Economist Statistician Course

Lesson 1 • Descriptive Statistics for Economic Data

Teaches central tendency, dispersion, and distributional shape for economic variables. Enables accurate summarisation before modelling.

Lesson 2 • Economic Concepts for Statisticians

Introduces scarcity, markets, incentives, and macro-micro distinctions. Grounds statistical work in economic context from the start.

Lesson 3 • Introduction to Economic Indicators

Surveys key macroeconomic indicators and their statistical construction. Links indicator design to the measurement concepts introduced earlier.

Lesson 4 • Statistical Reasoning and Measurement

Covers levels of measurement, uncertainty, and the logic of inference. Connects measurement theory to economic data collection.

Lesson 1 • Bayes' Theorem and Updating Beliefs

Introduces Bayesian reasoning and prior-to-posterior updating. Applies belief revision to economic forecasting and policy evaluation.

Lesson 2 • Continuous Probability Distributions

Examines normal, log-normal, uniform, and exponential distributions. Connects distributional choice to the nature of economic variables.

Lesson 3 • Discrete Probability Distributions

Covers binomial, Poisson, and geometric distributions with economic examples. Builds intuition for count and binary outcome modelling.

Lesson 4 • Multivariate Distributions and Dependence

Introduces joint, marginal, and conditional distributions and covariance. Prepares learners for multivariate regression and portfolio analysis.

Lesson 1 • Hypothesis Testing Framework

Establishes null and alternative hypotheses, test statistics, and decision rules. Applies the framework to economic policy and research questions.

Lesson 2 • Confidence Intervals and Interval Estimation

Constructs confidence intervals for means, proportions, and regression coefficients. Interprets interval width in terms of sample size and precision.

Lesson 3 • Nonparametric and Robust Tests

Introduces rank-based and permutation tests for non-normal economic data. Extends inference to settings where distributional assumptions fail.

Lesson 4 • Point Estimation and Estimator Properties

Covers unbiasedness, consistency, efficiency, and sufficiency of estimators. Establishes criteria for choosing estimators in economic applications.

Lesson 1 • National Income and Product Accounts

Explains GDP measurement via expenditure, income, and production approaches. Connects accounting identities to macroeconomic analysis.

Lesson 2 • Input-Output Analysis

Builds and interprets input-output tables and Leontief multipliers. Applies I-O analysis to sectoral impact and policy simulation.

Lesson 3 • Price Indices and Deflation

Constructs Laspeyres, Paasche, and Fisher price indices and applies deflation. Enables conversion of nominal to real economic aggregates.

Lesson 4 • Questionnaire Design and Data Quality

Addresses question wording, response scales, and nonresponse bias. Links questionnaire quality to the reliability of economic estimates.

Lesson 1 • Multiple Regression and Model Specification

Extends OLS to multiple predictors and addresses omitted variable bias. Teaches variable selection and specification testing strategies.

Lesson 2 • Simple Linear Regression

Derives OLS estimators, interprets slope and intercept, and assesses fit. Establishes the regression framework used throughout the course.

Lesson 3 • Instrumental Variables and Endogeneity

Addresses endogeneity using IV and two-stage least squares estimation. Applies IV methods to causal questions in labour and development economics.

Lesson 4 • Gauss-Markov Assumptions and Diagnostics

Examines each classical assumption and tests for violations. Links assumption failures to biased or inefficient estimates in economic models.

Lesson 1 • Time-Series Components and Decomposition

Identifies trend, seasonality, and cyclical components in economic series. Decomposition underpins all subsequent time-series modelling steps.

Lesson 2 • ARMA and ARIMA Models

Covers autoregressive and moving-average processes and the Box-Jenkins methodology. Applies ARIMA to GDP, inflation, and financial series.

Lesson 3 • Vector Autoregression and Structural Analysis

Estimates VAR models and uses impulse response functions and variance decomposition. Enables analysis of dynamic interactions among economic variables.

Lesson 4 • Cointegration and Error Correction Models

Tests for long-run equilibrium relationships and estimates ECMs. Connects cointegration theory to macroeconomic modelling of linked variables.

Lesson 1 • Count Data and Duration Models

Models event counts with Poisson and negative binomial regression and durations with hazard models. Applies to unemployment spells and firm entry data.

Lesson 2 • Binary and Multinomial Choice Models

Estimates logit and probit models and interprets marginal effects. Extends to multinomial and ordered outcomes for richer economic decisions.

Lesson 3 • Causal Inference and Programme Evaluation

Applies difference-in-differences, regression discontinuity, and matching methods. Estimates causal effects of policies and interventions on economic outcomes.

Lesson 4 • Censored and Truncated Regression

Addresses sample selection and censoring with Tobit and Heckman models. Corrects for selection bias in wage, consumption, and labour supply data.

Lesson 1 • Poverty, Inequality, and Welfare Measurement

Constructs poverty lines, Gini coefficients, and welfare indices from survey data. Applies distributional analysis to evaluate social and fiscal policies.

Lesson 2 • Communicating Statistical Findings

Teaches data visualisation, executive summary writing, and uncertainty communication. Prepares learners to present results to technical and non-technical audiences.

Lesson 3 • Data Management and Reproducibility

Covers data cleaning, merging, and version control for reproducible analysis. Establishes professional workflows for collaborative economic research.

Lesson 4 • Fiscal and Monetary Policy Analysis

Evaluates fiscal multipliers, debt sustainability, and monetary transmission mechanisms. Uses regression and VAR tools from prior chapters for policy simulation.