BECE 142 key facts: June 2026
- Course code: BECE 142
- Subject: Applied Econometrics
- Programme: BA (Honours) Economics (BAECH), Semester VI Discipline Specific Elective
- Credits: 6
- Blocks and units: 4 blocks, 12 units
- Maximum marks: 100 (term-end exam 70 per cent, assignments 30 per cent)
- Duration: 3 hours
- Questions in the June 2026 paper: 12 (Q12 offers 4 short notes, attempt any 2); you attempt 8
- Samples shown on this page: 2 (Q3 and Q11); all 12 answers in the paid file
BECE 142 Solved Question Paper for June 2026 gives you the official IGNOU Applied Econometrics term-end paper with complete answers written by our economics team. Two answers are open on this page, so you can check the depth and the working before you ask for the full file.
1. What the June 2026 Solved Paper Covers
BECE 142 Applied Econometrics is a six-credit Discipline Specific Elective in Semester VI of IGNOU's BA (Honours) Economics programme, and this file solves its June 2026 term-end paper.
The June 2026 paper has 12 questions in three sections. It leans on the four ideas this course is built around: binary choice models, panel data, simultaneous equations and distributed lags. One question also asks you to explain a regression run in R, EViews or STATA, so Block 4 cannot be skipped.
This is one of the more technical papers in the BAECH Question Paper set for Semester VI. Marks here depend on writing the model correctly, stating the right condition and then explaining it, which is exactly how our answers are laid out.
Our file follows the paper's own numbering, so you can match every answer to the question in front of you. Where a question needs an equation, the answer writes the model out in full and explains each term in plain English before moving to the estimation step.
2. Two Answers Solved in Full, Ten Held Back
Two June 2026 questions are solved in full below: Q3 from Section A (20 marks) and Q11 from Section B (12 marks).
We picked one long and one medium answer so you can judge both lengths. IGNOU does not publish model answers, so both answers below were written by our team and match the style of the full file.
The identification problem in simultaneous equations models
Question 3: What is meant by the identification problem in a simultaneous equations model? When do we say that an equation is identified? (20)
A simultaneous equations model is a system in which two or more variables are determined together. In a market model, price and quantity are both endogenous: demand and supply each depend on price, and the equilibrium price itself depends on both equations at once.
The identification problem asks whether we can recover the numerical values of each structural equation's parameters from the data we actually observe. We only ever observe equilibrium points, so the reduced form can always be estimated. A structural equation is identified only if its coefficients can be worked back out of the reduced-form coefficients.
Why the problem arises: a demand and supply example
Take the simplest market model:
Demand: Qt = α0 + α1Pt + u1t, with α1 < 0
Supply: Qt = β0 + β1Pt + u2t, with β1 > 0
Both equations contain exactly the same variables, Q and P. Every observed price and quantity pair is just an intersection of the two curves. If we regress Q on P, we cannot tell whether we have estimated the demand curve, the supply curve or some mixture of the two. Neither equation is identified.
Now let consumer income Yt shift demand: Qt = α0 + α1Pt + α2Yt + u1t. Solving the two equations gives the reduced form:
Pt = π0 + π1Yt + v1t and Qt = π2 + π3Yt + v2t
where π1 = −α2/(α1 − β1) and π3 = β1π1
So β1 = π3/π1 and β0 = π2 − β1π0. The supply equation is now identified: as income moves the demand curve up and down, the equilibrium points trace out the supply curve. The demand equation is still not identified, because nothing shifts supply on its own.
If a supply shifter such as rainfall Rt enters the supply equation, the demand curve gets traced out too. Each equation now excludes one variable that appears in the other, and both become identified. The lesson is that identification comes from economic theory about which variables enter which equation, not from collecting more data.
Three possible outcomes
- Under-identified: the structural parameters cannot be obtained from the reduced form at all, so no estimation method gives meaningful values.
- Exactly identified: the reduced form gives one unique value for each structural parameter, and indirect least squares (ILS) can be used.
- Over-identified: some parameters get more than one value from the reduced form, and two-stage least squares (2SLS) is used instead.
When do we say an equation is identified?
We apply two tests. Let M be the number of endogenous variables in the model (equal to the number of equations), K the number of predetermined variables in the model, m the number of endogenous variables in the equation being checked, and k the number of predetermined variables in that equation.
