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| 141383248 | What is a stochastic variable? | (Also called random variable) A variable whose value is not fully known until it is observed. Most economic variables are stochastic. | 1 | |
| 141383249 | What is a population? | The set of all possible values of a random variable. The size of a population may be infinite or finite. | 2 | |
| 141383250 | What is a sample? | A set of realizations selected from the population of a random variable. The size of a sample may be infinite or finite. Infinite samples are only relevant for theoretical considerations since they may never be observed. | 3 | |
| 141383251 | What is an estimator? | A formula (or, rule) used to calculate estimates of parameters from a sample. It is a random variable. Its value is not fully known until we obtain a sample. | 4 | |
| 141383252 | What is an estimate? | A result of an estimator given a specific sample. It is a realization of a random variable. | 5 | |
| 141383253 | What is the population mean? | A measure of the central tendency of the probability function of a random variable. | 6 | |
| 141383254 | What is the sample mean? | An estimator of the population mean of a random variable. | 7 | |
| 141383255 | What is the population variance? | A measure of the dispersion of the probability function of a random variable about its population mean. | 8 | |
| 141383256 | What is the normal distribution? | A continuous, symmetrical, bell shaped probability distribution that can be completely characterized by only two parameters -- its population mean and population variance. For this reason, it is one of the most studied, and useful, probability distributions. | 9 | |
| 141383257 | What is the standard normal distribution? | A normal distribution with a population mean of 0 and a population variance of 1. | 10 | |
| 141383258 | Explain why the sample mean and variance may be different than the population mean and variance. | The calculated sample mean and variance are realizations of estimators of the population mean and variance. Estimators are random variables whose values depend on a sample. Since the calculated sample mean and variance are derived from unbiased estimators, the random variables from which they are drawn are centered on the population mean and variance, but individual realizations can be different than this central tendency. | 11 | |
| 141388281 | What is the stochastic error term? | The variation in the dependent variable that cannot be explained by the deterministic portion of the model. | 12 | |
| 141388283 | What is regression analysis? | A statistical technique that attempts to "explain" the movement of a variable as a function of set of variables through the quantification of an equation. | 13 | |
| 141388285 | What is the residual? | The difference between the actual value of the dependent variable and the fitted value of the dependent variable. | 14 | |
| 141388287 | What is ordinary least squares? | A regression technique that chooses estimated coefficients that minimize the residual sum of squares. | 15 | |
| 141388289 | What is total sum of squares? | The amount of the variation in the dependent variable regression analysis attempts to explain. It is equal to the sum of the squared deviation of the dependent variable around its mean. | 16 | |
| 141388291 | What is the explained sum of squares? | The amount of TSS explained by the estimated regression equation. It is equal to the sum of the squared deviation of the fitted dependent variable around its mean. | 17 | |
| 141388293 | What is the residual sum of squares? | The amount of the TSS unexplained by the estimated regression equation. It is equal to the sum of the squared residuals. | 18 | |
| 141388294 | What is simple correlation coefficient? | A measure of the strength and direction of the linear relationship between two variables. It ranges from -1 to 1. The sign indicates the direction of the relationship and the absolute value indicates the strength of the relationship. | 19 | |
| 141388295 | What is meant by "least squares estimates"? | "Least-squares estimates" are estimates of the population coefficients (parameters) of the population regression model (data generating process). They are obtained by minimizing (finding the least possible value of) the sum of the squared residuals. | 20 | |
| 141388296 | What are the Classical Assumptions? | A set of conditions sufficient to insure that OLS coefficient estimators are the Best (minimum variance), Linear, Unbiased Estimators. | 21 | |
| 141388297 | What is the classical error term? | A stochastic term, added to the deterministic component of a regression equation, with a zero population mean, an identical population variance for each observation (no heteroskedasticity), and each observation is independent of all other observations (no serial correlation). | 22 | |
| 141388298 | What is an unbiased estimator? | An estimator whose expected value is equal to the parameter being estimated. | 23 | |
| 141388299 | What is BLUE? | Best (minimum variance), Linear, Unbiased Estimator. | 24 | |
| 141388300 | What would happen if we had perfect collinearity of the explanatory variables? | If there was perfect collinearity (of the explanatory variables), then estimation would not have been possible. We would not have any estimated coefficients. | 25 | |
| 141393654 | What is the 1st Classical Assumption? | The population and the estimated regression models are of the same form and are linear in the coefficients with an additive error term. | 26 | |
| 141393655 | What is meant by "of the same form" when describing variables in the Classical Assumptions? | "of the same form" means that variables in the population and variables in the estimate are the same. | 27 | |
| 141393656 | What is the 2nd Classical Assumption? | All explanatory variables are non-stochastic with a finite, non-zero variance for any sample size. | 28 | |
| 141393657 | What is the 3rd Classical Assumption? | No explanatory variable is a perfect linear combination of set of other explanatory variables and the number of observations is greater than the number of coefficients to be estimated. | 29 | |
| 141393658 | What is the 4th classical assumption? | The population mean of the error term is zero. | 30 | |
| 141393659 | What is the 5th Classical Assumption? | The error term from one observation is independent of the error from all other observations; no serial correlation; no autocorrelation. | 31 | |
| 141393660 | What is the 6th Classical Assumption? | The error term has a constant variance; no heteroskedasticity. | 32 | |
| 141393661 | True or False: According to the 2nd Classical Assumption, we can have variables with trends. | False. Variables must have a finite, non-zero variance, where finite means, essentially, that we CANNOT have variables with trends, except under very special circumstances. | 33 |
