Econometrics

Shopping on line can be easy, simple and save you lots of money. It can also take a lot of your time, frustrate you, and result in unwanted purchases. Now the same can be said for regular high street shopping, but with the vast opportunity presented by the Internet it will pay you to spend a few minutes reading this and understanding how to better optimize your Econometrics shopping experience:

1. Compare - without doubt the biggest advantage that the Econometrics offers shoppers today is the ability to compare thousands of Econometrics at a time. This is a great thing, but not necessarily all the time! Too much can be daunting at times so take advantage of the great comparison sites and where possible let them do the hard work for you.

2. Research - if it has been said it will be on the internet. Ignorance is no longer a justifiable reason for buying the wrong thing. Take the time to research in detail everything that you could possible want to know about

3. Testimonials - don't know anybody that has bought a Econometrics? Wrong! If the Econometrics is good the internet will let you know. Use the Internet as a friend and get testimonials before you buy.

4. Questions - Got a question about Econometrics then search the Forums, FAQ's, Blogs etc. Don't be afraid to ask .....

5. Reputation - Never heard of the company selling Econometrics? Don't worry, no reason why you should know every company in the world, but you know someone that does! Use the internet to find out what people are saying about Econometrics and build up a picture of their reputation for sales, returns, customer service, delivery etc.

6. Returns - still worried that even after all of the above your Econometrics wont be what you want? Check out the returns policy. There is so much competition now that someone, somewhere is bound to offer the terms that you are comfortable with.

7. Feedback - happy with your Econometrics then let people know, after all you are depending on others people input in your buying decision, so why not give a little back.

8. Security - check for the yellow padlock on the Econometrics site before you buy, and the s after http:/ /i.e. https:// = a secure site

9. Contact - got a question about Econometrics, or want to leave a comment then check out the sites contact page. Reputable companies have them and respond.

10. Payment - ready to pay for your Econometrics, then use your credit card or PayPal! Be aware of companies that don't accept them, there may be genuine reasons but given the huge amount of choice you have when buying online there is no reason at all not to buy via credit card or PayPal.

Econometrics is concerned with the tasks of developing and applying quantitative or statistical methods to the study and elucidation of economic principles.Ragnar Frisch(1933). "Editor's Note". ''Econometrica'' '''1'''. 1-4. Econometrics combines [economics with [statistics to analyze and test economic relationships. Theoretical econometrics considers questions about the statistical properties of estimators and tests, while applied econometrics is concerned with the application of econometric methods to assess economic theories.

Purpose The two main purposes of econometrics are to give empiricism content to economic theory and to subject economic theory to potentially falsifying tests.* M. Pesaren Hashem. "Econometrics,"The New Palgrave: A Dictionary of Economics, v. 1 (1987), pp. 8-22.

For example, consider one of the basic relationships in economics, the relationship between the price of a commodity and the quantities of that commodity that people wish to purchase at each price (the Supply and Demand#Empirical estimation relationship). According to economic theory, an increase in the price would lead to a decrease in the quantity demanded, holding other relevant variables constant to isolate the relationship of interest. A mathematical equation can be written that describes the relationship between quantity, price, other demand variables like income, and an random term ε to reflect simplification and imprecision of the theoretical model: Q = \beta_0 + \beta_1 Price + \beta_2 Income + \varepsilon.

Regression analysis could be used to estimate the unknown parameters \beta_0 , \beta_1 , and \beta_2 in the relationship, using data on price, income, and quantity. The model could then be tested for statistical significance as to whether an increase in price is associated with a decrease in the quantity, as hypothesis test: \beta_1 < 0 .

There are complications even in this simple example. In order to estimate the theoretical demand relationship, the observations in the data set must be price and quantity pairs that are collected along a demand relation that is stable. If those assumptions are not satisfied, a more sophisticated model or econometric method may be necessary to derive reliable estimates and tests.

Methods One of the fundamental statistical methods used by econometricians is regression analysis. For an overview of a linear implementation of this framework, see linear regression. Regression methods are important in econometrics because economists typically cannot use Experiment#Controlled experiments. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments. Observational data may be subject to omitted-variable bias and a list of other problems that must be addressed using causal analysis of simultaneous equation models.Edward E. Leamer, "specification problems in econometrics," The New Palgrave: A Dictionary of Economics, v. 4 (1987), pp. 472-75.