Order condition (necessary). An equation is identified only if:
K − k ≥ m − 1
That is, it must exclude at least as many predetermined variables as it includes endogenous variables minus one. If K − k = m − 1 the equation is exactly identified; if K − k > m − 1 it is over-identified; if K − k < m − 1 it is under-identified.
| Equation | m | k | K − k | m − 1 | Result |
|---|---|---|---|---|---|
| Demand (includes Y, excludes R) | 2 | 1 | 1 | 1 | Exactly identified |
| Supply (includes R, excludes Y) | 2 | 1 | 1 | 1 | Exactly identified |
Rank condition (necessary and sufficient). In a model of M equations, an equation is identified if and only if at least one non-zero determinant of order (M − 1) × (M − 1) can be formed from the coefficients of the variables excluded from that equation but included in the other equations.
Here M − 1 = 1. The demand equation excludes R, and R carries a non-zero coefficient in the supply equation, so a non-zero 1 × 1 determinant exists and demand is identified. The order condition alone can mislead: if an excluded variable had a zero coefficient everywhere else, the count would pass but the rank condition would fail.
Conclusion. An equation is identified when its structural parameters can be obtained from the reduced-form coefficients. In practice we check the order condition first, confirm it with the rank condition, and then choose ILS for an exactly identified equation and 2SLS for an over-identified one.
The Chow test: purpose and what it examines
Question 11: For what purpose is the Chow test used? What does it basically seek to examine? (12)
The Chow test, proposed by Gregory Chow in 1960, is used to check the structural stability of a regression model. It asks whether one regression with the same coefficients can describe two sets of observations, or whether the relationship has changed between them.
Where it is used. In time series, a typical case is the savings and income relationship in India before and after the 1991 economic reforms. In cross-section data, it can compare a wage equation for men and women, or for rural and urban households.
What it basically examines. Write the two sub-sample regressions as:
Yt = λ1 + λ2Xt + u1t for the first n1 observations
Yt = γ1 + γ2Xt + u2t for the next n2
The null hypothesis is H0: λ1 = γ1 and λ2 = γ2, meaning no structural break. The alternative is that at least one parameter differs.
Steps of the test
- Run the pooled regression on all n1 + n2 observations and note its residual sum of squares, the restricted RSSR, with n1 + n2 − k degrees of freedom, where k is the number of parameters.
- Run separate regressions on each sub-sample to get RSS1 and RSS2.
- Add them to get the unrestricted RSSUR = RSS1 + RSS2, with n1 + n2 − 2k degrees of freedom.
- Compute the F statistic:
F = [(RSSR − RSSUR) / k] ÷ [RSSUR / (n1 + n2 − 2k)]
which follows the F distribution with k and n1 + n2 − 2k degrees of freedom.
Decision rule. If the computed F is greater than the critical F at the chosen level of significance, reject H0 and conclude that the regression is not stable across the two sub-samples. If it is smaller, a single pooled regression is acceptable.
Illustration (hypothetical figures). Suppose n1 = n2 = 20, k = 2, RSSR = 1,800, RSS1 = 600 and RSS2 = 700. Then RSSUR = 1,300 and:
F = (500 / 2) ÷ (1,300 / 36) = 250 ÷ 36.11 ≈ 6.92
The 5 per cent critical value of F(2, 36) is about 3.26, so the hypothesis of a stable relationship is rejected.
Assumptions and limits. The test assumes normally and independently distributed errors with the same variance in both sub-samples, so heteroscedasticity should be checked first. The break point must be known in advance. The test also only tells us that something changed, not whether the intercept, the slope or both shifted; the dummy variable approach answers that.
3. Questions Answered Only in the Full File
The 10 questions below are printed exactly as they appear in the June 2026 paper. Their answers are in the full solved file only.
Section A (any two, 20 marks each)
Question 1: Explain the advantages of the logit model over the linear probability model. How do you estimate the logit model?
Question 2: Specify the form of a panel data regression model. In this context, explain the assumptions and features of the fixed effect panel data model.
Question 4: Explain how the following regression model can be estimated: Yt = B0 + B1Xt + B2Xt−1 + ut (where Yt is the dependent variable, Xt is the independent variable, u is the error term, and the subscript t indicates the time period).
Section B (any four, 12 marks each)
Question 5: While estimating a regression model, a researcher omitted a relevant variable. What are its consequences?
Question 6: Bring out the fundamental difference between the logit model and the probit model.
Question 7: Distinguish between the structural form and the reduced form of a simultaneous equations model.
Question 8: Consider an econometric software package (R, EViews or STATA). Explain how a simple regression model is estimated.
Question 9: Explain the important steps in an econometric study.
