FINANCIAL ECONOMETRICSFINANCIAL ECONOMETRICSFINANCIAL ECONOMETRICSFINANCIAL ECONOMETRICS

Feb.17, 2003Feb.17, 2003

SUN LI JIANSUN LI JIAN

INTRODUCTIONINTRODUCTION

ContentsContents 1. Models,Data and Process

– The nature of the econometric approach– The Process of an econometric analysis

2. Applications of Financial Econometrics– Dynamic effects of various shocks– Empirical finance– Refining data

3. Key Features of Financial Econometrics– The regression model– Time series models– Dynamic model– Others

4. Text and Software– Text– Software

1. MODELS, DATA AND PROCESS1. MODELS, DATA AND PROCESS

The Nature of The Econometric Approach– structural analysis– evaluation– forecasting

The Process of An Empirical Analysis– model specification

structural equations and reduced forms– parameters conditions– sampling and refining data– Identification and estimation– statistical test– economic interpretation

Time Series Analysis

Theory Facts

Model DataStatistical

Theory

Econometric Theory

Refined Data

Econometric Techniques

Estimation of Econometric Model with the Refined Data Using Econometric Techniques

Evaluation Forecasting Structural Analysis

Econometric Approach Econometric Approach

Structural AnalysisStructural AnalysisEconometric Model

– Linear model Greene (2000)– Nonlinear model* Davidson Mackinnon (1993) • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •– Static model– Time series model Enders （ 1995 ） Mills(1999)– Dynamic model Christian Gourieroux (1997)

Structure Change （ Maddala and Kim,1998)– Chow test– Time-varying parameters

EvaluationEvaluation

The Simulation Approach– Identification– Limited-information estimation– Full-information estimation– Monte Carlo studies

Other Approaches– The Instruments-targets approach– The Social-welfare-function approach

ForecastingForecasting

Forecasting Methods– Sample information– Economic theory

Introduction to Forecasting Techniques– Time series model (ARIMA,GARCH,KALMAN-filter)– Statistical model (Monte Carlo techniques,MSFE)

Data and RefiningData and Refining Type

– Quantitative versus qualitative data– Time-series versus cross-section data (Panel Data)– Non-experimental versus experimental data – Micro versus macro data

Nature– Degrees of freedom– Multicollinearity– Serial correlation– Structural change– Errors in measurement– Non-stationary (trends, seasonality)– Non-linearity

Source– IMF international financial statistics (CD-ROM)

2. 2. APPLICATIONS OF FINANCIAL APPLICATIONS OF FINANCIAL ECONOMETRICSECONOMETRICS

Dynamic Effects of Various Shocks– Transmission mechanism of financial crisis– Credit channel of policy

Empirical Finance– Forecasting(price of capital assets, risk premium,etc.) – Predictability of asset returns– Market microstructure– Term structure– Financial integration

Refining Data– Missing data– Base changes (GDP,M1,etc.)– Nonstationary (EX,IR,etc.)

3. KEY FEASURES OF FINANCIAL 3. KEY FEASURES OF FINANCIAL ECONOMETRICSECONOMETRICS

The Regression Model– The Method of ordinary least squares

Assumption (disturbance term;observations, independent variables) The Gauss-Markov theorem (BLUE,consistency)

– Other methods of estimation Maximum likelihood Moments Bayesian approach

– The Probability distribution for OLS estimator Parameters and disturbance term t,F,P tests and significance (confidence intervals) Applications (structural break,prediction,model selection)

– Extensions Diagnosis and treatment

)( ttt uxy

Time Series Models– Differences between LRM and TSM

Exogenous variables,sequence,theory– Components

Trends Seasonality Cycle Irregularity (convergence) Conditional heteroskedasticity (volatility) Non-linearity (state dependency)

– Determinants Function structure: Lag order:

Dynamic Model– Transfer process (impulse response function)

)],,...,([ 21 tptttt uxxxfx

fp

)],,...,,([ 21 tpttttt uyyyxfy

Other Useful Financial Other Useful Financial Econometric ModelsEconometric Models

Methods of Instrumental Variables GMM Discrete and Limited Dependent Variable Models

– Probit,logit and tobit models Computationally Intensive Methods

– Monte Carlo methods– The bootstrap– Permutation test– Nonparametric and semiparametric estimation

Panel Data Analysis Survival Data Analysis Event-Study Analysis

4. TEXT AND SOFTWARE4. TEXT AND SOFTWAREText 

– Greene,William H. (2002) Econometrics Analysis.5th ed. Prentice-Hall International,Inc.

– Mills,T.C. (1999) The Econometric Modeling of Financial Time Series. Cambridge University Press.

– TSP (Ver.4.4) Reference Manual (1997)

Software (http://emlab.berkeley.edu)– TSP,SHAZAM,RATS,Eviews– GAUSS,S-PLUS– SPSS,SAS,STATA– Mathematica,Excel

The Basics of Time Series Analysis Software– Starting and quitting

Interactive mode batch mode Fundamental program structure and some important commands

– Constructing and manipulating data Data set-up(frequency,numbers) Data input(external file;format;subsets) Data transformation(dynamic equation;order change) Refining data(seasonality,etc.) Descriptive statistics(mean,variance,correlation,etc.) Data output(print,plot,output,type,etc.)

– Linear regression analysis Analysis command(OLS) The interpretation of the test statistics The economical implication of empirical results

7. SUMMARY AND CONCLUSIONS7. SUMMARY AND CONCLUSIONS Econometrics utilizes economic theory,facts(data) and statistical

techniques,to measure and to test certain relationships among economic variables,thereby giving these results to economic reasoning.

Empirical finance provides analytical tools needed to examines the behavior of financial markets.Topics covered include estimating the dynamic impact multiplier of financial shocks,forecasting the value of capital assets,measuring the volatility of asset returns, testing the financial integration, and more.

Time-series econometrics is concerned with the estimation of difference equations containing stochastic components. These solution can be divided into two parts: a homogeneous portion and particular portion .The former is especially important in that it yields the characteristic roots which determine the system stability,the latter will be solved by the use of lag operators.

This chapter introduces some basic concepts of the soft used to time series analysis and describes commands for setting up observations, reading data,making transformation,and illustrating OLS estimation method.

Appendix : TSP Programs to Appendix : TSP Programs to Accompany Accompany IntroductionIntroduction

OPTIONS CRT;

? Monetary Approach to Exchange Rate

FREQ M;

SMPL 80 :1,90:12;

LOAD(FILE=‘C:\DATA\EXCISE1.XLS);

PRINT SJA MJA IJA YJA MGE IGE YGE;

? Data statistic description

MSD(CORR,COVA)MJA MGE IJA IGE;

? Data transformations

SJAGE=SJA/SGE;

LOGSJAGE=LOG(SJAGE);

LOGM=LOG(MJA)-LOG(MGE);

DI=IJA-IGE;

LOGY=LOG(YJA)-LOG(YGE);

PLOT LOGM * LOGY +;

PLOT DI %;

? Empirical analysis (technique:OLS)

OLSQ LOGSJAGE C LOGM DI LOGY;

ESLSJAGE=@FIT;

ESRES=@RES;

PLOT LOGSJAGE + ESLSJAGE*;

PLOT ESRES %;

END;