Data sets to which econometric analyses are applied can be classified as time-series data sets, cross-sectional data sets, panel data sets, and multidimensional panel data sets. Time-series data sets contain observations over time; for example, inflation over the course of several years. Cross-sectional data sets contain observations at a single point in time; for example, many individuals' incomes in a given year. Panel data sets contain both time-series and cross-sectional observations. Multi-dimensional panel data sets contain observations across time, cross-sectionally, and across some third dimension. For example, the Survey of Professional Forecasters contains forecasts for many forecasters (cross-sectional observations), at many points in time (time series observations), and at multiple forecast horizons (a third dimension).

Econometric analysis may also be classified on the basis of the number of relationships modelled. Single equation methods (econometrics) model a single variable (the dependent variable) as a function of one or more explanatory (or independent) variables. In many econometric contexts, such single equation methods may not recover the effect desired, or may produce estimates with poor statistical properties. Simultaneous equation methods (econometrics) have been developed as one means of addressing these problems. Many of these methods use variants of instrumental variable to make estimates.

Other important methods include Method of Moments, Generalized Method of Moments (Generalized Method of Moments), Bayesian methods, Two Stage Least Squares (2SLS), and Three Stage Least Squares (3SLS).

Example A simple example of a relationship in econometrics from the field of labor economics is

\ln(\mbox{wage})=\beta_0 + \beta_1(\mbox{Years of education}) + \epsilon.

Economic theory says that the natural logarithm of a person's wage is a linear function of the number of years of education that person has acquired. The parameter \beta_1 measures the increase in the natural log of the wage attributable to one more year of education. The term \epsilon is a random variable representing all other factors that may have direct influence on wage. The econometric goal is to estimate the parameters, \beta_0 \mbox{ and } \beta_1 under specific assumptions about the random variable \epsilon. For example, if \epsilon and Years of Education are uncorrelated, then the equation can be estimated with ordinary least squares.

If the researcher could randomly assign people to different levels of education, the data set thus generated would allow the econometrician to estimate the effect of changes in years of education on wages. In reality, those experiments cannot be conducted. Instead, the econometrician observes the years of education of and the wages paid to people who differ along many dimensions. Given this kind of data, the estimated coefficient on Years of Education in the equation above reflects both the effect of education on wages and the effect of other variables on wages, if those other variables were correlated with education. For example, people with more innate ability may have higher wages and higher levels of education. Unless the econometrician controls for innate ability in the above equation, the effect of innate ability on wages may be falsely attributed to the effect of education on wages.

The most obvious way to control for innate ability is to include a measure of ability in the equation above. Exclusion of innate ability, together with the assumption that \epsilon is uncorrelated with education produces a misspecified model. A second technique for dealing with omitted variables is instrumental variables estimation.

Notable Econometricians Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel recipients in the field of econometrics:

Jan Tinbergen and Ragnar Frisch were awarded (in 1969) the first Nobel Prize for Economic Sciences for having developed and applied dynamic models for the analysis of economic processes.
Lawrence Klein, Professor of Economics at the University of Pennsylvania, was awarded in 1980 for his computer modeling work in the field.
Trygve Haavelmo was awarded in 1989. His main contribution to econometrics was his 1944 article (published in Econometrica) "The Probability Approach to Econometrics."
Daniel McFadden and James Heckman shared the award in 2000 for their work in microeconometrics. McFadden founded the econometrics lab at the University of California, Berkeley.
Robert Engle and Clive Granger were awarded in 2003 for work on analysing economic time series. Engle pioneered the method of autoregressive conditional heteroskedasticity (ARCH) and Granger the method of cointegration.

The Econometric Author Links of the Econometrics Journal provides personal links to recent articles and working papers of econometric authors via the RePEc system in EconPapers.

Journals The main journals which publish work in econometrics are Econometrica, the Journal of Econometrics, the Review of Economics and Statistics, the Econometric Theory, and the Journal of Applied Econometrics.

Notes

References

Handbook of Econometrics Elsevier, links to:

v. 1, pp. 3-771 (1983) v. 2, pp. 775-1461 (1984) v. 3, pp. 1465-2107 (1986) v. 4, pp. 2111-3155 (1994) v. 5, pp. 3159-3843 (2001)

Harry H. Kelejian and Wallace E. Oates (1989, 3rd ed.) Introduction to Econometrics.
Peter Kennedy (2003). A Guide to Econometrics, 5th ed.
Robert S. Pindyck and Daniel L. Rubinfeld (1998, 4th ed.).
A.H. Studenmund (2000, 4th ed.) Using Econometrics: A Practical Guide.