Question 10: Explain the impact of measurement error in explanatory variables.
Section C (any two, 6 marks each)
Question 12: Write short notes on any two of the following.
(a) Pooled cross-section data
(b) Partial adjustment model
(c) Significance of lags in econometric models
(d) Akaike information criterion
4. Syllabus by Block and Unit
BECE 142 is taught through 4 blocks and 12 units, moving from research design to advanced regression, panel data and three software packages.
| Block | Unit | Core topics | June 2026 question |
|---|---|---|---|
| Block 1: Empirical Issues in Econometric Research | Unit 1: Stages in Empirical Research | Research design versus research methods; quantitative and qualitative research; stages of an econometric study | Q9 |
| Block 1 | Unit 2: Specification Issues | Omitted relevant and included irrelevant variables; Ramsey RESET; errors of measurement | Q5, Q10 |
| Block 1 | Unit 3: Model Selection Criteria | R2 against adjusted R2; AIC, SIC and HQIC; in-sample forecasting | Q11 (Sample 2), Q12(d) |
| Block 2: Advanced Topics in Regression Analysis | Unit 4: Auto-Regressive and Distributed Lag Models | Koyck transformation; Almon polynomial lags; partial adjustment and adaptive expectations | Q4, Q12(b), Q12(c) |
| Block 2 | Unit 5: Binary Dependent Variable Models | Linear probability model; logit and its odds ratio; probit and marginal effects | Q1, Q6 |
| Block 2 | Unit 6: Simultaneous Equations Models I | Simultaneity bias in the Keynesian income model; structural and reduced forms; order condition | Q3 (Sample 1), Q7 |
| Block 2 | Unit 7: Simultaneous Equations Models II | Indirect least squares; two-stage least squares; instrumental variable conditions | Q3 (estimation choice) |
| Block 3: Panel Data Models | Unit 8: Introduction to Panel Data | Pooled cross-sections against true panels; linear static panel model; fixed and random effects ideas | Q2, Q12(a) |
| Block 3 | Unit 9: Estimation of Panel Data Models | Within (LSDV) estimator; GLS for random effects; Hausman model selection | Q2 |
| Block 4: Econometric Software Packages | Unit 10: Introduction to R | Loading data; lm() regression and diagnostics; one-way ANOVA in R | Q8 |
| Block 4 | Unit 11: Introduction to EViews | Workfiles and data transformation; graphs and scatter plots; equation estimation | Q8 |
| Block 4 | Unit 12: Stata | Do-file editor; regress and test commands; estat ovtest and dwstat | Q8 |
Block 2 is the heaviest part of the course and supplied five of the twelve June 2026 questions. If you are reading the BECE 142 Study Material unit by unit, give Units 4 to 7 the most time, and read Unit 5 alongside Q1 and Q6 because both test binary choice.
Block and unit titles follow the BECE-142 course page on eGyanKosh. The question-to-unit mapping in the last column is our own reading of the material.
5. Paper Pattern and Marks
The June 2026 paper carries 100 marks over 3 hours, and you write 8 answers: 2 from Section A, 4 from Section B and 2 short notes from Q12.
| Section | Questions printed | Attempt | Marks each | Section total | June 2026 focus |
|---|---|---|---|---|---|
| A | Q1 to Q4 | Any 2 | 20 | 40 | Logit estimation, fixed effects, identification, a lagged-regressor model |
| B | Q5 to Q11 | Any 4 | 12 | 48 | Omitted variables, logit versus probit, reduced form, software, study steps, measurement error, Chow test |
| C | Q12 (a) to (d) | Any 2 notes | 6 | 12 | Pooled data, partial adjustment, lags, AIC |
Every BECE 142 paper we checked since June 2023 has used this same layout of four long questions, seven medium questions and one short-note question with four options. Section B gives the widest choice, four out of seven, so it is the safest place to collect marks first.
To see how the wording of a question shifts between sessions, compare this paper with the earlier sets in our IGNOU Previous Year Question Paper collection. The fixed effect question, for example, has been asked as "features", "assumptions" and "estimation" in different years.
