Econometria 2a mitad

What is the primary assumption regarding the stability of an econometric model related to prediction?. Stability of the functional relationship only. Stability of the set of explanatory variables, functional relationship, coefficient estimates, and error term distribution parameters. Stability of the error term distribution parameters only. Stability of the coefficient estimates only.

What is the main purpose of linearization in econometrics?. To transform linear models into non-linear models. To simplify complex non-linear relationships into a linear form for easier analysis. To increase the complexity of econometric models. To introduce more error terms into the model.

Which of the following is NOT a type of non-linear model mentioned in the document?. Non-linear according to variables. Non-linear according to parameters. Non-linear according to both variables and parameters. Non-linear according to the error term.

In the equation Y = a0 + a1X + a2X², how can it be transformed into a linear model?. By taking the natural logarithm of Y. By substituting X² with a new variable Z. By squaring the entire equation. By removing the a2 term.

What are the statistical regularities mentioned in the document?. Structure (distribution), dynamics and fluctuations, dependency in time, and dependency in space. Only dynamics and fluctuations. Only dependency in time and space. Structure, dynamics, and time dependency.

What is an example of a structural statistical regularity?. The increasing sale of cool drinks in summer months compared to winter months. The water quantity produced by an operator over several periods. The profit of a firm analyzed over a decade. The correlation between total costs and production across multiple companies.

In a statistical regularity of dynamics, what primarily influences the explained variable (Y)?. Explanatory variables that change across different observations. Time and some stochasticity. Spatial dependencies between observations. Only the structure of the data.

What characterizes a statistical regularity of dependency in time?. The phenomenon (Y) is observed across different geographical locations in a single period. The explained variable (Y) and explanatory variables (X) are observed over many periods. The relationship between variables is purely deterministic. The relationship is only dependent on the structure of the data.

An example of a statistical regularity of dependency in space involves: Analyzing the trend of a company's profits over 15 years. Observing how water production changes seasonally. Examining the relationship between production costs and output across different firms in a single year. Studying the impact of time on individual productivity.

What is the mathematical definition of an econometric model?. A deterministic representation of economic relationships. A statistical representation of stochastic relations between economic and non-economic phenomena. A purely theoretical framework without data. A description of exact functional relationships.

In the general form of an econometric model Y = f(X1, X2,..., Xk, u), what does 'u' represent?. The dependent variable. An independent variable. The error term (disturbance term, random error). A structural parameter.

What is the primary role of the error term 'u' in an econometric model?. To represent the exact functional relationship. To capture discrepancies between actual observed values and the values predicted by an exact functional relationship. To represent the independent variables. To define the dependent variable.

Which component is NOT typically part of the structure of an econometric model?. Dependent and independent variables. Structural parameters. Analytical form. Market speculation.

How are variables classified in econometric models?. Dependent, independent, and error. Explanatory, response, and control. Regressand, regressor, and stochastic. All of the above.

What are the two main reasons for the occurrence of the error term in an econometric model?. Measurement errors and deterministic factors. Imprecise functional relationships/measurement errors and the stochastic nature of economic phenomena. Unobserved factors and human behavior. Model specification and data limitations.

Which of the following best describes the Cobb-Douglas production function?. A model for consumer demand. A linear representation of production technology. A functional form representing the technological relationship between inputs (like labor and capital) and output. A model for predicting stock prices.

In the Cobb-Douglas function Q(L,K) = A•L^α • K^β, what does 'A' represent?. The amount of labor expended. The amount of physical capital. Total Factor Productivity (TFP). The output elasticity of labor.

What do the Greek characters alpha (α) and beta (β) represent in the Cobb-Douglas function?. The intercept and the error term. The amount of capital and labor. The output elasticities of the inputs (labor and capital, respectively). Total Factor Productivity.

Which application is NOT mentioned for the Cobb-Douglas production function?. Analyzing production size based on various factors. Estimating the relationship between factors and production. Analyzing net income in enterprises. Predicting short-term stock market fluctuations.

