Econometría Primera mitad

What are the two main properties of the error term in the OLS context?. Zero conditional mean and heteroskedasticity. Zero conditional mean and homoskedasticity. Positive conditional mean and homoskedasticity. Negative conditional mean and heteroskedasticity.

What does the 'zero conditional mean' property of the error term imply?. The error term has a non-zero expected value for any given value of the explanatory variable. The error term has an expected value of zero for any given value of the explanatory variable. The error term's variance is dependent on the explanatory variable. The error term is always zero.

What does 'homoskedasticity' mean in the context of the error term?. The error term has a constant variance across all values of the explanatory variable. The error term has a non-constant variance across all values of the explanatory variable. The error term is always positive. The error term is always negative.

Which of the following is NOT a stage of econometric modelling as listed in the document?. Determination of a research goal. Specification of explanatory variables. Estimation of advanced structural parameters. Model quality verification.

What is the difference between spatial data and time series data, according to the document?. Spatial data uses multiple companies over time, while time series data uses one company over time. Spatial data uses one company over time, while time series data uses multiple companies over time. Spatial data is collected at a single point in time, while time series data is collected over a period. Spatial data is used for cross-sectional analysis, while time series data is used for longitudinal analysis.

How many steps are involved in building an econometric model, according to the document?. 5. 6. 7. 4.

What is the basis for 'selection' of explanatory variables?. Statistical procedures. Economic theory, expert opinion, and previous research. Random assignment. Minimizing R-squared.

What is the basis for the 'choice' of explanatory variables?. Economic theory. Expert opinion. Statistical procedures. Random assignment.

Hellwig's method prefers candidate variables that are: Weakly correlated with the dependent variable and highly correlated with each other. Highly correlated with the dependent variable and weakly correlated with each other. Not correlated with the dependent variable and highly correlated with each other. Highly correlated with the dependent variable and highly correlated with each other.

Which of the following is NOT one of the three methods mentioned for choosing a functional relationship?. Source approach. Resultative approach. Empirical approach. Mixed approach.

What is the primary goal of OLS (Ordinary Least Squares)?. To maximize the error term. To minimize the sum of squared differences between observed and predicted values. To ensure all variables are perfectly correlated. To ignore the dependent variable.

The Gauss-Markov Theorem states that under certain conditions, the OLS estimator is: The worst unbiased estimator. The best linear unbiased estimator (BLUE). Always biased. A biased estimator.

In the matrix form of the OLS estimator, what does 'Y' represent?. Matrix of independent variables. Vector of structural parameters. Vector of dependent variables. Vector of error terms.

In the matrix form of the OLS estimator, what does 'Beta' represent?. Vector of dependent variables. Matrix of independent variables. Vector of error terms. Vector of structural parameters.

Which of the following is a measure of model verification?. Autocorrelation. Heteroskedasticity. Goodness of fit. Multicollinearity.

What does the coefficient of determination (R-squared) measure?. The proportion of variance in the independent variable explained by the dependent variable. The proportion of variance in the dependent variable explained by the independent variable(s). The overall accuracy of the model's predictions. The number of independent variables in the model.

For forecasting purposes, what is considered a good R-squared value?. Less than 60%. Between 60% and 70%. Higher than 90%. Exactly 50%.

How is the intercept (beta zero) of a linear model interpreted?. The predicted value of Y when X is one unit. The predicted value of Y when X is zero. The change in Y for a one-unit change in X. The error term when X is zero.

How is the slope (beta one) of a linear model interpreted?. The predicted value of Y when X is zero. The average error term. The change in Y for a one-unit change in X, holding other factors constant. The total variation in Y.

What does a T-statistic being equal to or greater than the critical value suggest?. Fail to reject the null hypothesis. Reject the null hypothesis in favor of the alternative hypothesis. The model has perfect fit. The error term is zero.

What is a 'p-value' in the context of hypothesis testing?. The probability of rejecting a true null hypothesis. The smallest significance level at which the null hypothesis would be rejected. The probability of accepting a false null hypothesis. The critical value from the T-distribution.

Autocorrelation of residuals occurs when: Residuals are independent of each other. Residuals in period 't' are dependent on residuals in period 't-1'. The variance of residuals is constant. The error term has a zero conditional mean.

Which of the following is a common reason for autocorrelation of residuals?. Perfect multicollinearity. Omitted variables, misspecification, or systematic measurement errors. Homoskedasticity. Zero conditional mean.

What is a major effect of autocorrelation on OLS estimators?. They remain the Best Linear Unbiased Estimators (BLUE). They become inefficient, and estimated variances are biased and inconsistent. They are still unbiased and consistent and BLUE. Hypothesis testing remains valid.

Heteroskedasticity means: The error term has a constant variance. The error term's variance depends on the explanatory variable(s). The error term has a zero conditional mean. The error term is normally distributed.

What is multicollinearity?. A strong linear relationship between the dependent variable and independent variables. A strong linear relationship between two or more independent variables. A non-linear relationship between independent variables. The presence of outliers in the data.

