undergraduate econometrics syllabus
this video provides an overview of the subject of econometrics at undergraduate level, and surveys the topics which this online course will cover.
also, see a part of the transcript below.
in this video i want to provide a description of the syllabus which we are going to cover in the undergraduate course. so at the back of our minds within the undergraduate course, and actually at the graduate level as well. the idea is that there is some population and within the population there might be countries, there might be individuals, there might be firms. and the idea is that we don't actually have the whole population data set you only have a sample from that population. so perhaps we have the just the figures which i'm highlighting here in purple, and these individuals form a sample data set. and the idea with econometrics is that we want to use some sort of tool, some sort of statistical or mathematical tool on that sample, to enable us to make some inference about what's going on in the population or to make some estimation of some sort of population parameter. so what exactly do i mean by estimating the population parameter? so there might be a relationship between the level of wage an individual obtains and their level of education, which is given by the relationship here which i've drawn mathematically. and the idea here is that the coefficient beta might represent the effect of one-year extra of education on an individual's average level of wages. and that would be defined within our population, and that would be the average effect of one year of education on wages within the population but the idea is that we don't actually have the whole population's data, we only have a sample from that data, so we'd like to use our tool on the sample to enable us to estimate this particular parameter, in this case beta. when we first start off talking about estimation techniques, we're going to first start off discussing cross-sectional data so that might be when we have the level of wages and the level of education for a set of individuals one point in time. and it just so happens that under a certain set of criteria that a tool which we call 'ordinary least-squares' happens to be quite a good tool to use on our sample. so under a certain set of criteria ordinary least-squares happens to be what we call blue, so that means it is the best linear unbiased estimator possible. don't worry if you don't understand what that means, we are going to cover that in due course. the set of criteria which needs to be fulfilled are what we call the gauss-markov assumptions, and assuming that each of these is satisfied then ordinary least-squares are a useful thing to use on our sample. but how do we actually go ahead and test the certain criteria? well we need a set of what we call diagnostic tests, and what these tests enable us to do, is they enable us to test as to whether it is the case that these criteria are satisfied. and if each of these criteria are satisfied that's fine we can still use ols on our sample, and that will enable us to make some sort of good estimate about population parameters. but if these criteria aren't satisfied, so these diagnostic tests show that they aren't satisfied then ordinary least-squares is no longer blue, and in this particular circumstance we need to define a whole set of estimators which may not be blue but may possess some sort of other good property which an estimator might have; in particular consistency and under a set of less restrictive assumptions they may happen to be blue or consistent. so some of the estimators we are going to discuss here are instrumental variables estimation gls estimators and maximum likelihood estimators. we may also talk a little bit about gmm and but we are going to keep that to a minimum. i should also mention that i'm going to try and make this course as non-mathematical as possible. wherever i can avoid using maths i'm going to avoid it, and where i do use maths there is not necessarily the need to follow those videos if you are just looking for the intuition behind the theory. ok, so the second part of the course is going to be concerned with a second type of data which we call time series data. so an example of time series data might be let's say we have a given company's level of sales and now we don't just have it at one point in time we might have it across time. so this might represent the company's sales across time, alternatively you might be looking at the gdp within the uk, we might be looking at inflation within the uk. check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses.
this video provides an overview of the subject of econometrics at undergraduate level, and surveys the topics which this online course will cover. also, see a part of the transcript below.in this video i want to provide a description of the syllabus which we are going to cover in the undergraduate course. so at the back of our minds within the undergraduate course, and actually at the graduate level as well. the idea is that there is some population and within the population there might be countries, there might be individuals, there might be firms. and the idea is that we don't actually have the whole population data set you only have a sample from that population. so perhaps we have the just the figures which i'm highlighting here in purple, and these individuals form a sample data set. and the idea with econometrics is that we want to use some sort of tool, some sort of statistical or mathematical tool on that sample, to enable us to make some inference about what's going on in the population or to make some estimation of some sort of population parameter. so what exactly do i mean by estimating the population parameter? so there might be a relationship between the level of wage an individual obtains and their level of education, which is given by the relationship here which i've drawn mathematically. and the idea here is that the coefficient beta might represent the effect of one-year extra of education on an individual's average level of wages. and that would be defined within our population, and that would be the average effect of one year of education on wages within the population but the idea is that we don't actually have the whole population's data, we only have a sample from that data, so we'd like to use our tool on the sample to enable us to estimate this particular parameter, in this case beta. when we first start off talking about estimation techniques, we're going to first start off discussing cross-sectional data so that might be when we have the level of wages and the level of education for a set of individuals one point in time. and it just so happens that under a certain set of criteria that a tool which we call 'ordinary least-squares' happens to be quite a good tool to use on our sample. so under a certain set of criteria ordinary least-squares happens to be what we call blue, so that means it is the best linear unbiased estimator possible. don't worry if you don't understand what that means, we are going to cover that in due course. the set of criteria which needs to be fulfilled are what we call the gauss-markov assumptions, and assuming that each of these is satisfied then ordinary least-squares are a useful thing to use on our sample. but how do we actually go ahead and test the certain criteria? well we need a set of what we call diagnostic tests, and what these tests enable us to do, is they enable us to test as to whether it is the case that these criteria are satisfied. and if each of these criteria are satisfied that's fine we can still use ols on our sample, and that will enable us to make some sort of good estimate about population parameters. but if these criteria aren't satisfied, so these diagnostic tests show that they aren't satisfied then ordinary least-squares is no longer blue, and in this particular circumstance we need to define a whole set of estimators which may not be blue but may possess some sort of other good property which an estimator might have; in particular consistency and under a set of less restrictive assumptions they may happen to be blue or consistent. so some of the estimators we are going to discuss here are instrumental variables estimation gls estimators and maximum likelihood estimators. we may also talk a little bit about gmm and but we are going to keep that to a minimum. i should also mention that i'm going to try and make this course as non-mathematical as possible. wherever i can avoid using maths i'm going to avoid it, and where i do use maths there is not necessarily the need to follow those videos if you are just looking for the intuition behind the theory. ok, so the second part of the course is going to be concerned with a second type of data which we call time series data. so an example of time series data might be let's say we have a given company's level of sales and now we don't just have it at one point in time we might have it across time. so this might represent the company's sales across time, alternatively you might be looking at the gdp within the uk, we might be looking at inflation within the uk. check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. use of i want you to v 1st form daily use structure for spoken english video 40 use of is to am to are to v 1 advanced english strucrures use of was to were to v 1 form practical english daily use sentences video 42 use of has to have to v 1 advanced english grammar spoken english advance structures use of had to v 1 daily use english conversational english for daily use use of will have to v 1 advanced grammar daily use english sentences achi butt shahid kaka saqi shah in chakwal tapeball cricket tournament achi butt best batting arslan achi butt 50 runs arslan achi butt best batting arslan achi butt 5 sixes in 5 balls arslan achi butt 30 runs in 5 balls undergraduate econometrics syllabus what is econometrics econometrics vs hard science natural experiments in econometrics populations and samples in econometrics estimators the basics estimator properties unbiasedness and consistency unbiasedness vs consistency of estimators an example efficiency of estimators good estimator properties summary lines of best fit in econometrics the mathematics behind drawing a line of best fit least squares estimators as blue deriving least squares estimators part 1 deriving least squares estimators part 2 deriving least squares estimators part 3 deriving least squares estimators part 4 deriving least squares estimators part 5 least squares estimators in summary taking expectations of a random variable moments of a random variable central moments of a random variable kurtosis skewness expectations and variance properties covariance and correlation population vs sample quantities the population regression function problem set 1 estimators introduction gauss markov assumptions part 1 gauss markov assumptions part 2 zero conditional mean of errors gauss markov assumption omitted variable bias example 1 omitted variable bias example 2 omitted variable bias example 3 omitted variable bias proof part 1 omitted variable bias proof part 2 reverse causality part 1 reverse causality part 2 measurement error in independent variable part 1 measurement error in independent variable part 2 functional misspecification 1 functional misspecification 2 linearity in parameters gauss markov random sample summary gauss markov gauss markov explanation of random sampling and serial correlation serial correlation summary serial correlation as a symptom of omitted variable bias serial correlation as a symptom of functional misspecification motion introduction class 9 physics use of i want you to v 1st form daily use structure for spoken english video 40 use of is to am to are to v 1 advanced english strucrures use of was to were to v 1 form practical english daily use sentences video 42 use of has to have to v 1 advanced english grammar spoken english advance structures use of had to v 1 daily use english conversational english for daily use use of will have to v 1 advanced grammar daily use english sentences achi butt shahid kaka saqi shah in chakwal tapeball cricket tournament achi butt best batting arslan achi butt 50 runs arslan achi butt best batting arslan achi butt 5 sixes in 5 balls arslan achi butt 30 runs in 5 balls undergraduate econometrics syllabus what is econometrics econometrics vs hard science natural experiments in econometrics populations and samples in econometrics estimators the basics estimator properties unbiasedness and consistency unbiasedness vs consistency of estimators an example efficiency of estimators good estimator properties summary lines of best fit in econometrics the mathematics behind drawing a line of best fit least squares estimators as blue deriving least squares estimators part 1 deriving least squares estimators part 2 deriving least squares estimators part 3 deriving least squares estimators part 4 deriving least squares estimators part 5 least squares estimators in summary taking expectations of a random variable moments of a random variable central moments of a random variable kurtosis skewness expectations and variance properties covariance and correlation population vs sample quantities the population regression function problem set 1 estimators introduction gauss markov assumptions part 1 gauss markov assumptions part 2 zero conditional mean of errors gauss markov assumption omitted variable bias example 1 omitted variable bias example 2 omitted variable bias example 3 omitted variable bias proof part 1 omitted variable bias proof part 2 reverse causality part 1 reverse causality part 2 measurement error in independent variable part 1 measurement error in independent variable part 2 functional misspecification 1 functional misspecification 2 linearity in parameters gauss markov random sample summary gauss markov gauss markov explanation of random sampling and serial correlation serial correlation summary serial correlation as a symptom of omitted variable bias serial correlation as a symptom of functional misspecification motion introduction class 