What does 'Returns to Scale' refer to in production?. The change in output from a change in a single input. The change in output when all inputs are increased by a constant factor. The efficiency of a single production process. The profit margin of a company.

If a+β = 1 in a Cobb-Douglas model, what does it indicate about returns to scale?. Increasing returns to scale. Decreasing returns to scale. Constant returns to scale. No returns to scale.

What happens when there are Decreasing Returns to Scale (DRS)?. Output increases by more than the proportional increase in all inputs. Output increases by exactly the same proportion as the increase in all inputs. Output increases by less than the proportional increase in all inputs. Output remains unchanged.

What is the key difference between an economic model and an econometric model?. Economic models are always deterministic; econometric models are stochastic. Economic models are based on statistical data; econometric models are theoretical. Economic models use precise relationships; econometric models use simplified relationships. Economic models focus on qualitative analysis; econometric models focus on quantitative analysis.

Which statement about econometric models is FALSE?. They are mathematical forms based on statistical data. They represent stochastic relations. Model parameters reflect concrete quantitative relations. They can include both economic and non-economic phenomena.

What is the Durbin-Watson (DW) statistic used for?. Testing for heteroscedasticity. Testing for multicollinearity. Testing for serial correlation of residuals (autocorrelation). Testing the overall significance of the regression model.

What does a Durbin-Watson statistic value of 2 indicate?. Strong positive autocorrelation. Strong negative autocorrelation. No autocorrelation. Inconclusive results.

In the context of the Durbin-Watson test, what does rejecting the null hypothesis (HO) typically imply?. There is no autocorrelation. There is significant autocorrelation (either positive or negative). The model is perfectly specified. The sample size is too small.

What is the first step in specifying explanatory variables for time or spatial dependency models?. Choosing variables based on statistical procedures. Checking the volatility of potential variables. Selecting potential explanatory variables based on economic theory, expert opinion, or prior research. Testing for correlation among potential variables.

What is a consequence of omitting a relevant explanatory variable from a regression model?. The estimators will be unbiased and efficient. The disturbance variance will be underestimated. The estimators will be biased, and the disturbance variance will be overestimated. The precision of estimation for relevant coefficients will increase.

When does the bias resulting from omitting an explanatory variable occur?. Only when the omitted variable influences the dependent variable. Only when the omitted variable is correlated with an included explanatory variable. When the omitted variable influences the dependent variable AND is correlated with an included explanatory variable. Always, regardless of correlation or influence.

What is a 'residual' in the context of regression analysis?. The predicted value of the dependent variable. The coefficient of an independent variable. The difference between the observed value and the fitted (predicted) value of the dependent variable. The sum of squared errors.

What is the goal when deriving estimates for regression coefficients (like β₀ and β₁)?. To maximize the sum of residuals. To minimize the sum of squared residuals. To make the residuals equal to the observed values. To make the residuals as large as possible.

In the model DATA = FIT + RESIDUAL, what does the 'RESIDUAL' term represent?. The systematic part of the data explained by the model. The deviations of observed values from their mean, normally distributed with mean 0 and variance σ. The exact relationship between variables. The average value of the dependent variable.

What property of OLS estimators is being proven in section 54?. Consistency. Efficiency. Unbiasedness. Robustness.

What does it mean for an estimator to be unbiased?. Its estimate's value is always exactly equal to the true parameter value. Its expected value is equal to the true value of the parameter. Its distribution is tightly clustered around the true parameter value. It yields the most precise estimates possible.

What is 'consistency' of an estimator?. The estimator's expected value equals the true parameter. The estimator's probability distribution becomes more tightly distributed around the true parameter as the sample size grows. The estimator is immune to outliers. The estimator is the most efficient among unbiased estimators.

Which type of non-linear model can be transformed into a linear one using logarithms?. Polynomial models like Y = a0 + a1X + a2X². Strictly non-linear functions like Y = (X + a1)^a2. Power functions like Y = a0 * X^a1. Both (a) and (b).

How is an exponential function like Y = e^(a0 + a1X) linearized?. By taking the square root of both sides. By taking the natural logarithm of both sides. By substituting X with Z. By cubing both sides.