6. Topics That Keep Returning
Binary choice models, panel data, simultaneous equation estimation and econometric software appeared in all six official BECE 142 papers we checked, from June 2023 to December 2025.
| Topic | Sessions where it appeared | Count (of 6) | Linked June 2026 question |
|---|---|---|---|
| Binary choice models (LPM, logit, probit) | All six papers | 6 | Q1, Q6 |
| Econometric software (R, EViews, STATA) | All six papers | 6 | Q8 |
| Panel data: fixed and random effects, Hausman | All six papers | 6 | Q2 |
| Simultaneity bias, ILS, 2SLS, IV and reduced form | All six papers | 6 | Q7 |
| Omitted or irrelevant variables and RESET | Jun 23, Dec 23, Jun 24, Dec 24, Jun 25 | 5 | Q5 |
| Role of lags and lags in market equilibrium | Jun 23, Dec 23, Jun 24, Dec 24, Jun 25 | 5 | Q12(c) |
| Research stages, design and methods | Jun 23, Dec 23, Dec 24, Jun 25, Dec 25 | 5 | Q9 |
| Pooled cross-section data | Dec 23, Jun 24, Dec 24, Jun 25 | 4 | Q12(a) |
| Distributed lag estimation (Koyck, Almon) | Dec 24, Jun 25, Dec 25 | 3 | Q4 |
| Identification problem, order and rank | Jun 23, Jun 24, Dec 25 | 3 | Q3 (Sample 1) |
| Model selection: adjusted R2, AIC, SIC, HQIC | Jun 24, Dec 24, Dec 25 | 3 | Q12(d) |
| Partial adjustment and adaptive expectations | Jun 25, Dec 25 | 2 | Q12(b) |
| Errors of measurement | Jun 23, Dec 25 | 2 | Q10 |
| Chow test | Jun 24 | 1 | Q11 (Sample 2) |
The counts come from the official IGNOU papers for June 2023, December 2023, June 2024, December 2024, June 2025 and December 2025. We could not find an official paper before June 2023, so this table covers six sessions, not five full years. A session counts once even if a topic appeared twice in it.
June 2026 repeated several recent questions almost word for word. Q3 on the identification problem mirrors December 2025 Q8, Q5 on an omitted variable mirrors June 2025 Q6, and Q12(d) on the Akaike criterion returns from December 2024. Our IGNOU Solved Guess Paper sets are built from this kind of session-by-session tracking.
7. How Our Answers Are Written and Checked
Every BECE 142 answer is written from the June 2026 paper and the eGyanKosh units, then checked by a second reviewer before the file is released.
Before writing, the team studied the six earlier BECE 142 papers, so answers to repeated topics such as fixed effects or omitted variables cover the angles IGNOU has already used, not just the one asked in June 2026.
For an econometrics paper, the checks are specific. Every reduced-form step and test statistic is re-derived by the reviewer, conditions such as K − k ≥ m − 1 are stated in the notation of the course material, and each model is written before it is explained, as examiners expect.
Section A answers are planned for 20 marks, so they run longer and include a worked model or a small table. Section B answers stay focused on the exact ask, and the Q12 notes are kept short and definition-led, the way 6-mark notes are marked.
8. Solved File Index, Question by Question
The full file answers all 12 June 2026 questions; this page shows 2 of them and the other 10 are in the full file only.
BECE 142 is a theory course with no separate practical or lab record, and even its software block is examined in writing. So instead of a practical file index, the table maps each question to its unit.
| Question | Section | Marks | Topic | Unit | Where the answer is |
|---|---|---|---|---|---|
| Q1 | A | 20 | Logit model versus the linear probability model, and logit estimation | Unit 5 | Full file |
| Q2 | A | 20 | Panel data regression form; assumptions and features of the fixed effect model | Units 8 and 9 | Full file |
| Q3 | A | 20 | Identification problem in simultaneous equations; when an equation is identified | Units 6 and 7 | Sample 1 above |
| Q4 | A | 20 | Estimating a model with Xt and its one-period lag Xt−1 | Unit 4 | Full file |
| Q5 | B | 12 | Consequences of omitting a relevant variable | Unit 2 | Full file |
| Q6 | B | 12 | Fundamental difference between logit and probit | Unit 5 | Full file |
| Q7 | B | 12 | Structural form versus reduced form | Unit 6 | Full file |
| Q8 | B | 12 | Estimating a simple regression in R, EViews or STATA | Units 10 to 12 | Full file |
| Q9 | B | 12 | Important steps in an econometric study | Unit 1 | Full file |
| Q10 | B | 12 | Measurement error in explanatory variables | Unit 2 | Full file |
| Q11 | B | 12 | Purpose of the Chow test and what it examines | Unit 3 | Sample 2 above |
| Q12 | C | 6 + 6 | Short notes: pooled cross-section data, partial adjustment, lags, AIC | Units 3, 4 and 8 | Full file |
9. Programmes That Include BECE 142
BECE 142 Applied Econometrics is a Semester VI Discipline Specific Elective in IGNOU's BA (Honours) Economics programme, code BAECH.
