Econometría V/F 2a mitad

What is the phenomenon of stability of an econometric model as a prediction assumption?. True. False.

Linearization of an econometric model aims to transform a linear model into a non-linear one. True. False.

A non-linear model can be non-linear according to variables only. True. False.

Taking the natural logarithm of both sides of Y = a0 * X^a1 transforms it into a linear model. True. False.

Statistical regularities can include structure (distribution), dynamics, and dependency in time. True. False.

A structural statistical regularity is exemplified by the distribution of individual productivity in a shipbuilding company, where employees work more in the first hours than the last. True. False.

A statistical regularity of dynamics implies that the explained variable Y depends only on time. True. False.

An example of statistical regularity of dependency in time is analyzing the dependency between total costs and total production in a company from 2000-2015, where production increases lead to cost increases. True. False.

Statistical regularity of dependency in space involves observing variables over different periods within a single period. True. False.

An econometric model is a mathematical form of stochastic relations based on intuition. True. False.

In an econometric model, the error term 'u' represents a deterministic component. True. False.

In the general form of an econometric model Y = f(X1, X2,...,Xk, u), Y is the independent variable. True. False.

The structure of an econometric model includes dependent and independent variables, structural parameters, an analytical form, and an error term. True. False.

In econometric models, X represents the dependent variable. True. False.

Deterministic error terms in econometric models arise from unobserved factors or measurement errors. True. False.

Indeterministic error terms represent the stochastic nature of economic phenomena. True. False.

The Cobb-Douglas production function is used to represent the technological relationship between inputs and outputs. True. False.

In the Cobb-Douglas function Q(L,K) = A•L^α • K^β, 'A' represents the output elasticity. True. False.

Output elasticity in the Cobb-Douglas model indicates the percentage change in output for a 1% change in a specific input, holding other inputs constant. True. False.

The Cobb-Douglas function can be applied to analyze production size and net income in enterprises. True. False.

Returns to scale refers to changes in output after a proportional change in only one input. True. False.

Increasing returns to scale (IRS) occur when output increases by more than the proportional change in all inputs. True. False.

A Cobb-Douglas model with α+β=1 shows increasing returns to scale. True. False.

Decreasing returns to scale means that doubling all inputs results in output that is less than doubled. True. False.

Economic models and econometric models are the same. True. False.

Economic models possess a deterministic character where changes are precise. True. False.

Econometric models are based on statistical data. True. False.

The Durbin-Watson (DW) statistic is used to test for serial correlation of residuals. True. False.

A Durbin-Watson statistic value of 2 indicates perfect positive autocorrelation. True. False.

If DW < dL in the Durbin-Watson test, we fail to reject the null hypothesis. True. False.

Criteria for explanatory variable specification include selecting variables based on economic theory and expert opinion. True. False.

The main effect of omitting a relevant explanatory variable is that the estimator becomes unbiased. True. False.

Bias from omitting an explanatory variable occurs only if the omitted variable influences the dependent variable. True. False.

Residuals are the differences between observed and fitted values of the dependent variable. True. False.

The sum of residuals in a regression analysis is always greater than zero. True. False.

OLS (Ordinary Least Squares) estimators are biased. True. False.

Consistency of an estimator means its probability distribution becomes more tightly distributed around the true parameter value as sample size grows. True. False.

The model Y = a0 + a1*lnX is a power function that can be transformed into a linear model. True. False.

A polynomial model like Y = a0 + a1*X + a2*X^2 can be linearized by substitution (e.g., Z = X^2). True. False.

Strict non-linear functions, like Y = (X + a1)^a2, can always be transformed into linear models using logarithms. True. False.

A point forecast provides a range within which a future value is expected to lie with a certain probability. True. False.

Törnquist functions describe the demand for consumption of goods based on income. True. False.

In a Törnquist function for basic goods, D_i = (a1*Y_i) / (Y_i + a2), if a2 > 0, the demand increases indefinitely with income. True. False.

In the Cobb-Douglas function Qt = a0*X1^a1 * X2^a2 * ... * Xk^ak * e^ut, the term 'a_i' represents the intercept. True. False.

Linearizing the Cobb-Douglas production function involves taking the natural logarithm of both sides. True. False.

Individual labor productivity models can be used to find relationships between productivity and factors like job seniority. True. False.

The model Pi = a1*log(Ti + 1) + a2 describes the relationship between aggregate labor productivity and job seniority. True. False.

A restricted regression assumes that the coefficients of some independent variables are zero. True. False.

The null hypothesis HO: β1 = 0 in simple linear regression represents a restricted model. True. False.