NTA UGC NET/JRF Exam, December-2022 Economics (Shift-II)

The modern theory of rent is an economic theory that explains how rent is generated when the supply of a factor of production is scarce relative to demand.
The theory has several features, including :
(1) Rent is a part of all factors of production.
(2) Rent is the difference between actual and transfer earnings.
(3) Rent can depend on perspective.
(4) Rent can arise from other factors of production.
According to the modern theory of rent:
(1) All the factors may earn rent.
(2) Transfer earning of an unused factor is zero.
(3) Entire earning of an unused factor is economic rent.

The rational expectations theory is an economic theory that states that people make decisions based on the best information that they have, as well as their past experiences and rational thinking.

The theory suggests that people's expectations of the future will influence the current economic decisions. and that these expectations will help shape the actual economic outcome.
• Rational expectations views include :
(1) Rational expectation as a theory of expectations.
(2) An emphasis on the credibility of policy makers.
(3) A preference for policy making rules rather than discretion.

The Budget at a glance is a yearly publication that provides a reader. Friendly overview of the Union Budget of India.
• In the budget at glance, following items are included :-
(1) A budget estimate of the current year.
(2) Revised estimate of the current year.
(3) Actuals for the preceding year.
(4) Budget estimate for the forthcoming year.

Yes, technology shocks can affect labor demand and supply.

Labor demand - Technology shocks can decrease the demand for unskilled labor and increase the demand for skilled labor. For example, in the US, technological change has led to a decline in middle - skilled and automatable roles, particularly in manufacturing.

Labor supply - Technology shocks can lead to a reallocation of labour. For example, a shock to productivity differential can create excess demand for non-traded goods, which can lead tomore hiring in the non-traded sector.

Negative technology shocks : A negative technology shock can increase the price of labor and capital. For example, oil shocks of the late 1970s were a negative technology shock because the energy used to extract, oil became more expensive.

Points-
Adverse technology shocks affect labour demand and supply.
(1) Decrease the demand for labour.
(2) Both real wage and employment fall.
(3) Adverse effect an market equilibrium.
New technology can replace labor, leading to a fall in employment.

Cases of Poisson distribution-
(1) 'n' the number of trials is indefinitely large i.e.
n → ∞
(2) np = m is finite.
(3) μ₄ = m+3m²
Important points -
Poisson Process Criteria -
(1) Events are independent of each other. The occurrence of one event does not affect the probability another event will occur.
(2) The average rate (events per time period is constant).
(3) Two events cannot occur at the same time.

Followings that are correct conclusion without error in case of testing of Hypothesis :
(1) Accept H₀, when it is true.
(2) Reject H₀, when H is false.
(3) Ассеpts H₀, when H is false.

Ordinary Least Squares (OLS) estimation can be used in the presence of heteroscedasticity, but it has some consequences

(1) Unbiasedness The OLS estimator remains unbiased, but it is no longer the Best Linear Unbiased Estimator (BLUE).
(2) Unefficiency The OLS estimator is inefficient, which means that the regression prediction will also be inefficient.
(3) Inaccurate forecasts The forecasts based on the model with heteroscedasticity will be less accurate.
(4) Invalid statistical tests The estimated standard error (SE) is wrong, so confidence intervals and hypothesis tests can not be relied on.
(5) Formula for variance of coefficients The formula for the variance of the coefficients can not be applied.
If the form of the heteroscedasticity is known, it can be corrected by transforming the data.

The resulting estimator is called generalized least squares (GLS). However, GLS can exhibit strong bias in small samples if the actual skedastic function is unknown. In econometrics, it is now standard practice to include Heteroskedasticity consistent standard error instead of using GLS.

The estimated coefficients are consistent.

Assumptions of the Classical Linear Regression Model :
1) The regression model is linear, correctly specified, and has an additive error term.
2) The error term has a zero population mean.
Important points :-
The main assumptions with a linear regression model:
Linearity: The relationship between X and the mean of Y is linear. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other.

1) Zero mean value of disturbance Uᵢ.
2) No auto correlation between the disturbances.

If A, B and C are three different non empty sets then
(i) (A)' = A is true
(ii) A∪ (B∪C) = (A∪B) ∪C is correct.
(iii) A-B = A∩B' is also correct.

An F-test in a regression model hypothesis test for H₀ : ẞ₂ = ẞ₃ = 0 compares the variances of two samples or the ratio of variance between multiple samples :
(1) Null hypothesis: The null hypothesis restricts ẞ₂ and ẞ₃ to zero.
(2) Restricted Model : The restricted model is the model that results from imposing the null hypothesis on the urestricted model
(3) Test Statistic: The test statistic, random variable F, determines if the tested data has an F-distribution under the null hypothesis.
(4) Decision: The decision is made by comparing the test statistic to a critical value or by evaluating a p-value computed from the test statistic.
In a regression model, the F-test is used to identify the model that best fits the population from which the data were sampled.
Note:-
An F-test in a regression model is used to determine if a linear regression model is a better fit for a dataset than a model with no predictor variables. It's also used to check if the slope in a regression model is equal to zero.