For non-linear models that cannot be easily transformed to linear forms (strict non-linear functions), what methods are typically used for parameter estimation?. Ordinary Least Squares (OLS) directly. Graphical analysis only. Numerical algorithms or data segmentation. Simple averaging of data points.

What is the difference between a point forecast and an interval forecast?. A point forecast predicts a range, while an interval forecast predicts a single value. A point forecast predicts a single value at a specific future time, while an interval forecast provides a range of probable values. Point forecasts use historical data, while interval forecasts do not. Interval forecasts are always more accurate than point forecasts.

What do Törnquist functions describe?. Production input-output relationships. The demand for goods or services, with income as an explanatory factor. The dynamics of stock prices. The relationship between inflation and unemployment.

In the Törnquist function for basic goods, Dᵢ = (α₁Yᵢ) / (Yᵢ + α₂), what does α₁ represent when viewed as a horizontal asymptote?. The income elasticity of demand. The price elasticity of demand. The maximum potential demand or saturation level. The base level of demand when income is zero.

What is the general definition of a production function?. It describes the relationship between market prices and demand. It measures the technical relationships between inputs/resources and the effects of the production process. It forecasts the profit of a firm. It analyzes the distribution of income.

In the context of the Cobb-Douglas production function Qt = a₀X₁ᵗᵃ¹ X₂ᵗᵃ² ... X<0xE2><0x82><0x96>ᵗᵃᵏ eᵘᵗ, what does 'aᵢ' represent?. The intercept term. The output elasticity of the i-th production factor. The total factor productivity. The error term.

Linearizing the Cobb-Douglas function involves: Taking the square root of both sides. Applying logarithms to both sides and then using OLS. Multiplying all variables by a constant. Removing the exponential term.

What does 'a₁' describe in the linearized Cobb-Douglas function lnQₜ = lna₀ + a₁lnX₁t + ... + a<0xE2><0x82><0x96>lnX<0xE2><0x82><0x96>t + uₜ?. The intercept. The percentage change in production for a 1% increase in the first factor (X₁), holding others constant. The total factor productivity. The error variance.

What are the two main types of individual labor productivity studied?. Short-term vs. long-term productivity. Inter-individual and intra-individual productivity. High-skill vs. low-skill productivity. Manual vs. cognitive productivity.

In the model Pᵢ = a₁log(Tᵢ + 1) + a₂, what does the estimate of 'a₂' represent?. The impact of job seniority on productivity. The rate of change of productivity with seniority. The level of productivity for an employee with zero job seniority. The maximum productivity level achievable.

What is a 'restricted model' in regression analysis?. A model where all coefficients are restricted to be non-zero. A model where coefficients of some independent variables are assumed to be zero. A model with a very small sample size. A model that includes only one independent variable.

For simple linear regression, a common null hypothesis (H0) associated with a restricted model is: H0: β₀ = 0. H0: β₁ ≠ 0. H0: β₁ = 0. H0: R² = 1.

What does 'linearity in parameters' mean for a model?. The dependent variable is a linear function of the independent variables. The model's equation is linear with respect to its coefficients (parameters). The independent variables are linearly related to each other. The error term is linearly related to the parameters.

Consider the equation Y = a₀ + a₁X + a₂X². Is this equation linear in parameters?. No, because X is squared. Yes, because it can be transformed into a linear form by substitution. Yes, because the parameters a₀, a₁, and a₂ enter the equation linearly. No, because it's a non-linear model.

What is the Cobb-Douglas production function widely used to represent?. Consumer utility maximization. The technological relationship between inputs and output. Market equilibrium in the short run. The dynamics of inflation.

If a production process exhibits increasing returns to scale, what happens to output when all inputs are doubled?. Output less than doubles. Output exactly doubles. Output more than doubles. Output remains the same.

What statistical regularity describes patterns observed across different geographical locations in a single time period?. Dependency in time. Dynamics and fluctuations. Structure (distribution). Dependency in space.