RBI Grade B DSIM

The RBI Grade B exam is a prestigious opportunity for those aspiring to join the Reserve Bank of India as an officer. Specifically, the DSIM (Department of Statistics and Information Management) stream focuses on candidates with expertise in statistics, data management, and analysis. This blog post aims to provide a comprehensive guide covering the syllabus, exam pattern, and effective preparation strategies for the RBI Grade B DSIM exam.

Exam Pattern

Selection will be through an Online/Written Examination (WE) and an Interview. The examination consists of three papers: Paper I, which is an Objective Type paper on Statistics, followed by Paper II and Paper III, both of which are Subjective Type papers. After clearing all three papers, there will be an Interview.

Paper-I : Objective Type (on Statistics)  —–  Duration – 120 minutes ——— Maximum Marks – 100

Paper-II : Descriptive Type (on Statistics) ——— Duration – 180 minutes ——– Maximum Marks – 100

Paper-III : English – Descriptive      ————  Duration – 90 minutes ————–  Maximum Marks – 100

Eligibility Criteria

(a). A Master’s Degree in Statistics/ Mathematical Statistics/ Mathematical
Economics/ Econometrics/ Statistics & Informatics/ Applied Statistics &
Informatics with a minimum of 55% marks or equivalent grade in aggregate of
all semesters / years; OR
(b). Master’s Degree in Mathematics with a minimum of 55% marks or an
equivalent grade in aggregate of all semesters / years and one year post
graduate diploma in Statistics or related subjects from an Institute of repute;
OR
(c). Master’s Degree in Data Science/ Artificial Intelligence/ Machine Learning/
Big Data Analytics, with a minimum of 55% marks or equivalent grade in
aggregate of all semesters/ years from a recognized University/ Institute, an
institute of national importance, UGC/ AICTE approved programme; OR
(d). Four-year Bachelor’s Degree with a minimum of 60% marks or equivalent
grade in aggregate of all semesters/ years in Data Science/ AI/ ML/ Big Data
Analytics from a recognized University/ Institute, an institute of national
importance, UGC/ AICTE approved programme;

OR
(e). Two years Post Graduate Diploma in Business Analytics (PGDBA) with a
minimum of 55% marks or equivalent grade in aggregate of all
semesters/years from a recognized University/ Institute, an institute of national
importance, UGC/ AICTE approved programme.

Syllabus for RBI Grade B DSIM

Syllabus for Paper – 1

Questions would cover Probability: Definition of Probability, Standard distribution, Large and small sample theory, Analysis of Variance, Estimation, Testing of Hypotheses, Multivariate analysis and Stochastic Processes.

Syllabus for Paper – 2

Questions would cover (i) Probability and Sampling, (ii) Linear Models and Economic Statistics, (iii) Statistical Inference: Estimation, Testing of hypothesis and Non-parametric Test, (iv) Stochastic Processes, (v) Multivariate analysis, (vi) Econometrics and time series, (vii) Statistical computing; and (viii) Data Science, Artificial Intelligence and Machine Learning Techniques.

syllabus for Paper – 3 (For English Paper)

English: The paper on English shall be framed in a manner to assess the writing skills including expression and understanding of the topic.

Detailed Syllabus for RBI Grade – B DSIM

1 . Theory of Probability and Probability Distributions :

Classical and axiomatic approach of probability and its properties, Bayes Theorem and its application, strong and weak laws of large numbers, characteristic functions, central limit theorem, probability inequalities.

Standard probability distributions – Binomial, Poison, Geometric, Negative binomial, Uniform, Normal, exponential, Logistic, Log-normal, Beta, Gamma, Weibull, Bivariate normal etc.

Exact Sampling distributions – Chi-square, student’s t, F and Z distributions and their applications. Asymptotic sampling distributions and large sample tests, association and analysis of contingency tables.

Sampling Theory:

Standard sampling methods such simple random sampling, Stratified random sampling, Systematic sampling, Cluster sampling, Two stage sampling, Probability proportional to size etc. Ratio estimation, Regression estimation, non-sampling errors and problem of non-response, and Correspondence and categorical data analysis.

