this is the point that we are making in this slide. Then there's this interwar period. So this is the interwar period. The main thing to notice here is that the African farmers began expanding their production also to take opportunity of export markets. Now this is the period just up to the period of independence in 1963, this one we got independence. So during this period there was extensive winch employment in the country, but actually it was of a temporary nature. People would come to the urban areas, work for a short time, and then go back because of the restrictions. And there was also the beginning of rural urban migration, which is also a source of structural change, creating distinct rural and urban sectors in the economy. So this was the main events there. And during this period, this is now the first independence period, 1960 to 1976. Now during this period there was the change in the interrelation distribution of both power and incomes. So actually the incomes and assets were in the hands of the colonial setbacks. Some of those assets passed into the hands of the natives. And that also started to increase the inequality. But what happened actually during this period was a huge increase in winch employment, especially in the public sector, and the winch increased by 48% compared to the 6% increase in the same winch in the private sector. And here, during this period, a group of co-poor people, the people who were extremely poor also managed, and that is still a problem today. And these people actually were characterized by having very little land to cultivate on, and also very low earnings from their off-farm employment. Many of them were also under arrest. Here, this shows that this is around 1914, this is up to 1976. Sorry, there is some information missing here. This is the trend in sector income share. So this is the share of winch in total incomes. As you can see, this is more or less a term of independence. Roads are then declined. And then this is now the share of agricultural sector in total GDP, total income actually declined substantially. And this is the share of profits, also rose and then declined. These trends have implications for poverty and income distribution. And this is the share of income by race. This is the share of total income by the African population. Actually, you can see it's the highest because they were the majority also. That's the reason. And this other group here, this is the European, not very different from the Asian. So, in terms of the evolution of poverty and income from 1900 to 1976, what we find is inequality actually increased until the 1950s, then fluctuated. And we see it from here, this is gaining coefficient. This is for everyone, this one here, actually increased. Then it kind of fell so still high that the inequality. And then this is now for the inequality in the modern sector, it was the highest, simply because of the way we mentioned it using income from the former sector. And the inequality among the Europeans was actually the lowest, but also actually for some reason it fell after independence. Around this period you can see it fell from here. And this is an important graph for us. This is the evolution of poverty over this period from 1900 to 1976. There was no FGT at this time, actually. The FGT method discovered by Eric cannot discover this method at the time, but of course there was the anti-country issue which was there. So this is the same index which is modified by the inequality among the poor. So the point to note here is that actually poverty has been quite a problem in the country for quite some time. And then after this it stabilized, 1976. If we can use the data sets for 1982, 1980s, 1990s and so on, this would just be around there. So actually around the forties there. But it has come down, but it's not as serious as it was before. Right now we think we have a lot of poverty, but poverty used to be much a bigger problem if you say that now. And this is a more recent period and we are trying to see things that might actually be in driving the incomes that we measure. Now this is one factor in driving that income. This is the capital rubber ratio. Capital rubber ratio is this one, which is rose and then declined. And this one here is, yes, this is the land delivery issue. Land per person declining very sharply actually. Okay, and this is also one factor explaining poverty. And this other one is the capital rubber ratio, the capital land ratio actually. So capital has been accumulating faster than land. Of course land doesn't grow, but capital has been growing, so that's why you have that relationship there. But because of this pattern here, this one here, okay, you can see the return to land is actually very large. Okay, so now other people actually, land is an issue in Kenya now because I've become very short and very scarce, and there is a high return on land. Then for GDP for a more recent period, here this line here is the rubber land ratio. Person per land actually, this has been rising. But you see the slope here is very slow, but actually if you look at the over the wrong period in the other picture, because I don't have much time, I won't go back. Actually the problem is more serious than we see. And this is the labor income shares. These have been fairly constant actually, the share between labor and the other factors of production. Okay, not changing much over this recent period. And so we have monetary measures of poverty, which we look at and these are insufficient health, poor health, nutrition, it is just, actually gave us an idea about this, insecurity, low self-esteem and powerlessness. These are the measures of well-being. What we notice is that all the status in Kenya has been improving. If we look