chapter 1 - The nature of econometrics and economic data (lecture 1 content)
causality and the notion of ceteris paribus in Econometric analysis: what does ceteris paribus mean
'other relevant factors being equal'
structure of economic data: panel or longitudinal data - observing the same units over time leads to what advantages over cross-sectional data or even pooled cross-sectional data?
- having multiple observations on the same units allows us to control for certain unobserved characteristics of individuals, firms and so on. - panel data allows us to study the importance of lags in behavior or the result of decision making.
structure of economic data: what are the main types of data set?
1. cross sectional data 2. time-series data 3. pooled cross sections 4. panel or longitudinal data
structure of economic data: cross sectional data - what are some problems with random sampling?
1. may not be appropriate if we cannot draw the data from the random sample 2. population may not be large enough to reasonably assume the observations are independent draws e.g. trying to separate businesses by states and how state policy affects them.
structure of economic data: cross sectional data -define
consists of a sample of individuals, households, firms, cities, states, countries or a variety of other units, taken at a given point in time.
what is u?
contains the unobserved factors and errors in measuring
structure of economic data: time series data - what is another feature of time series data that requires special attention?
data frequency at which the data are collected e.g. daily, weekly, monthly
structure of economic data: cross sectional data - what is an important feature of cross sectional data?
data is obtained by random sampling from the underlying population
structure of economic data: time series data - what is a key feature of time series that makes them more difficult to analyze?
economic observations can rarely, if ever, be assumed to be independent across time.
causality and the notion of ceteris paribus in Econometric analysis: is ceteris paribus always possible?
it will usually not be possible to hold all else equal - have enough other factors been held fixed to make a case for causality? rarely is an econometric study evaluated without raising this issue.
structure of economic data: pooled cross sections - why is pooled cross sections useful?
often an effective way of analyzing the effects of a new government policy - increases the sample size - see how a key relationship has changed over time
structure of economic data: panel or longitudinal data - define
panel data or longitudinal data consists of a time series for each cross-sectional member in the data set. e.g. wage, education history for a set of individuals followed over a ten-year period
structure of economic data: pooled cross sections - define
some data sets have both cross-sectional and time series features to increase our sample size we can form a pooled cross section by combining two different years of random sampling
structure of economic data: time series data - how is time series data different to cross sectional data?
the chronological ordering of observations in a time series conveys potentially important information
what are the constants β0, β1 ....
the parameters of the econometric model describe the directions and strengths of the relationships between y and x
structure of economic data: panel or longitudinal data - what is the key feature of panel data that distinguishes them from a pooled cross section?
the same cross-sectional units (individuals, firms or countries) are followed over a given time period, whereas with pooled cross sections it is likely to be different individuals, houses when looking at house prices etc
once the econometric model is specified, what is stated?
the various hypotheses of interest in terms of the unknown parameters
structure of economic data: time series data - define
time series data consists of observations on a variable or several variables over time e.g. stock prices, money supply, consumer price index
