UGC-NET (June 2025) · India · National Testing Agency
Original practice questions written to the published syllabus · not past papersEstimating economic relationships from data and knowing when the estimates can be trusted: OLS and its assumptions, their violations, simultaneous equations, time series and binary-choice models.
In the exams: agricultural economics PhD entrance and fellowship examinations, quantitative methods (heavy); agribusiness management PhD entrance and fellowship examinations (present)
UGC-NET Economics (statistics and econometrics part)
Properties of OLS estimators, Gauss-Markov, ML, violations and tests, simultaneous-equation identification and estimation, stationarity and unit roots, binary choice. Largely conceptual MCQs with occasional short computations.
How our questions for UGC-NET spread across the units of Econometrics. Difficulty: 12 easy, 48 medium, 28 hard.
| Simultaneous equations and identification | 9 | |
| Linear probability, logit and probit models | 9 | |
| Autocorrelation | 8 | |
| Stationarity and unit roots | 8 | |
| Specification error | 7 | |
| ARIMA models | 7 | |
| Maximum likelihood | 6 | |
| Cointegration and error-correction models | 6 | |
| Hypothesis testing in regression | 5 | |
| Multicollinearity | 5 | |
| Heteroscedasticity | 5 | |
| Dummy variables | 4 | |
| Distributed lag models | 4 | |
| BLUE properties and the Gauss-Markov theorem | 3 | |
| Classical linear regression model and OLS | 2 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
With the same bounds (dL = 1.284, dU = 1.567), a computed d of 2.90 indicates
D. negative autocorrelation
For negative autocorrelation compare d with 4 - dL = 2.716 and 4 - dU = 2.433. Since 2.90 > 2.716, negative autocorrelation is indicated. Between 2.433 and 2.716 would be inconclusive.
Further reading: Gujarati and Porter, Basic Econometrics
A Breusch-Godfrey test for second-order autocorrelation (p = 2) uses n = 60; the auxiliary regression R-squared is 0.14. The 5 percent chi-square critical value with 2 df is 5.99. The test statistic and decision are
B. 8.12; reject no autocorrelation
BG = (n - p)R^2 = 58 x 0.14 = 8.12 > 5.99, so the null of no autocorrelation up to order 2 is rejected. Dividing by p is not part of the statistic.
Further reading: Gujarati and Porter, Basic Econometrics
True model: yield = b1 + 0.3 fertiliser + 5.0 soil index + u. Soil index is omitted, and the slope from regressing soil index on fertiliser is 0.02. The expected value of the estimated fertiliser coefficient is
B. 0.40
E(b2) = b2 + b3 x b32 = 0.3 + 5.0 x 0.02 = 0.40. The fertiliser effect is overstated because better soils receive more fertiliser and also yield more. 0.10 is the bias alone. 0.20 subtracts the bias instead of adding it.
Further reading: Gujarati and Porter, Basic Econometrics
Plot area is self-reported with classical measurement error. The true area variance is 80, the measurement error variance 20, and the true slope 0.5. The probability limit of the OLS slope is
A. 0.40
With classical error in X, plim b = b x var(X*)/[var(X*) + var(e)] = 0.5 x 80/100 = 0.40: attenuation toward zero. The estimate is inconsistent, not unbiased; it is not inflated. 0.625 inverts the reliability ratio (0.5 x 100/80); 0.10 uses the error share (0.5 x 20/100).
Further reading: Gujarati and Porter, Basic Econometrics
A researcher must choose between two non-nested models of farm credit demand, neither a special case of the other. A suitable test is
B. the Davidson-MacKinnon J test
The J test adds the fitted values of the rival model as a regressor and tests their significance, in both directions. It can reject both models or neither. The other tests address structural change, autocorrelation and heteroscedasticity.
Further reading: Gujarati and Porter, Basic Econometrics
In a Koyck distributed lag with impact coefficient b0 = 0.25 and decay rate lambda = 0.6, the long-run multiplier and mean lag are
B. 0.625 and 1.5 periods
Long run = b0/(1 - lambda) = 0.25/0.4 = 0.625. Mean lag = lambda/(1 - lambda) = 0.6/0.4 = 1.5. 1.36 is the median lag (-ln 2/ln lambda), a different measure.
Further reading: Gujarati and Porter, Basic Econometrics
Applying OLS to a structural demand equation in which price is endogenous gives estimates that are
B. biased and inconsistent
Price is determined jointly with quantity and so is correlated with the demand error. That is simultaneity bias, which does not disappear as the sample grows.
Further reading: Gujarati and Porter, Basic Econometrics
In the Dickey-Fuller regression Delta Y_t = delta Y_(t-1) + u_t, the null and alternative hypotheses are
A. H0: delta = 0 (unit root); H1: delta < 0 (stationary)
delta = rho - 1, so delta = 0 means rho = 1, a unit root. The alternative is one-sided stationarity. KPSS reverses the roles, taking stationarity as the null.
Further reading: Gujarati and Porter, Basic Econometrics
A regression of one district's wheat price on another's, both in levels over 25 years, gives R-squared 0.96 and Durbin-Watson 0.31. This should first raise the suspicion of
C. spurious regression between non-stationary series
Granger and Newbold's warning sign is R-squared above d. The series may be cointegrated, but that must be tested (residual ADF with Engle-Granger critical values); a high R-squared alone proves nothing. d near 0 means strong positive, not negative, autocorrelation.
Further reading: Gujarati and Porter, Basic Econometrics
The Granger representation theorem states that
A. cointegrated variables can always be represented by an error-correction model
Cointegration and error correction are two sides of one fact: a stable long-run tie implies that at least one variable responds to past disequilibrium.
Further reading: Gujarati and Porter, Basic Econometrics
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