this video provides an introduction to the subject of econometrics, using a few examples to explain the sorts of question which are likely to be encountered.

in this video i want to talk about what actually do we mean by econometrics. so econometrics is in general a statistical tool set which helps us to evaluate some sort of relationship of interest. an example might be we're interested in, for individuals, what is the effect of an individual's level of education on the average wage which that individual might expect to obtain. so if an individual's level of education increases we might expect that the level of wages which an individual obtains on average might increase. so if i was to plot a graph of the level of education of individuals on the x-axis, against the wages which a group of individuals have obtained on the y-axis, then we might hope to see some sort of positive correlation between these two variables. that's not to say that is necessarily a causal relationship only that there is some sort of positive relationship between these two variables. econometrics help us to quantify this degree of correlation by in a sense drawing a line through the centre of all those points. and by drawing a line through the centre of all those points we are hoping to capture what is the average effect of education on wages. so on average an individual who has years of education, might expect to obtain a wage which is let's say four hundred dollars. whereas an individual perhaps if they had a year's worth of education might expect back their wages to go up by a hundred dollars. so they now earn five hundred dollars. well econometrics is a toolset for finding out what the strength of this relationship is. so how much do wages actually go up by. and this type of relationship here where we're concerned with the relationship for individual people or individual firms is the subject of microeconometrics. and it's called microeconometrics for analogy with microeconomics. another sort of microeconometric relationship we might be interested in might be, 'what is the effect of tv advertising on a company's level of sales. so if i was to draw a graph of a company's level of sales over time then we might have something which looks something like this. if there is some sort of seasonality. perhaps this is coffee sales or ice cream sales. and we might be interested in do these peaks which we observe in the data - are they caused by tv advertising? and tv advertising might look something like these bars that i have drawn below. so econometrics is a way of understanding for this time series data does this tv advertising here cause sales to go up? and similarly does this tv advertising here cause sales to go up. so this is slightly different to the previous example in that we are dealing with what we call time series data, whereas the original data was what we call cross-sectional data. but it's still actually what we call microeconometric data because we are dealing with data for a particular firm. another type of econometrics is the subject of macroeconometrics and macroeconometrics as its name suggests is to deal with macro relationships. so an example here might be what is the effect of interest rate falls on inflation. so traditional economic theory suggests that if the interest rate falls then the inflation rate should increase because of increases in aggregate demand. so econometrics is a way of quantifying that particular relationship. so all of these relationships are variations on the same sort of theme which we see time and time again in econometrics. so the idea with econometrics is that there is some sort of population or in the case of time series data we actually call this a data generating process but you can kind of think about it for now at least in terms of a population. and within this population there is some sort of true relationship between the variables which are interested in. so there might be some sort of true relationship between an individual's level of wages and let's say their level of education. so in this example here beta quantifies the effects of one year of education on an individual's level of average wages. but there are other factors which also determine wages, which we group together in our population error 'u', which are all these other idiosyncratic factors which affect an individual's level of wages. so an example might be 'where does the individual live?' are they based in oecd countries for example? what are their interests? are they interested in pursuing a career where they earn lots of money like an investment banker or are they interested in going into the civil service for example? those are the sorts of things which are contained within the population error 'u'... 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 introduction to the subject of econometrics, using a few examples to explain the sorts of question which are likely to be encountered.in this video i want to talk about what actually do we mean by econometrics. so econometrics is in general a statistical tool set which helps us to evaluate some sort of relationship of interest. an example might be we're interested in, for individuals, what is the effect of an individual's level of education on the average wage which that individual might expect to obtain. so if an individual's level of education increases we might expect that the level of wages which an individual obtains on average might increase. so if i was to plot a graph of the level of education of individuals on the x-axis, against the wages which a group of individuals have obtained on the y-axis, then we might hope to see some sort of positive correlation between these two variables. that's not to say that is necessarily a causal relationship only that there is some sort of positive relationship between these two variables. econometrics help us to quantify this degree of correlation by in a sense drawing a line through the centre of all those points. and by drawing a line through the centre of all those points we are hoping to capture what is the average effect of education on wages. so on average an individual who has years of education, might expect to obtain a wage which is let's say four hundred dollars. whereas an individual perhaps if they had a year's worth of education might expect back their wages to go up by a hundred dollars. so they now earn five hundred dollars. well econometrics is a toolset for finding out what the strength of this relationship is. so how much do wages actually go up by. and this type of relationship here where we're concerned with the relationship for individual people or individual firms is the subject of microeconometrics. and it's called microeconometrics for analogy with microeconomics. another sort of microeconometric relationship we might be interested in might be, 'what is the effect of tv advertising on a company's level of sales. so if i was to draw a graph of a company's level of sales over time then we might have something which looks something like this. if there is some sort of seasonality. perhaps this is coffee sales or ice cream sales. and we might be interested in do these peaks which we observe in the data - are they caused by tv advertising? and tv advertising might look something like these bars that i have drawn below. so