The Cobb-Douglas production function is given as Q = alpha0 * E^alpha1 * A^alpha2. What does alpha0 typically represent?. Output elasticity of labor. Output elasticity of assets. Total factor productivity. Number of employees.

In the context of the Cobb-Douglas function Q = alpha0 * E^alpha1 * A^alpha2, what do alpha1 and alpha2 represent?. Total factor productivity. Output elasticities of labor and assets, respectively. The number of employees and assets. The overall output quantity.

What is the definition of forecasting?. Calculating historical values of a dependent variable. Inferring the past based on current data. Calculating future values of a dependent variable based on an econometric model. Describing the current state of a phenomenon.

What is the rule of unbiased prediction?. The forecast is calculated as the minimum value of the dependent variable. The forecast is calculated as the expected value of the dependent variable, assuming the error term is zero. The forecast is the actual observed value of the dependent variable. The forecast is the average of past dependent variable values.

Which of the following is an assumption of econometric prediction (forecasting)?. The relationship between variables is unstable. The error term is always non-zero. The explanatory variables in the prediction horizon are known. The model cannot be extrapolated beyond the sample range.

What does 'error ex ante' refer to?. The difference between the predicted and observed value after the fact. A measure of prediction accuracy calculated before the prediction is made. The variance of the error term in the model. The bias in the model's coefficients.

What does 'error ex post' refer to?. The accuracy of a prediction made before the event. The difference between the predicted future value and the actual observed value after the fact. The variance of the explanatory variables. The stability of the functional relationship.

In OLS, the functional relationship between Y and X for cross-sectional data is typically expressed as: Y = g(X) where g is an unknown function. Estimated Y = beta0 + beta1*X1 (linear form). Y is independent of X. Y is perfectly predictable from X.

Which assumption of OLS states that the number of observations must be greater than the number of estimated coefficients?. SLR.1 (Linearity). SLR.2 (Random Sampling). SLR.3 (Sample Variation). The implied assumption related to degrees of freedom (n>k).

What does SLR.4 (zero conditional mean) state?. The error term has a non-zero expected value. The error term has an expected value of zero given any value of the explanatory variable. The variance of the error term is zero. The dependent variable has an expected value of zero.

What does SLR.5 (homoscedasticity) state?. The variance of the error term is constant. The variance of the error term is dependent on X. The error term is normally distributed. The error term is zero.

What is the main goal of econometric model verification?. To maximize the R-squared value. To ensure all parameters are statistically significant. To analyze the model's quality and its ability to represent economic phenomena. To simplify the model as much as possible.

A regression of 'I type' refers to: An exact functional relationship. A stochastic functional relationship. A relationship with perfect multicollinearity. A relationship with heteroskedasticity.

A regression of 'II type' refers to: An exact functional relationship. A stochastic functional relationship. A relationship with zero autocorrelation. A relationship with a constant error variance.

Which method of functional relationship choice is based on dynamic features of the phenomenon?. Resultative approach. Mixed approach. Source approach. Empirical approach.

When assuming symmetry of the error distribution and applying the Central Limit Theorem, what can be suggested about the error term 'u'?. It follows a uniform distribution. It follows a normal probability distribution. It follows a binomial distribution. It follows a Poisson distribution.

The 'ceteris paribus' assumption in linear regression means: All variables are changing simultaneously. Other factors influencing Y are held fixed when examining the effect of one X. The model is perfectly specified. The error term is zero.

If the DW statistic is less than dL, what is the conclusion regarding the null hypothesis of no autocorrelation?. Fail to reject the null hypothesis. Reject the null hypothesis in favor of the alternative hypothesis. The test is inconclusive. The autocorrelation is positive.

What is the role of 'explanatory variables' in an econometric model?. They are predicted by the model. They are used to explain or predict the dependent variable. They are always equal to the error term. They are determined randomly.

Which of the following best describes 'goodness of fit' in model verification?. How well the model's predictions match historical data. How closely the observed data points conform to the model's predictions. The statistical significance of the intercept term. The number of assumptions the model adheres to.

What does 'inference' mean in the context of model verification?. Calculating the R-squared value. Testing hypotheses about the population parameters based on sample estimates. Determining the functional form of the relationship. Ensuring the error term is homoskedastic.

If a model is used for descriptive purposes, what is an acceptable level for R-squared?. Higher than 90%. Around 50%. Lower than 60-70%. Should not be less than 60-70%.

What is the consequence of violating the assumption of homoskedasticity (i.e., having heteroskedasticity)?. OLS estimators are no longer unbiased. OLS estimators are no longer consistent. OLS estimators remain BLUE. The standard errors of the coefficients are biased and inconsistent, making hypothesis tests unreliable.

The 'resultative approach' to choosing a functional relationship is described as being proper only in: The context of dynamic systems. The sample of range for a single two-variable equation. Models with more than three variables. Situations with perfect multicollinearity.