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<p> 320350.zip (3.3 MB, 需要: 10 个论坛币) 本附件包括：

• ch00.pdf
• ch01.pdf
• ch02.pdf
• ch02 stochastic processes.pdf
• ch03 Forecasting.pdf
• ch04 ARMA n VAR representation.pdf
• ch05 stochastic regression.pdf
• ch06 estimation of LR cov matrix.pdf
• ch07 testing linear forecasting model.pdf
• ch08 VAR techniques.pdf
• ch09 GMM.pdf
• ch10 application of GMM.pdf
• ch11 UR.pdf
• ch12 coint n spurious.pdf
• ch13 cointg.pdf
• ch14.pdf
• ch15 VAR UR.pdf
• ch16 panel.pdf
• chapp-complex variable spectrum.pdf
• chapp-intro to gauss.pdf

2009-4-30 00:38:00 上传

[下载] Structural Macroeconometrics
需要: 10 个论坛币  [购买]

</p><p>[point=10][/point]</p><p><br/></p><p>1 INTRODUCTION 1<br/>2 STOCHASTIC PROCESSES 5<br/>2.1 Review of Probability Theory . . . . . . . . . . . . . . . . . . . . . . 5<br/>2.2 Stochastic Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 7<br/>2.3 Conditional Expectations . . . . . . . . . . . . . . . . . . . . . . . . . 8<br/>2.4 Stationary Stochastic Processes . . . . . . . . . . . . . . . . . . . . . 12<br/>2.5 Conditional Heteroskedasticity . . . . . . . . . . . . . . . . . . . . . . 16<br/>2.6 Martingales and Random Walks . . . . . . . . . . . . . . . . . . . . . 18<br/>2.A A Review of Measure Theory . . . . . . . . . . . . . . . . . . . . . . 19<br/>2.B Convergence in Probability . . . . . . . . . . . . . . . . . . . . . . . . 29<br/>2.B.1 Convergence in Distribution . . . . . . . . . . . . . . . . . . . 30<br/>2.B.2 Propositions 2.2 and 2.3 for In¯nite Numbers of R.V.'s (Incom-<br/>plete) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31<br/>3 FORECASTING 33<br/>3.1 Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33<br/>3.1.1 De¯nitions and Properties of Projections . . . . . . . . . . . . 33<br/>3.1.2 Linear Projections and Conditional Expectations . . . . . . . 35<br/>3.2 Some Applications of Conditional Expectations and Projections . . . 37<br/>3.2.1 Volatility Tests . . . . . . . . . . . . . . . . . . . . . . . . . . 37<br/>3.2.2 Parameterizing Expectations . . . . . . . . . . . . . . . . . . . 39<br/>3.2.3 Noise Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40<br/>3.A Introduction to Hilbert Space . . . . . . . . . . . . . . . . . . . . . . 42<br/>3.A.1 Vector Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . 42<br/>3.A.2 Hilbert Space . . . . . . . . . . . . . . . . . . . . . . . . . . . 44<br/>4 ARMA AND VECTOR AUTOREGRESSION REPRESENTATIONS 51<br/>4.1 Autocorrelation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51<br/>4.2 The Lag Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52<br/>4.3 Moving Average Representation . . . . . . . . . . . . . . . . . . . . . 53<br/>4.4 Autoregression Representation . . . . . . . . . . . . . . . . . . . . . . 55<br/>iii<br/>iv CONTENTS<br/>4.4.1 Autoregression of Order One . . . . . . . . . . . . . . . . . . . 55<br/>4.4.2 The p-th Order Autoregression . . . . . . . . . . . . . . . . . 57<br/>4.5 ARMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58<br/>4.6 The Wold Representation . . . . . . . . . . . . . . . . . . . . . . . . 58<br/>4.7 Fundamental Innovations . . . . . . . . . . . . . . . . . . . . . . . . . 61<br/>4.8 The Spectral Density . . . . . . . . . . . . . . . . . . . . . . . . . . . 63<br/>4.A Di®erence Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . 65<br/>5 STOCHASTIC REGRESSORS IN LINEAR MODELS 67<br/>5.1 The Conditional Gauss Markov Theorem . . . . . . . . . . . . . . . . 68<br/>5.2 Unconditional Distributions of Test Statistics . . . . . . . . . . . . . 73<br/>5.3 The Law of Large Numbers . . . . . . . . . . . . . . . . . . . . . . . 75<br/>5.4 Convergence in Distribution and Central Limit Theorem . . . . . . . 76<br/>5.5 Consistency and Asymptotic Distributions of OLS Estimators . . . . 