econometrics is a way of understanding for this time series data does this tv advertising here cause sales to go up? and similarly does this tv advertising here cause sales to go up. so this is slightly different to the previous example in that we are dealing with what we call time series data, whereas the original data was what we call cross-sectional data. but it's still actually what we call microeconometric data because we are dealing with data for a particular firm. another type of econometrics is the subject of macroeconometrics and macroeconometrics as its name suggests is to deal with macro relationships. so an example here might be what is the effect of interest rate falls on inflation. so traditional economic theory suggests that if the interest rate falls then the inflation rate should increase because of increases in aggregate demand. so econometrics is a way of quantifying that particular relationship. so all of these relationships are variations on the same sort of theme which we see time and time again in econometrics. so the idea with econometrics is that there is some sort of population or in the case of time series data we actually call this a data generating process but you can kind of think about it for now at least in terms of a population. and within this population there is some sort of true relationship between the variables which are interested in. so there might be some sort of true relationship between an individual's level of wages and let's say their level of education. so in this example here beta quantifies the effects of one year of education on an individual's level of average wages. but there are other factors which also determine wages, which we group together in our population error 'u', which are all these other idiosyncratic factors which affect an individual's level of wages. so an example might be 'where does the individual live?' are they based in oecd countries for example? what are their interests? are they interested in pursuing a career where they earn lots of money like an investment banker or are they interested in going into the civil service for example? those are the sorts of things which are contained within the population error 'u'... 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 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 states motion class 9 physics 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 states motion class 9 physics 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 states motion class 9 physics this video provides an introduction to the subject of econometrics, using a few examples to explain the sorts of question which are likely to be encountered.

in this video i want to talk about what actually do we mean by econometrics. so econometrics is in general a statistical tool set which helps us to evaluate some sort of relationship of interest. an example might be we're interested in, for individuals, what is the effect of an individual's level of education on the average wage which that individual might expect to obtain. so if an individual's level of education increases we might expect that the level of wages which an individual obtains on average might increase. so if i was to plot a graph of the level of education of individuals on the x-axis, against the wages which a group of individuals have obtained on the y-axis, then we might hope to see some sort of positive correlation between these two variables. that's not to say that is necessarily a causal relationship only that there is some sort of positive relationship between these two variables. econometrics help us to quantify this degree of correlation by in a sense drawing a line through the centre of all those points. and by drawing a line through the centre of all those points we are hoping to capture what is the average effect of education on wages. so on average an individual who has years of education, might expect to obtain a wage which is let's say four hundred dollars. whereas an individual perhaps if they had a year's worth of education might expect back their wages to go up by a hundred dollars. so they now earn five hundred dollars. well econometrics is a toolset for finding out what the strength of this relationship is. so how much do wages actually go up by. and this type of relationship here where we're concerned with the relationship for individual people or individual firms is the subject of microeconometrics. and it's called microeconometrics for analogy with microeconomics. another sort of microeconometric relationship we might be interested in might be, 'what is the effect of tv advertising on a company's level of sales. so if i was to draw a graph of a company's level of sales over time then we might have something which looks something like this. if there is some sort of seasonality. perhaps this is coffee sales or ice cream sales. and we might be interested in do these peaks which we observe in the data - are they caused by tv advertising? and tv advertising might look something like these bars that i have drawn below. so econometrics is a way of understanding for this time series data does this tv advertising here cause sales to go up? and similarly does this tv advertising here cause sales to go up. so this is slightly different to the previous example in that we are dealing with what we call time series data, whereas the original data was what we call cross-sectional data. but it's still actually what we call microeconometric data because we are dealing with data for a particular firm. another type of econometrics is the subject of macroeconometrics and macroeconometrics as its name suggests is to deal with macro relationships. so an example here might be what is the effect of interest rate falls on inflation. so traditional economic theory suggests that if the interest rate falls then the inflation rate should increase because of increases in aggregate demand. so econometrics is a way of quantifying that particular relationship. so all of these relationships are variations on the same sort of theme which we see time and time again in econometrics. so the idea with econometrics is that there is some sort of population or in the case of time series data we actually call this a data generating process but you can kind of think about it for now at least in terms of a population. and within this population there is some sort of true relationship between the variables which are interested in. so there might be some sort of true relationship between an individual's level of wages and let's say their level of education. so in this example here beta quantifies the effects of one year of education on an individual's level of average wages. but there are other factors which also determine wages, which we group together in our population error 'u', which are all these other idiosyncratic factors which affect an individual's level of wages. so an example might be 'where does the individual live?' are they based in oecd countries for example? what are their interests? are they interested in pursuing a career where they earn lots of money like an investment banker or are they interested in going into the civil service for example? those are the sorts of things which are contained within the population error 'u'... check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses.