Lazarski Open Courses

01. International Economics and Finance (IEF): Overview of the course

02. IEF: The overview of the Global Economy and Sources of Data on Global Economy

03. IEF: The Gravity Model of Trade - Theoretical Introduction and Estimation in Eviews and R

04. IEF: Comparative advantage model - part 1: general information and model assumptions

05. IEF: Comparative advantage model - part 2: productivity table and potential gains from trade

06. IEF: Comparative advantage model - part 3: production possibility frontier and autarky

07. IEF: Comparative advantage part 4 - terms of trade and gains from trade

08. IEF: Comparative advantage part 5 - model extensions

09. IEF: Neoclassical model of trade - introduction and main propositions

10. IEF: Neoclassical model of trade: production functions and isoquants

11. IEF: Neoclassical model of trade - cost minimisation

12. IEF: Neoclassical model of trade: demand for labor/capital and constant returns to scale

Functions - Indentation Errors - Python

8.1 Models with natural logarithm (log-log, lin-log, and log-lin models)

08. AQE: Introduction to difference equations and iterative method

6.1. Dummy Variable Regression Models: Introduction

60. IEA: Introduction to nonlinear programming and nonnegativity restrictions

Loops and iterables - Continue and break - Python

5 2 Assessing normality of distribution using Gretl

Lists - Modifying elements - Python

6.6 Converting from Normal Distribution to Standard Normal Distribution

Functions - Real Life Example - Python

Strings - Built-in functions - Python

6.4. Dummy variable trap

Gravity model example

2.6. Two-Variable Regression Analysis: The Gauss-Markov theorem

Lists - Accessing elements - Python

Strings - Arithmetic Operations - Python

64. IEA: Example of maximization with inequality constraint - a customer choice problem

Advanced functions - Recursion - Python

Advanced functions - Global variables - Python

9.3. Dummy variables for more than two categories

Conditional Statements - First example - Python

10.6. Time Series Econometrics: Order of integration

11. AQE: Time paths of complementary functions in case of first-order difference equations

Welcome to Programming in Python!

3.1. Two-Variable Regression: Normality assumption

03. AQE: Complementary function under complex roots

Loops and iterables - Iterables and iterators - Python

Advanced functions - Functions inside other functions - Python

13. DEA: Investment and capital formation

21. AQE: Second order difference equations - complementary functions for repeated a:nd complex root

Functions - Definition - Python

15. AQE: Introduction to difference equations and iterative method of solution

16. AQE: Solving first-order difference equation using general method

10. IEA: Implicit function theorem and derivatives

63. IEA: Kuhn-Tucker conditions in the case of n variables and m constraints

09. IEA: Leontief Input-Output Model. Example

2.1. Two-Variable Regression Analysis: Introduction

Strings - Introduction - Python

Two variable regression with OLS