80<br/>5.6 Consistency and Asymptotic Distributions of IV Estimators . . . . . 82<br/>5.7 Nonlinear Functions of Estimators . . . . . . . . . . . . . . . . . . . . 83<br/>5.8 Remarks on Asymptotic Theory . . . . . . . . . . . . . . . . . . . . . 83<br/>5.A Monte Carlo Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 84<br/>5.A.1 Random Number Generators . . . . . . . . . . . . . . . . . . . 85<br/>5.A.2 Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88<br/>5.A.3 Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89<br/>5.A.4 A Pitfall in Monte Carlo Simulations . . . . . . . . . . . . . . 90<br/>5.A.5 An Example Program . . . . . . . . . . . . . . . . . . . . . . 92<br/>6 ESTIMATION OF THE LONG-RUN COVARIANCE MATRIX 101<br/>6.1 Serially Uncorrelated Variables . . . . . . . . . . . . . . . . . . . . . 102<br/>6.2 Serially Correlated Variables . . . . . . . . . . . . . . . . . . . . . . . 103<br/>6.2.1 Unknown Order of Serial Correlation . . . . . . . . . . . . . . 103<br/>6.2.2 Known Order of Serial Correlation . . . . . . . . . . . . . . . 108<br/>7 TESTING LINEAR FORECASTING MODELS 112<br/>7.1 Forward Exchange Rates . . . . . . . . . . . . . . . . . . . . . . . . . 113<br/>7.2 The Euler Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116<br/>7.3 The Martingale Model of Consumption . . . . . . . . . . . . . . . . . 118<br/>7.4 The Linearized Euler Equation . . . . . . . . . . . . . . . . . . . . . 119<br/>7.5 Optimal Taxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121<br/>8 VECTOR AUTOREGRESSION TECHNIQUES 124<br/>8.1 OLS Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125<br/>8.2 Granger Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126<br/>8.3 The Impulse Response Function . . . . . . . . . . . . . . . . . . . . . 129<br/>8.4 Forecast error decomposition . . . . . . . . . . . . . . . . . . . . . . . 132<br/>CONTENTS v<br/>8.5 Structural VAR Models . . . . . . . . . . . . . . . . . . . . . . . . . . 133<br/>8.6 Identi¯cation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136<br/>8.6.1 Short-Run Restrictions for Structural VAR . . . . . . . . . . . 136<br/>8.6.2 Identi¯cation of block recursive systems . . . . . . . . . . . . . 138<br/>8.6.3 Two-step ML estimation . . . . . . . . . . . . . . . . . . . . . 139<br/>9 GENERALIZED METHOD OF MOMENTS 143<br/>9.1 Asymptotic Properties of GMM Estimators . . . . . . . . . . . . . . . 143<br/>9.1.1 Moment Restriction and GMM Estimators . . . . . . . . . . . 143<br/>9.1.2 Asymptotic Distributions of GMM Estimators . . . . . . . . . 144<br/>9.1.3 Optimal Choice of the Distance Matrix . . . . . . . . . . . . . 145<br/>9.1.4 A Chi-Square Test for the Overidentifying Restrictions . . . . 146<br/>9.2 Special Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146<br/>9.2.1 Ordinary Least Squares . . . . . . . . . . . . . . . . . . . . . 146<br/>9.2.2 Linear Instrumental Variables Regressions . . . . . . . . . . . 147<br/>9.2.3 Nonlinear Instrumental Variables Estimation . . . . . . . . . . 148<br/>9.2.4 Linear GMM estimator . . . . . . . . . . . . . . . . . . . . . . 148<br/>9.3 Important Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . 149<br/>9.3.1 Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150<br/>9.3.2 Identi¯cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 151<br/>9.4 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151<br/>9.4.1 Sequential Estimation . . . . . . . . . . . . . . . . . . . . . . 151<br/>9.4.2 GMM with Deterministic Trends . . . . . . . . . . . . . . . . 153<br/>9.4.3 Minimum Distance Estimation . . . . . . . . . . . . . . . . . . 153<br/>9.5 Hypothesis Testing and Speci¯cation Tests . . . . . . . . . . . . . . . 154<br/>9.6 Numerical Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 156<br/>9.7 The Optimal Choice of Instrumental Variables . . . . . . . . . . . . . 