This builtin is not currently supported: Animation
- Book & Article Categories
- Collections
- Custom Solutions
- Main Menu
Book & Article Categories
- Main Menu
Book & Article Categories
Roberto Pedace
Roberto Pedace, PhD, is an associate professor in the Department of Economics at Scripps College. His published work has appeared in Economic Inquiry, Industrial Relations, the Southern Economic Journal, Contemporary Economic Policy, the Journal of Sports Economics, and other outlets.
Articles & Books From Roberto Pedace
Cheat Sheet / Updated 02-09-2022
You can use the statistical tools of econometrics along with economic theory to test hypotheses of economic theories, explain economic phenomena, and derive precise quantitative estimates of the relationship between economic variables.To accurately perform these tasks, you need econometric model-building skills, quality data, and appropriate estimation strategies.
Article / Updated 12-09-2021
Many economic phenomena are dichotomous in nature; in other words, the outcome either occurs or does not occur. Dichotomous outcomes are the most common type of discrete or qualitative dependent variables analyzed in economics. For example, a student who applies to graduate school will be admitted or not. If you're interested in determining which factors contribute to graduate school admission, then your outcome or dependent variable is dichotomous.
Article / Updated 02-22-2017
In econometrics, a random variable with a normal distribution has a probability density function that is continuous, symmetrical, and bell-shaped. Although many random variables can have a bell-shaped distribution, the density function of a normal distribution is precisely
where
represents the mean of the normally distributed random variable X,
is the standard deviation,and
represents the variance of the normally distributed random variable.
Article / Updated 01-25-2017
In econometrics, you use the chi-squared distribution extensively. The chi-squared distribution is useful for comparing estimated variance values from a sample to those values based on theoretical assumptions. Therefore, it’s typically used to develop confidence intervals and hypothesis tests for population variance.
Article / Updated 03-26-2016
Using natural logs for variables on both sides of your econometric specification is called a log-log model. This model is handy when the relationship is nonlinear in parameters, because the log transformation generates the desired linearity in parameters (you may recall that linearity in parameters is one of the OLS assumptions).
Article / Updated 03-26-2016
If you believe that the outcome (dependent variable) you’re modeling is likely to approach some value asymptotically (as X approaches zero or infinity), then an inverse function may be the way to go.
Inverse functions can be useful if you’re trying to estimate a Phillips curve (the inverse relationship between inflation and unemployment rates) or a demand function (the inverse relationship between price and quantity demanded), among other economic phenomena where the variables are related inversely.
Article / Updated 03-26-2016
In econometrics, the procedure used for forecasting can be quite varied. If historical data is available, forecasting typically involves the use of one or more quantitative techniques. If historical data isn't available, or if it contains significant gaps or is unreliable, then forecasting can actually be qualitative.
Article / Updated 03-26-2016
In econometrics, the regression model is a common starting point of an analysis. As you define your regression model, you need to consider several elements:
Economic theory, intuition, and common sense should all motivate your regression model.
The most common regression estimation technique, ordinary least squares (OLS), obtains the best estimates of your model if the CLRM assumptions hold.
Article / Updated 03-26-2016
Economists apply econometric tools in a variety of specific fields (such as labor economics, development economics, health economics, and finance) to shed light on theoretical questions. They also use these tools to inform public policy debates, make business decisions, and forecast future events.
Following is a list of ten interesting, practical applications of econometric techniques.
Article / Updated 03-26-2016
In econometrics, the standard estimation procedure for the classical linear regression model, ordinary least squares (OLS), can accommodate complex relationships. Therefore, you have a considerable amount of flexibility in developing the theoretical model. You can estimate linear and nonlinear functions including but not limited to
Polynomial functions (for example, quadratic and cubic functions)
Inverse functions
Log functions (log-log, log-linear, and linear-log)
In many cases, the dependent variable in a regression model can be influenced by both quantitative variables and qualitative factors.