2 . Linear Models and Economic Statistics

Simple linear regression – assumptions, estimation, and inference diagnostic checks; polynomial regression, transformations on Y or X (Box-Cox, square root, log etc.), method of weighted least squares, inverse regression. Multiple regression – Standard Gauss Markov setup, least squares estimation and related properties, regression analysis with correlated observations. Simultaneous estimation of linear parametric functions, Testing of hypotheses; Confidence intervals and regions; Multicollinearity and ridge regression, LASSO

Definition and construction of index numbers, Standard index numbers; Conversion of chain base index to fixed base and vice-versa; base shifting, splicing and deflating of index numbers; Measurement of economic inequality: Gini’s coefficient, Lorenz curves etc.

3 . Statistical Inference: Estimation, Testing of Hypothesis and Non-Parametric Test

Estimation 

Concepts of estimation, unbiasedness, sufficiency, consistency and efficiency. Factorization theorem. Complete statistic, Minimum variance unbiased estimator (MVUE), Rao-Blackwell and Lehmann-Scheffe theorems and their applications. Cramer-Rao inequality.

Methods of Estimation

Method of moments, method of maximum likelihood estimation, method of least square, method of minimum Chi-square, basic idea of Bayes estimators.

Principles of Test of Significance

Type-I and Type-II errors, critical region, level of significance, size and power, best critical region, most powerful test, uniformly most powerful test, Neyman Pearson theory of testing of hypothesis. Likelihood ratio tests, Tests of goodness of fit. Bartlett’s test for homogeneity of variances.

Non-Parametric Test

The Kolmogorov-Smirnov test, Sign test, Wilcoxon Signed-rank test, Wilcoxon Rank-Sum test, Mann Whitney U-test, Kruskal-Walls one way ANOVA test, Friedman’s test, Kendall’s Tau coefficient, Spearman’s coefficient of rank correlation.

4. Stochastic Processes :

 Poisson Processes

Arrival, interarrival and conditional arrival distributions. Non-homogeneous Processes. Law of Rare Events and Poisson Process. Compound Poisson Processes.

Markov Chains

Transition probability matrix, Chapman- Kolmogorov equations, Regular chains and Stationary distributions, Periodicity, Limit theorems. Patterns for recurrent events. Brownian Motion – Limit of Random Walk, its defining characteristics and peculiarities; Martingales.

5. Multivariate Analysis

Multivariate normal distribution and its properties and characterization; Wishart matrix, its distribution and properties, Hotelling’s T2 statistic, its distribution and properties, and its applications in tests on mean vector, Mahalanobis’ D2 statistics; Canonical correlation analysis, Principal components analysis, Factor analysis and cluster analysis.

6. Econometrics and Time Series

General linear model and its extensions, ordinary least squares and generalized least squares estimation and prediction, heteroscedastic disturbances, pure and mixed estimation. Auto correlation, its consequences and related tests; Theil BLUS procedure, estimation and prediction; issue of multi-collinearity, its implications and tools for handling it; Ridge regression.

Linear regression and stochastic regression, instrumental variable regression, autoregressive linear regression, distributed lag models, estimation of lags by OLS method. Simultaneous linear equations model and its generalization, identification problem, restrictions on structural parameters, rank and order conditions; different estimation methods for simultaneous equations model, prediction and simultaneous confidence intervals.

Exploratory analysis of time series; Concepts of weak and strong stationarity; AR, MA and ARMA processes and their properties; model identification based on ACF and PACF; model estimation and diagnostic tests; BoxJenkins models; ARCH/GARCH models.

Inference with Non-Stationary Models

ARIMA model, determination of the order of integration, trend stationarity and difference stationary processes, tests of non-stationarity.

7. Statistical Computing

Simulation techniques for various probability models, and resampling methods jack-knife, bootstrap and crossvalidation; techniques for robust linear regression, nonlinear and generalized linear regression problem, treestructured regression and classification; Analysis of incomplete data – EM algorithm, single and multiple imputation; Markov Chain Monte Carlo and annealing techniques, Gibbs sampling, Metropolis-Hastings algorithm; Neural Networks, Association Rules and learning algorithms.

8. Data Science, Artificial Intelligence and Machine Learning Techniques

ntroduction to supervised and unsupervised pattern classification; unsupervised and reinforcement learning, basics of optimization, model accuracy measures.

Supervised Algorithms

Linear Regression, Logistic Regression, Penalized Regression, Naïve Bayes, Nearest Neighbour, Decision Tree, Support Vector Machine, Kernel density estimation and kernel discriminant analysis; Classification under a regression framework, neural network, kernel regression and tree and random forests.