158<br/>9.8 Small Sample Properties . . . . . . . . . . . . . . . . . . . . . . . . . 158<br/>9.A Asymptotic Theory for GMM . . . . . . . . . . . . . . . . . . . . . . 161<br/>9.A.1 Asymptotic Properties of Extremum Estimators . . . . . . . . 162<br/>9.A.2 Consistency of GMM Estimators . . . . . . . . . . . . . . . . 164<br/>9.A.3 A Su±cient Condition for the Almost Sure Uniform Convergence165<br/>9.A.4 Asymptotic Distributions of GMM Estimators . . . . . . . . . 170<br/>9.B A Procedure for Hansen's J Test (GMM.EXP) . . . . . . . . . . . . . 174<br/>10 EMPIRICAL APPLICATIONS OF GMM 181<br/>10.1 Euler Equation Approach . . . . . . . . . . . . . . . . . . . . . . . . 181<br/>10.2 Alternative Measures of IMRS . . . . . . . . . . . . . . . . . . . . . . 183<br/>10.3 Habit Formation and Durability . . . . . . . . . . . . . . . . . . . . . 185<br/>10.4 State-Nonseparable Preferences . . . . . . . . . . . . . . . . . . . . . 187<br/>10.5 Time Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188<br/>10.6 Multiple-Goods Models . . . . . . . . . . . . . . . . . . . . . . . . . . 189<br/>vi CONTENTS<br/>10.7 Seasonality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191<br/>10.8 Monetary Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192<br/>10.9 Calculating Standard Errors for Estimates of Standard Deviation, Cor-<br/>relation, and Autocorrelation . . . . . . . . . . . . . . . . . . . . . . 193<br/>10.10Real Business Cycle Models and GMM Estimation . . . . . . . . . . 194<br/>10.11GMM and an ARCH Process . . . . . . . . . . . . . . . . . . . . . . 199<br/>10.12Other Empirical Applications . . . . . . . . . . . . . . . . . . . . . . 202<br/>11 UNIT ROOT NONSTATIONARY PROCESSES 210<br/>11.1 De¯nitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211<br/>11.2 Decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212<br/>11.3 Tests for the Null of Di®erence Stationarity . . . . . . . . . . . . . . 214<br/>11.3.1 Dickey-Fuller Tests . . . . . . . . . . . . . . . . . . . . . . . . 214<br/>11.3.2 Said-Dickey Test . . . . . . . . . . . . . . . . . . . . . . . . . 216<br/>11.3.3 Phillips-Perron Tests . . . . . . . . . . . . . . . . . . . . . . . 218<br/>11.3.4 Park's J Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 218<br/>11.4 Tests for the Null of Stationarity . . . . . . . . . . . . . . . . . . . . 220<br/>11.5 Near Observational Equivalence . . . . . . . . . . . . . . . . . . . . . 221<br/>11.6 Asymptotics for unit root tests . . . . . . . . . . . . . . . . . . . . . 222<br/>11.6.1 DF test with serially uncorrelated disturbances . . . . . . . . 222<br/>11.6.2 ADF test with serially correlated disturbances . . . . . . . . . 226<br/>11.6.3 Phillips-Perron test . . . . . . . . . . . . . . . . . . . . . . . . 232<br/>11.A Asymptotic Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239<br/>11.A.1 Functional Central Limit Theorem . . . . . . . . . . . . . . . 239<br/>11.B Procedures for Unit Root Tests . . . . . . . . . . . . . . . . . . . . . 239<br/>11.B.1 Said-Dickey Test (ADF.EXP) . . . . . . . . . . . . . . . . . . 239<br/>11.B.2 Park's J Test (JPQ.EXP) . . . . . . . . . . . . . . . . . . . . 240<br/>11.B.3 Park's G Test (GPQ.EXP) . . . . . . . . . . . . . . . . . . . . 241<br/>12 COINTEGRATING AND SPURIOUS REGRESSIONS 245<br/>12.1 De¯nitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246<br/>12.2 Exact Finite Sample Properties of Regression Estimators . . . . . . . 248<br/>12.2.1 Spurious Regressions . . . . . . . . . . . . . . . . . . . . . . . 249<br/>12.2.2 Cointegrating Regressions . . . . . . . . . . . . . . . . . . . . 253<br/>12.3 Large Sample Properties . . . . . . . . . . . . . . . . . . . . . . . . . 