at, if we use mortality, but if we use life expectancy, it's actually uneven. Okay, but this one is fast-moving indicator, this is slow-moving indicator. And so we want to say something about policy with the results that we have. So in Kenya, the aim of our government now is to reduce policy to reduce poverty. Okay, so we actually, and this policy will succeed if certain things happen, if certain objectives are achieved. It's even growth actually occurs. Okay, but growth in itself will not actually reduce poverty. It needs to be, the process needs to be inclusive. And it showed us an example where Kenya actually reasonably well, but poverty did not change much, did not fall much. And we also need to change our political governance systems and reduce poverty and increase efficiency in the use of the resources that we have. The other thing that comes out from our long-term analysis is regional inequality. If we reduce inequality across regions, actually we will reduce overall inequality. Okay, so if we can focus on this thing here, these two are very closely linked. And the issue now is how to transform, to do this we need to transform our economy. And this is why we ask actually what do we mean by this structural change that we want. And I'm trying to think about it that in my mind as to what structural change is, keeps on changing. So what are the measures of structural change? Factor proportions, right? Capital labor issue. Okay, if that we see that changing, then there's a structural change. And also the other thing is the change in the age composition of the population. Okay, now what might be the changes which what factors might cause factor proportions change? Technical knowledge. Okay, norms, actually for me, these technical knowledge, norms and institutions and the mechanisms for social sharing, these are the issues, these are the structural changes. Okay, and if we can figure out how to change the norms, how to increase our technical knowledge, how to change our sharing, because this will affect inequality, then we can actually change both the inequality and poverty the way we want. Okay, so my time is up. Thank you very much. Thank you very much.


presented by your local Ford stores and Meredith the trip to a National League Park again Yankees getting away from Yankee stadium for a bit. What does that do to the lineup for the Yankees tonight? Well Bob as we know the Yankees are losing their D age for the next three days and it really beg the question what does that mean for John Carlo Stanton as far as tonight is concerned. Stanton is not in the lineup instead Aaron Boone opting to go with Gallo Gardner in judge from left to right. I asked Boone what his thought process was in leaving Stanton out of the starting lineup tonight. Just looking at two of the three games that you know I'll try and play here and just kind of weighing out the matchups just kind of makes makes sense personnel wise to go with them on looking at going with them Saturday and Sunday. How difficult a decision is that to have not only John Carlo Stanton but Luke Boyd because it's a National League Park out of the lineup knowing what type of power they potentially possess. Yeah I mean especially now is you know we're much more whole and healthy and have all our guys you know you hate not having that D age there but you know that's part of it. We got two big big bats over there that we can fire in different situations hopefully in a big spot tonight that you know to help us.


What is going on guys, welcome back to the channel critical overload here to talk about screen six in this video here again today Screen six had another block of filming that took place yesterday It's of course having another block of filming happening today right now as we speak I guess while I'm recording this now Shout out to Craven something scary really quick because he did a video that went into a image that was shared with him from An individual named Chris who also happened to share the image with me as well now this image you see here on your screen Rumored to be the home of gale weathers. I guess in screen six. It's not confirmed It might not be so if it's actually not don't be surprised, but it could be so The stuff outside of the house you'd like the jack-o-lantern and you know just all of that stuff Has people thinking that okay? We're gonna be set in halloween have a halloween theme based screen movie now I'm not against that the only thing I'm now thinking about is okay Well how depending on certain shots couldn't certain things reveal that we're not in the fall Which could then of course make some people say okay? I'm not completely sold on the fact that this is set in halloween, but you know Some of us like me will still suspend our disbelief But there are going to be certain aspects of it depending on let's say it actually is set in halloween The depending on certain shots they decide to do or not do it could give away that we're not set in the fall It's just something to consider It's nothing wrong with it being set in the fall But certain things are about the scenery and with the time of year you shooting could reveal that you're not set in the fall I'm sure they could find a way to make it work, of course, but like the halloween movies do of course But the other thing I want to talk about with a screen-based movie set in the fall Or set around halloween time because Craven didn't mention this stuff as well And it's completely true you could have ghost face feeling a lot more bold Getting to walk around in the open because you could have a lot of people that are wandering around dressed as ghost face from the stabs series And you could have an actual ghost face killer who's not trying to walk around Being a costume to go