TAILIEUCHUNG - Ebook Financial econometrics - From basics to advanced modeling techniques: Part 2

(BQ) Part 2 book "Financial econometrics - From basics to advanced modeling techniques" has contents: Approaches to ARIMA modeling and forecasting, autoregressive conditional heteroskedastic models, vector autoregressive models, robust estimation, principal components analysis and factor analysis,.and other contents. | c07-Approaches ARIMA Page 241 Thursday, October 26, 2006 2:06 PM CHAPTER 7 Approaches to ARIMA Modeling and Forecasting he specification and estimation of the conditional mean (., mean estimate given a certain history of time series observations) is essential in the analysis of a time series. This is, in a first step, typically done in form of an autoregressive (AR) or autoregressive moving average (ARMA) model described in the previous chapter. In case of nonstationarity, we consider autoregressive integrated moving average (ARIMA)(p, d, q) models given by (). To do so, we difference the original level series, possibly nonstationary, until it becomes stationary and model the differenced series in the standard ARMA framework. The original level data can be recovered from the differenced series by integration. There are two basic approaches to provide methods (procedures) for assessing the appropriateness of ARIMA models to describe a given time series. The first approach is attributed to Box and In essence, the Box-Jenkins approach involves inspecting the computed sample autocorrelation functions (SACFs) and sample partial autocorrelation functions (SPACFs) of the time series and comparing them with the theoretical autocorrelation functions (ACFs) and partial autocorrelation functions (PACFs). Once a good match is observed, respective parameters are computed. The major advantage of this approach lies in its systematic application of steps in model building. The disadvantage is that the visual examination of SACFs and SPACFs is rather subjective. The second approach is to select a set of possible (p, q) combinations and estimate the parameters of the corresponding ARMA(p,q) T 1 G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, rev. ed. (San Francisco: Holden-Day, 1976). 241 c07-Approaches ARIMA Page 242 Thursday, October 26, 2006 2:06 PM 242 FINANCIAL ECONOMETRICS models accordingly. The model for which a .

• Ngân hàng - Tín dụng
• Financial econometrics
• Autoregressive conditional heteroskedastic models
• Vector autoregressive models
• Robust estimation
• Principal components analysis
• Factor analysis
• Econometrics for Finance
• Introductory Econometrics for Finance
• Financial data
• Financial modelling
• Purpose of econometrics
• Econometric model
• Kinh tế tài chính
• Lecture notes in Financial Econometrics
• Least squares estimation
• Regression diagnostics
• Time series analysis
• Regression model
• Straight lines
• Statistical foundations
• Classical linear regression model
• Regression analysis
• Multiple linear regression
• Statistical distributions
• Univariate time series modelling
• Stochastic processes
• Multivariate models
• Simultaneous equations models
• Modelling long run relationship
• Stochastic non stationarity
• Modelling volatility
• GARCH models
• Switching models
• Switching behaviour
• Panel data
• Fixed effects models
• Limited dependent variable models
• Linear probability model
• Simulation methods
• Random number generation
• Empirical research project
• Research project
• Advanced modeling techniques
• Selected topics
• Regression applications
• Regression applications in finance
• Stepwise regression
• business management
• econometrics
• finance
• methodological
• empirical
• project management
• Banking crisis
• Spatial econometrics
• Financial liberalization
• Spatial panel model
• Estimate the parameters
• Introduction to EViews
• Stationarity
• Volatility Models
• Multivariate Time
• Unit Roots Tests
• Autocorrelations