Seminário - Rodrigo Reis Soares

Convidamos todos para o Seminário Competition and the Racial Wage Gap: Testing Becker's Model of Employer Discrimination, que será realizado dia 25 de março de 2015 (quarta-feira), apresentado pelo Prof. Dr. Rodrigo Reis Soares, docente da Escola de Economia de São Paulo/FGV, às 14h30, na Sala 22 do Bloco B-1 da FEA-RP.



É uma honra enorme ter o Rodrigo aqui na FEA-RP para esse seminário. O convite está aberto a todos os interessados.

Mini-cursos

Em relação aos dois ultimos posts, sobre o Encontro Brasileiro de Finanças e a Escola de Séries Temporais, é importante dar um destaque especial aos dois mini-cursos que serão oferecidos.


O mini-curso do Encontro Brasileiro de Finanças será ministrado pelo Prof. Yacine Ait-Sahalia, e será sobre High-Frequency Financial Econometrics, baseado no seu livro "High-Frequency Financial Econometrics" escrito em co-autoria com Jean Jacod. Fiz uma resenha rápida do livro, e recomendo bastante o curso e o livro.

Na Escola de Séries Temporais será ministrado o curso  "State Space Models: Theory, Methods and Applications" pelo Prof. David Stoffer, que deve ser baseado também em um recente livro - Nonlinear Time Series - Theory, Methods and Applications with R Examples. Randal Douc, Eric Moulines e David Stoffer, que eu também já avalie aqui.

Os dois mini-cursos abordam o estado da arte em análise de séries temporais, econometria e finanças. Assim, recomendo bastante e estarei assistindo os dois.

XVI Escola de Séries Temporais e Econometria

http://www.ime.usp.br/~abe/este2015/

XVI Escola de Séries Temporais e Econometria

Conferências

Marcelo Fernandes (FGV-SP)

Dani Gamerman (UFRJ)

S.J. Koopman (VU Amsterdam)

Alexandra Schmidt (UFRJ)

Ruey Tsay (Booth School of Business - Chicago)

Brani Vidakovic (GeorgiaTech)

Mauricio Zevallos (UNICAMP)

Wilfredo Palma (UC Chile)


ST1: High Dimensional Time Series
Chairman: Flávio Ziegelmann
Siem J. Koopman
Marcelo Medeiros
Guilherme Moura

ST2: High Dimension Volatility Models
Chairman: Hedibert Lopes
Ruey Tsay
Hedibert Lopes
André Portela

ST3: Discrete-Valued Time Series
Chairman: Valdério Reisen
Wilfredo Palma
Glaura Franco
Klaus Vasconcelos

ST4: Regularized Regression and Classification
Chairman: Marcelo Fernandes
Brani Vidakovic
Eduardo Mendes
Aluisio Pinheiro


Minicurso

 State Space Models: Theory, Methods and Applications

 David Stoffer (U. Pittsburgh)

The state space model (SSM) or the hidden Markov Model (HMM) is a very general model that subsumes a whole class of special cases of interest in much the same way that regression does.  For example, the linear Gaussian model includes such varied models ARMA models as well as smoothing splines.  Nonlinear state space models are used in finance, in particular, stochastic volatility as well as computational ecology to study population dynamics and to track animals.  While inference for linear state space models is fairly simple using numerical optimization based on derivatives, inference in the nonlinear case is difficult and relies on derivative free numerical optimization. I will introduce the basic model along with some theory and applications.  I will then introduce a variety of nonlinear models, and discuss applications and inference for these models based on Monte Carlo methods including MCEM, Metropolis-Hastings, and particle methods.


Tutoriais

T1 - Time-varying Copulas - Flávio Ziegelmann


T2 - An Introduction to Singular Spectrum Analysis - Paulo Canas Rodrigues


T3 - Bayesian Regularization - Hedibert Lopes


T4 - Resampling Techniques for Nonstationary Time Series - Jacek Leskow

15 Encontro Brasileiro de Finanças

Points of Departure - Thomas Sargent

Points of Departure - Thomas Sargent

Um ensaio sobre a evolução da econometria de expectativas racionais.  E muito mais que isso. Alguns trechos:

"Macroeconomics and econometrics are tools for recognizing patterns in data and interpreting them in ways that distinguish cause from coincidence. What attracted me to macroeconomics were its noble goals of identifying the causes of economic depressions and of understanding how government policies can promote prosperity. In graduate school, I learned that modern macroeconomics required more math than I knew. Therefore, for years at Minnesota, I’d audit a math class each quarter. It was easy to select classes: if I sought to understand economics papers about X, then I wanted math Z. During math classes, I’d get ideas for papers or recognize how to solve economic problems that had stumped me. "


"Keynes’s literary style reflected his equipment. Many of the tools thatnow serve modern macroeconomics hadn’t been invented. Keynes stressedpeople’s expectations about future outcomes as volatile determinants of aggregateinvestment, but because he had no theory of expectations, he treatedthem as exogenous variables. Wiener and Kolmogorov created a statisticaltheory of prediction during World War II. Kalman invented a recursiveversion of that theory in 1960. Wald and Bellman invented dynamic programmingin the 1940s and 1950s. Von Neumann, Morgenstern, and Savageconstructed their theories of expected utility only in the 1940s and 1950s.  "