Econometrics-I  |  Level 3  |  Semester 1

Econometrics-I

Instructor: Dr. Khalid Imran
Department of Economics, HSTU
Textbook: Wooldridge — Introductory Econometrics

Course Objectives

This course introduces students to the fundamental tools of econometrics. By the end of the course, students will be able to interpret economic data, build and estimate regression models, test hypotheses, and critically evaluate empirical economic research. The course follows Chapters 1–6 of Wooldridge's Introductory Econometrics: A Modern Approach.

Topics Covered

  • Chapter 1 — The Nature of Econometrics and Economic Data
    What is econometrics? Types of economic data: cross-sectional, time series, panel. Causality vs. correlation.
  • Chapter 2 — The Simple Regression Model
    OLS estimation, interpretation of slope and intercept, goodness-of-fit (R²), classical assumptions.
  • Chapter 3 — Multiple Regression Analysis: Estimation
    OLS with multiple regressors, omitted variable bias, multicollinearity, partialling-out interpretation.
  • Chapter 4 — Multiple Regression Analysis: Inference
    t-tests, F-tests, confidence intervals, testing linear restrictions.
  • Chapter 5 — Multiple Regression Analysis: OLS Asymptotics
    Large-sample properties of OLS, consistency, asymptotic normality.
  • Chapter 6 — Multiple Regression Analysis: Further Issues
    Functional forms (log, quadratic), standardized coefficients, prediction and residual analysis.

Assignment

Assignment 1 — Understanding Econometrics with Own Research Ideas

Topics: Chapters 1 & 2

Students are required to form groups and independently develop a research idea of their interest. Each group will select a topic, design a simple survey, and collect their own cross-sectional dataset. The collected data must be cleaned and submitted as the deliverable for this assignment.

  • Form groups and agree on a research topic of your interest.
  • Design a simple survey instrument to collect cross-sectional data.
  • Collect the data from your target respondents.
  • Clean the dataset (handle missing values, inconsistencies, outliers).
  • Submit the cleaned dataset along with a brief description of the data collection process.
File Submission Rules:
  • Maximum file size: 5 MB per file.
  • Accepted formats: PDF, DOC, DOCX, R, do, dta, CSV, ZIP.
  • Files larger than 5 MB will not be accepted — compress or split if needed.
  • Name your file as: GroupName_Assignment1 (e.g., GroupA_Assignment1.pdf).

Submission: Fill in the form below and upload your file.

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