Students of the IGNOU Bachelor of Arts (Honours) Economics programme usually reach this course after the core econometrics paper. Every June 2026 question in this file is set for BAECH, and the paper header itself names the programme.
The course builds directly on BECC 110 Introductory Econometrics, which teaches the classical linear regression model and OLS. BECE 142 then takes those tools into situations OLS cannot handle on its own: yes or no outcomes, lagged effects, jointly determined variables and repeated observations on the same units.
If BECE 142 appears on your own course list under a different programme, check your programme guide before ordering, since IGNOU sets one term-end paper per course code.
10. How to Get the Full Solved Question Paper
Send a WhatsApp message to 9899436384 with the code BECE 142 to get the complete June 2026 solved paper with all 12 answers.
The full file answers the 2 questions shown here and the 10 that are only listed above, including all four Q12 short notes. Mention the session, June 2026, and your programme, BAECH, so our team shares the right Applied Econometrics file.
Replies come from our team on the same number, and you can ask about other Semester VI economics papers in the same chat.
11. BECE 142 Applied Econometrics FAQs
Quick answers to what students ask most about the June 2026 paper and our solved file.
Which questions are answered in full on this page?
Two questions are answered in full here: Q3 from Section A on the identification problem in simultaneous equations, and Q11 from Section B on the purpose of the Chow test. The other 10 questions, from logit models and fixed effects to measurement error and the four Q12 short notes, are listed as text only and answered in the full file.
How can I get the full solved paper on WhatsApp?
Send a WhatsApp message to 9899436384 with the course code BECE 142 and the session June 2026. Our team replies with the details and shares the complete solved file, which answers all 12 questions in the paper, including the two shown on this page and all four Q12 short notes. Mention BAECH in your message so you get the right Applied Econometrics file.
Is this the official IGNOU paper for June 2026?
Yes. The questions come from the official IGNOU term-end paper for BECE 142 Applied Econometrics held in June 2026 for BA (Honours) Economics. Every question on this page follows the printed paper, with the original numbering. Only the answers are our own work, written by our economics team and checked by a reviewer before release.
How many questions do I attempt in the BECE 142 exam?
You write eight answers: any two of the four Section A questions for 20 marks each, any four of the seven Section B questions for 12 marks each, and any two of the four Q12 short notes for 6 marks each. That adds up to 100 marks, and the paper lasts three hours, so time planning matters.
Which BECE 142 topics repeat most often in IGNOU papers?
Across the six official papers from June 2023 to December 2025, binary choice models, panel data, simultaneous equation estimation and econometric software appeared in every session. Omitted variables, the role of lags and research design appeared in five. June 2026 kept the pattern: its Q3 on the identification problem closely repeats December 2025 Q8, and Q5 repeats June 2025 Q6.
How many credits and blocks does BECE 142 have?
BECE 142 carries 6 credits and sits in Semester VI of BAECH as a Discipline Specific Elective. Its eGyanKosh material has four blocks: Empirical Issues in Econometric Research, Advanced Topics in Regression Analysis, Panel Data Models and Econometric Software Packages, with 12 units in all. The term-end exam carries 100 marks and lasts three hours.
Do I need R, EViews or STATA installed to prepare for BECE 142?
Not for the written exam. BECE 142 software questions, like June 2026 Q8, ask you to describe how a regression is set up and read in R, EViews or STATA, not to run it in the hall. Knowing the commands, the menu steps and the main parts of the output is enough. Practising on any one package does make Block 4 answers easier to write.
Should I revise BECC 110 before studying BECE 142?
Yes, it helps a lot. BECE 142 builds directly on BECC 110 Introductory Econometrics, which covers the classical linear regression model, OLS and hypothesis testing. The BECE 142 material extends those skills to binary dependent variables, lagged effects, simultaneous equations and panel data. If your OLS basics feel shaky, revise BECC 110 first, because almost every BECE 142 answer starts from a regression model.
12. Who Wrote and Checked the BECE 142 Answers
The BECE 142 answers were written by Prateek Talwar and reviewed by Sheetal Kirola.
Prateek Talwar, founder of Unnati Educations, leads the IGNOU solved-paper series and prepares answers from the course blocks and the official term-end paper. Sheetal Kirola, Academic Head (M.Ed.), re-derives every reduced form and test statistic before release.