Prerequisites:
Students must be familiar with the notions and methods acquired in Econometrics I and basic Statistics.
Course Description
This course gives an overview of the basic concepts in time series econometrics, with a particular emphasis on the tools needed to undertake empirical analysis. The final objective is to be able to analyze the evolution of the economic variables (inflation, Gross Domestic Product, Money, interest rate…), to understand the dynamic relationship between those variables, and to predict them. In macroeconomics context, this is very useful for policy makers; since it helps them take their decision based on better knowledge of how many macroeconomic variables affect each other at different horizons. We will focus on the following topics:
Program:
(1) Characteristics of economic time series data:
Stochastic processes and time series, stationarity and ergodicity, simple autocorrelation function (ACF) and Partial autocorrelation function (PACF). [Lecture notes + Brockwell P.J. and Davis Chapter I].
(2) Univariate stationary models:
Wold decomposition, ARMA processes, Causal models, invertible models, estimation and inference on the mean and the ACF, estimation and inference on the parameter estimates of ARMA models, white noise tests, model selection (information criteria), methodologies for the design of ARMA models, real data examples (interest rates, growth rate of GDP, temperature, etc.) [Lecture notes + B&D chapters II, III & V].
(3) Forecasting :
Forecasts computing, forecast evaluation . [Lecture notes + B&D chapters II, III & V].
(4) Dynamic Single-Equation Econometric models :
Distributed Lag models (DL), Short and Long run multipliers, Mean and Median lags, examples (partial adjustment models), estimation and inference with and without autocorrelation in errors. [Lecture notes +Stock and Watson Chapter 13].
(5) Non-stationary processes:
Non-stationary processes about a trend (vs. integrated processes, unit root Dickey-Fuller test, forecasting with non-stationary models, structural changes, permanent and transitory shocks. [Lecture notes +Stock and Watson Chapter 14, Wooldridge Chapter 18].
(6) Regression with nonstationary variables (2 Weeks):
Spurious regressions, Cointegration, Error-Correction models. [Lecture notes +Stock and Watson Chapter 14, Wooldridge Chapter 18].
(7) Additional Topics: Seasonality, HAC and ARCH