Unsupervised Classification

Hierarchical and non-hierarchical methods: k-means, k-medoids and linkage methods, Cluster validation indices: Dunn index, Gap statistics.

Bagging (Random Forest) and Boosting (Adaptive Boosting, Gradient Boosting) techniques; Recurrent Neural Network (RNN); Convolutional Neural Network; Natural Language Processing.

Preparation strategy

• Understand the Syllabus Thoroughly:

  • Familiarize yourself with each topic in the syllabus. This will help you prioritize your study schedule and focus on weak areas.

• Create a Study Plan:

  • Develop a structured timetable that allocates time for each subject. Ensure you incorporate regular revision sessions.

• Use Standard Study Material:

  • Refer to trusted books and online resources. Consider titles like

• Practice Mock Tests:

  • Regularly attempt mock exams to familiarize yourself with the exam format and improve your time management skills.

• Stay Updated:

  • Follow current affairs through newspapers, magazines, and reliable online sources. This is crucial for the General Awareness section.

• Enhance Analytical Skills:

  • Work on exercises related to statistical analysis and data interpretation. Familiarize yourself with tools like Excel, R, or Python.

• Join Study Groups:

  • Collaborating with peers can provide different perspectives and motivate you to stay on track.

• Focus on Writing Skills:

  • Practice essay and report writing regularly. Get feedback from mentors or peers to improve your writing style and clarity.

• Prepare for the Interview:

  • Research common interview questions for RBI Grade B candidates. Be ready to discuss your knowledge of statistics, financial management, and current affairs.

Book recommendation

For Paper-I and Paper-II

• Theory of Probability and Probability Distributions
  • Rohatgi, V. K. and Saleh, A.K. Md. E. (2005). An Introduction to Probability and Statistics
  • Goon, A.M., Gupta, M.K. and Dasgupta. B. (1985). An Outline of Statistical Theory Vol- I & II
  • Sukhatme, P.V., Sukhatme, B.V., Sukhatme, S. and Asok, C. (1984). Sampling Theory of Surveys with Applications
  • S. C. Gupta, V. K. Kapoor (2000). Fundamentals of Mathematical Statistics
  • W.G. Cochran (1977). Sampling Techniques
•    Linear Models and Economic Statistics
  • P.G. Hoel, S.C. Port and C.J. Stone (1971). Introduction to Statistical Theory
  • A.M. Mood, F.A. Graybill and D.C. Boes (1974). Introduction to Theory of Statistics
  • R. G. D. Allen (1975). Index Numbers in Theory and Practice
• Statistical Inference
  • Kale, B.K. (1999). A First Course on Parametric Inference
  • Rao, C.R. (1973). Linear Statistical Inference and Its Applications
  • Bartoszynski, R. and Bugaj, M.N. (2007). Probability and Statistical Inference
  • Gibbons, J.D. and Chakraborti, S. (1992). Nonparametric Statistical Inference
• Stochastic Processes
  • Bhat, B.R. (2000). Stochastic Models- Analysis and Applications
  • Prabhu, N.U. (2007). Stochastic Processes: Basic Theory and its Applications
  • J. Medhi (2009). Stochastic Process
• Multivariate Analysis
  • Anderson, T.W. (2003). An Introduction to Multivariate Statistical Analysis
  • Arnold, Steven F. (1981). The Theory of Linear Models and Multivariate Analysis
  • Giri, N.C. (1977). Multivariate Statistical Inference, Academic Press
  • Alvin C. Rencher (2012). Methods of Multivariate Analysis
• Econometrics and Time Series
  • Johnston, J. (1984). Econometric Methods
  • James H. Stock and Mark W. Watson (2019). Introduction to Econometrics
  • J.D. Hamilton (1994). Time Series Analysis
  • William H. Greene (2018). Econometric Analysis
• Statistical Computing and Data Science, Artificial Intelligence and Machine Learning Techniques
  • Sheldon M. Ross (2012). Simulation
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009). The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Second Edition
  • Charu C. Aggarwal (2018). Neural Networks and Deep Learning
  • Roger D. Peng: Advanced Statistical Computing
  • William J. Kennedy, Jr. and James E. Gentle: Statistical Computing