trick-or-treating they're looking for victims you could have a scene That's kind of mirroring what we got in Halloween 2018 when Michael Myers came home to Haddon Field and you know Got to wander around because it was Halloween night and you know no one's gonna think anything of it So you could have something like that go down in screen six Maybe Gail gets a knock on her door from all this would actually be kind of maybe something that triggers something emotional her Let's say and I don't know why this would happen But maybe just some people who are fans of the stab franchise and know that Gail Weathers lives there Let's say three friends show up at her house dressed as her Get dressed as her Sydney and do it and then of course seeing someone dressed as Dewey could cause an emotional aspect of her to be Unleashed during that moment and she might lash out at the trick-or-treating I'm just thinking about something that they could do that would probably Be a cool thing to see on screen.  It's a prank. So Ghostface knocks on her door Kicks in the door or pushes her in locks the door behind them And then Gail has a chase scene or some type of horrific sequence go down in her house She doesn't have to die here, but you know, you could have something iconic go down like that if this is her house There's a lot of things that you can get away with you could have people walking around dressed as other characters West Craven is responsible for like Freddy Krueger You can have other horror icons wandering the streets or people wandering the streets and costumes of other horror icons There's so much you can do with a screen movie set in Halloween time The only thing again, I'm thinking about is if they can actually if they might lose some people on Selling them that this is set in Halloween time because of certain things with the scenery that could give away Okay, we're not setting the fall but people like me and many others who are gonna still suspend a disbelief will be all on board for something like that and You know, we could also have I'm now thinking if you have the movie set on Halloween What the whole movie be set on Halloween night or would only a certain part of it be set on Halloween night Because the other thing people were talking about like I mentioned in another video Was that maybe this is set in winter time because someone apparently was seen on set wearing some type of Christmas ornament It doesn't mean we're gonna be set in Christmas And the only reason I'm saying that is the biggest reminder of we don't need to be set around Christmas People wear Christmas stuff is Karen from Halloween 2018 and Halloween kills with her choice of attire She chose to wear so, you know, there's so many different things You could do if the movie is indeed set on Halloween night or if it's set in winter ghost face attack in the snow You know, so many different things. I know people would love to see Like some fresh fresh things you could do in this in the screen universe that have not been done before Seeing radio silence take some risk and throw their own unique stuff into this series and Get people excited about a screen movie because they see some things Maybe highlighted in a trailer or teaser that we get that shows that this is gonna be unlike anything We've ever gotten before in the screen series. So while you may have lost Neve Campbell, you know Certain things like that could easily reignite interest because people will see. Oh, they're they're doing a lot of different stuff here Okay, I guess I'll go give it a check. I'll go give it a shot. So We'll see what ultimately ends up happening if that's Gale's house We'll see if that ends up being true or not as far as again Gale herself I do expect her to have a big role going into this just going off the basis of earlier things that we know Saw online what IMDB not that it was ever verified about her getting involved after a past survivors killed Not again, not that that's verified and you know being set in New York We know she lives in New York She has a show that she's a part of in New York And it seems like with this whole New York thing the ghostface motive this time around will have something to do with Gale It's something that I've been thinking about ever since I saw that there was a chance that Gale Could be the focus of this story not that she'll be more of a focus than the carpenters But when we're not dealing with the carpenters It seems like Gale is going to have a very heavy Role to play in this movie compared to any other movie she starred in from the past screen movies Similar how do we had a pretty hefty role of it of significance? I'll say in screen 5 It seems like we'll get something like that with Gale in screen 6 and then we'll get something like that Hopefully with Sydney in screen 7 Doesn't mean that either of them have to die and I'm not saying that But I'm just thinking about all the things that Gale could do to kind of show everyone that's doubting her that she's just as much of an Important final girl to this series as Sydney Prescott is because again like I said in my last video There's a lot of people that are doubting Gale, so I can't wait to see her Hopefully silence a lot of her doubters But let me know what y'all think about all this down in the comment section below Do you think that we'll be setting Halloween time? Would you even like to see that if you have an array? Of course make sure you go ahead and subscribe Turn on post the vacation that you never miss a video in the description out of links and social media accounts on Facebook Twitter and Instagram You can message me there of course limit There's any movies news or reviews let me to cover in the future with all that in mind guys I will see you in the next video