9 physics use of i want you to v 1st form daily use structure for spoken english video 40 use of is to am to are to v 1 advanced english strucrures use of was to were to v 1 form practical english daily use sentences video 42 use of has to have to v 1 advanced english grammar spoken english advance structures use of had to v 1 daily use english conversational english for daily use use of will have to v 1 advanced grammar daily use english sentences achi butt shahid kaka saqi shah in chakwal tapeball cricket tournament achi butt best batting arslan achi butt 50 runs arslan achi butt best batting arslan achi butt 5 sixes in 5 balls arslan achi butt 30 runs in 5 balls undergraduate econometrics syllabus what is econometrics econometrics vs hard science natural experiments in econometrics populations and samples in econometrics estimators the basics estimator properties unbiasedness and consistency unbiasedness vs consistency of estimators an example efficiency of estimators good estimator properties summary lines of best fit in econometrics the mathematics behind drawing a line of best fit least squares estimators as blue deriving least squares estimators part 1 deriving least squares estimators part 2 deriving least squares estimators part 3 deriving least squares estimators part 4 deriving least squares estimators part 5 least squares estimators in summary taking expectations of a random variable moments of a random variable central moments of a random variable kurtosis skewness expectations and variance properties covariance and correlation population vs sample quantities the population regression function problem set 1 estimators introduction gauss markov assumptions part 1 gauss markov assumptions part 2 zero conditional mean of errors gauss markov assumption omitted variable bias example 1 omitted variable bias example 2 omitted variable bias example 3 omitted variable bias proof part 1 omitted variable bias proof part 2 reverse causality part 1 reverse causality part 2 measurement error in independent variable part 1 measurement error in independent variable part 2 functional misspecification 1 functional misspecification 2 linearity in parameters gauss markov random sample summary gauss markov gauss markov explanation of random sampling and serial correlation serial correlation summary serial correlation as a symptom of omitted variable bias serial correlation as a symptom of functional misspecification motion introduction class 9 physics this video provides an overview of the subject of econometrics at undergraduate level, and surveys the topics which this online course will cover.
also, see a part of the transcript below.
in this video i want to provide a description of the syllabus which we are going to cover in the undergraduate course. so at the back of our minds within the undergraduate course, and actually at the graduate level as well. the idea is that there is some population and within the population there might be countries, there might be individuals, there might be firms. and the idea is that we don't actually have the whole population data set you only have a sample from that population. so perhaps we have the just the figures which i'm highlighting here in purple, and these individuals form a sample data set. and the idea with econometrics is that we want to use some sort of tool, some sort of statistical or mathematical tool on that sample, to enable us to make some inference about what's going on in the population or to make some estimation of some sort of population parameter. so what exactly do i mean by estimating the population parameter? so there might be a relationship between the level of wage an individual obtains and their level of education, which is given by the relationship here which i've drawn mathematically. and the idea here is that the coefficient beta might represent the effect of one-year extra of education on an individual's average level of wages. and that would be defined within our population, and that would be the average effect of one year of education on wages within the population but the idea is that we don't actually have the whole population's data, we only have a sample from that data, so we'd like to use our tool on the sample to enable us to estimate this particular parameter, in this case beta. when we first start off talking about estimation techniques, we're going to first start off discussing cross-sectional data so that might be when we have the level of wages and the level of education for a set of individuals one point in time. and it just so happens that under a certain set of criteria that a tool which we call 'ordinary least-squares' happens to be quite a good tool to use on our sample. so under a certain set of criteria ordinary least-squares happens to be what we call blue, so that means it is the best linear unbiased estimator possible. don't worry if you don't understand what that means, we are going to cover that in due course. the set of criteria which needs to be fulfilled are what we call the gauss-markov assumptions, and assuming that each of these is satisfied then ordinary least-squares are a useful thing to use on our sample. but how do we actually go ahead and test the certain criteria? well we need a set of what we call diagnostic tests, and what these tests enable us to do, is they enable us to test as to whether it is the case that these criteria are satisfied. and if each of these criteria are satisfied that's fine we can still use ols on our sample, and that will enable us to make some sort of good estimate about population parameters. but if these criteria aren't satisfied, so these diagnostic tests show that they aren't satisfied then ordinary least-squares is no longer blue, and in this particular circumstance we need to define a whole set of estimators which may not be blue but may possess some sort of other good property which an estimator might have; in particular consistency and under a set of less restrictive assumptions they may happen to be blue or consistent. so some of the estimators we are going to discuss here are instrumental variables estimation gls estimators and maximum likelihood estimators. we may also talk a little bit about gmm and but we are going to keep that to a minimum. i should also mention that i'm going to try and make this course as non-mathematical as possible. wherever i can avoid using maths i'm going to avoid it, and where i do use maths there is not necessarily the need to follow those videos if you are just looking for the intuition behind the theory. ok, so the second part of the course is going to be concerned with a second type of data which we call time series data. so an example of time series data might be let's say we have a given company's level of sales and now we don't just have it at one point in time we might have it across time. so this might represent the company's sales across time, alternatively you might be looking at the gdp within the uk, we might be looking at inflation within the uk. check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses.