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by: Rahul Bose

15

6

2

# Econometrics: random sampling ECO 5100

Marketplace > Wayne State University > Economcs > ECO 5100 > Econometrics random sampling
Rahul Bose
WSU

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notes
COURSE
Econometrics
PROF.
Arjun
TYPE
Class Notes
PAGES
2
WORDS
KARMA
25 ?

## Popular in Economcs

This 2 page Class Notes was uploaded by Rahul Bose on Wednesday June 22, 2016. The Class Notes belongs to ECO 5100 at Wayne State University taught by Arjun in Summer 2016. Since its upload, it has received 15 views. For similar materials see Econometrics in Economcs at Wayne State University.

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Date Created: 06/22/16
Chapter one : Introduction A complete econometric model for example 2 might be: wage = β0 + β1edu + β2exper + β3training + u (4) where the term u contains factors such as "innate ability", quality of education, family background, etc. For the most part, we would start with an econometric model and use economic reasoning and common sense as guides for choosing the variables. Once an econometric model has been specified, various hypotheses can be stated in terms of the unknown parameters. e.g. in equation (3), we may test β1 = 0. After data have been collected, econometric methods are used to estimate the parameters in the econometric model and to test the hypotheses of interest. In some cases, the econometric model is used to make predictions in either the testing of a theory or the study of a policy’s impact. A cross-sectional data set consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point of time. Sometimes, the data on all units do not correspond to precisely the same time period. e.g. several families may be surveyed during different weeks within a year. In a pure cross-sectional analysis, we would ignore any minor timing differences in collecting the data. An important feature of cross-sectional data is that we can often assume that they have been obtained by random sampling from the underlying population. e.g. if we obtain information on wages, education, experience, and other characteristics by randomly drawing 500 people from the working population, then we have a random sample from the population of all working people. A key feature resulting from random sampling is that the ordering of the data does not matter for econometric analysis. A time series data set consists of observations on a variable or several variables over time. Examples: stock prices, consumer price index, gross domestic product, etc. Because past events can influence future events, time is an important dimension. Unlike the arrangement of cross-sectional data, the chronological ordering of observations in a time series conveys potentially important information. A key feature of time series data that makes them more difficult to analyze than cross-sectional data is that economic observations can rarely be assumed to be independent across time. Most economic and other time series are related, often strongly related, to their recent histories. e.g. knowing something about the GDP from last quarter tells us quite a bit about the likely range of the GDP during this quarter, because GDP tend to remain fairly stable from one quarter to the next. New techniques have been developed to account for the dependent nature of economic time series and to address other issues, e.g. the fact that some economic variables tend to display clear trends over time. Xu Lin (Wayne State University) Another feature of time series data that can require special attention is the data frequency at which the data are collected. In economics, the most common frequencies are daily, weekly, monthly, quarterly, and annually. For example, stock prices are recorded daily. The money supply in the US economy is reported weekly. Many macroeconomic series e.g. inflation and unemployment rates are tabulated monthly. GDP is an example of quarter series.Other time series such as infant mortality rates for states in the US, are available only on an annual basis. Many weekly, monthly, and quarterly economic time series display a strong seasonal pattern. e.g. monthly data on housing starts differ across the months simply due to changing weather conditions. When econometric methods are used to analyze time series data, the data should be stored in chronological order. Many data sets have both cross-sectional and time series features. e.g. suppose two cross-sectional household surveys are taken in the US, one in 1985 and one in 1990. In 1985, a random sample of households is surveyed for variables such as income, savings, family size, and so on. In 1990, a new random sample of households is taken using the same survey questions. To increase sample size, we can form a pooled cross section by combining the two years. Pooling across sections from different years is often an effective way of analyzing the effects of a new government policy. The idea is to collect data from the years before and after a key policy change. As an example, consider the data on housing prices taken in 1993 and 1995, before and after a reduction in property taxes in 1994. Suppose we have data on 250 houses for 1993 and on 270 houses for 1995. See Table 1.4. Observations 1-250 correspond to the houses sold in 1993, 251-520 to the 270 houses sold in 1995. Although the order in which we store the data turns out not to be crucial, keeping track of the year is very important. This is why we enter year as a separate variable. A pooled cross section is analyzed much like a standard cross section, except that we often need to account for secular differences in the variables across the time. In fact, in addition to increasing sample size, the point of a pooled cross-sectional analysis is often to see how a key relationship has changed over time.

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