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Lecture

Investment Model

In course

FIN-406: Macrofinance

This course provides students with a working knowledge of macroeconomic models that explicitly incorporate financial markets. The goal is to develop a broad and analytical framework for analyzing the interaction of financial decisions, macroeconomic events ...

Description

This lecture covers the investment model, including topics such as steady state and parameter values, numerical derivatives, first-order approximation, budget constraints, lagrangian, and analytical derivatives. The instructor explains how to evaluate functions at the steady state and analyze stable eigenvalues.

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Instructors (3)

Christian Pröbsting

Stylianos Tsiaras

I am a scientific collaborator at the Luisa Lambertini's Chair of International Finance at the Swiss Finance Inst i tute. Previously I was a Max Weber Fellow at the European University Institute. I got my PhD in economics at the University of Surrey. My thesis was supervised by Professor Martin Kaae Jensen and Dr. Tom Holden. My recent research explores the set of unconventional monetary policies by the European Central Bank and their impact on the macroeconomy, welfare and inequality.

Luisa Lambertini

Luisa Lambertini is Full Professor at the Ecole Polytechnique Federale de Lausanne (EPFL), where she holds the Chair of International Finance. She received her Ph.D. in Economics from the University of California at Berkeley in 1995, her Master of Science in Economics from Warwick University in 1989 and a Laurea cum Laude from the Universita' degli Studi di Bologna in 1987. Prior to coming to EPFL, Professor Lambertini was Associate Professor at Claremont McKenna College 2005-06, Associate Professor at Boston College 2003-06 and Assistant Professor at the University of California at Los Angeles 1995-03. Professor Lambertini's research is in international finance, open-economy macroeconomics and political economy. She studies how monetary and fiscal policies affect the economy, how they interact and how they can be used to achieve good economic outcomes and improve social welfare. Professor Lambertini's research focuses on how monetary and fiscal institutions should be designed to successfully mitigate the impact of unexpected shocks while pursuing long-run goals consistent with price stability and fiscal sustainability. These issues are even more important for small open economies in an interdependent world and for countries that adopt a common currency. Professor Lambertini has published articles in the American Economic Review, the Review of Economic Studies, the European Economic Review, the Journal of International Economics and the Economic Journal, among others. She was a Consultant in the Fiscal Policies Division of the European Central Bank and she has organized several international conferences. More details on my CV

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Ontological neighbourhood

Theoretical computer science

Programming language theory: High-level programming languages

Related concepts (32)

Numerical analysis

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). It is the study of numerical methods that attempt at finding approximate solutions of problems rather than the exact ones. Numerical analysis finds application in all fields of engineering and the physical sciences, and in the 21st century also the life and social sciences, medicine, business and even the arts.

Numerical integration

In analysis, numerical integration comprises a broad family of algorithms for calculating the numerical value of a definite integral, and by extension, the term is also sometimes used to describe the numerical solution of differential equations. This article focuses on calculation of definite integrals. The term numerical quadrature (often abbreviated to quadrature) is more or less a synonym for numerical integration, especially as applied to one-dimensional integrals.

Numerical methods for ordinary differential equations

Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals. Many differential equations cannot be solved exactly. For practical purposes, however – such as in engineering – a numeric approximation to the solution is often sufficient. The algorithms studied here can be used to compute such an approximation.

Numerical methods for partial differential equations

Numerical methods for partial differential equations is the branch of numerical analysis that studies the numerical solution of partial differential equations (PDEs). In principle, specialized methods for hyperbolic, parabolic or elliptic partial differential equations exist. Finite difference method In this method, functions are represented by their values at certain grid points and derivatives are approximated through differences in these values.

Analytic function

In mathematics, an analytic function is a function that is locally given by a convergent power series. There exist both real analytic functions and complex analytic functions. Functions of each type are infinitely differentiable, but complex analytic functions exhibit properties that do not generally hold for real analytic functions. A function is analytic if and only if its Taylor series about converges to the function in some neighborhood for every in its domain.

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