254<br/>12.3.1 Canonical Cointegrating Regression . . . . . . . . . . . . . . . 255<br/>12.3.2 Estimation of Long-Run Covariance Parameters . . . . . . . . 257<br/>12.4 Tests for the Null Hypothesis of No Cointegration . . . . . . . . . . . 259<br/>12.5 Tests for the Null Hypothesis of Cointegration . . . . . . . . . . . . . 260<br/>12.6 Generalized Method of Moments and Unit Roots . . . . . . . . . . . 261<br/>12.A Procedures for Cointegration Tests . . . . . . . . . . . . . . . . . . . 263<br/>12.A.1 Park's CCR and H Test (CCR.EXP) . . . . . . . . . . . . . . 263<br/>CONTENTS vii<br/>12.A.2 Park's I Test (IPQ.EXP) . . . . . . . . . . . . . . . . . . . . . 264<br/>13 ECONOMIC MODELS AND COINTEGRATING REGRESSIONS247<br/>13.1 The Permanent Income Hypothesis of Consumption . . . . . . . . . . 248<br/>13.2 Present Value Models of Asset Prices . . . . . . . . . . . . . . . . . . 251<br/>13.3 Applications to Money Demand Functions . . . . . . . . . . . . . . . 253<br/>13.4 The Cointegration Approach to Estimating Preference Parameters . . 253<br/>13.4.1 The Time Separable Addilog Utility Function . . . . . . . . . 255<br/>13.4.2 The Time Nonseparable Addilog Utility Function . . . . . . . 259<br/>13.4.3 Engel's Law and Cointegration . . . . . . . . . . . . . . . . . 264<br/>13.5 The Cointegration-Euler Equation Approach . . . . . . . . . . . . . . 267<br/>13.5.1 The Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . 270<br/>13.5.2 The 2-Step Estimation Method . . . . . . . . . . . . . . . . . 274<br/>13.5.3 Measuring Intertemporal Substitution: The Role of Durable<br/>Goods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276<br/>13.6 Purchasing Power Parity . . . . . . . . . . . . . . . . . . . . . . . . . 276<br/>14 ESTIMATION AND TESTING OF LINEAR RATIONAL EXPEC-<br/>TATIONS MODELS 284<br/>14.1 The Nonlinear Restrictions . . . . . . . . . . . . . . . . . . . . . . . . 284<br/>14.1.1 Stationary dt . . . . . . . . . . . . . . . . . . . . . . . . . . . 285<br/>14.1.2 Di®erence Stationary dt . . . . . . . . . . . . . . . . . . . . . 287<br/>14.2 Econometric Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 288<br/>14.2.1 Stationary dt . . . . . . . . . . . . . . . . . . . . . . . . . . . 288<br/>14.2.2 Di®erence Stationary dt . . . . . . . . . . . . . . . . . . . . . 289<br/>15 VECTOR AUTOREGRESSIONS WITH UNIT ROOT NONSTA-<br/>TIONARY PROCESSES 296<br/>15.1 Identi¯cation on Structural VAR Models . . . . . . . . . . . . . . . . 297<br/>15.1.1 Long-Run Restrictions for Structural VAR Models . . . . . . . 297<br/>15.1.2 Short-run and Long-Run Restrictions for Structural VAR Models298<br/>15.2 Vector Error Correction Model . . . . . . . . . . . . . . . . . . . . . . 301<br/>15.2.1 The model and Long-run Restrictions . . . . . . . . . . . . . . 301<br/>15.2.2 Identi¯cation of Permanent Shocks . . . . . . . . . . . . . . . 303<br/>15.2.3 Impulse Response Functions . . . . . . . . . . . . . . . . . . . 305<br/>15.2.4 Forecast-Error Variance Decomposition . . . . . . . . . . . . . 307<br/>15.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308<br/>15.3 Structural Vector Error Correction Models . . . . . . . . . . . . . . . 309<br/>15.4 An Exchange Rate Model with Sticky Prices . . . . . . . . . . . . . . 311<br/>15.5 The Instrumental Variables Methods . . . . . . . . . . . . . . . . . . 318<br/>15.6 Tests for the Number of Cointegrating Vectors . . . . . . . . . . . . . 322<br/>15.7 How Should an Estimation Method be Chosen? . . . . . . . . . . . . 324<br/>viii CONTENTS<br/>15.7.1 Are Short-Run Dynamics of Interest? . . . . . . . . . . . . . . 325<br/>15.7.2 The Number of the Cointegrating Vectors . . . . . . . . . . . 325<br/>15.7.3 Small Sample Properties . . . . . . . . . . . . . . . . . . . . . 326<br/>15.A Estimation of the Model . . . . . . . . . . . . . . . . . . . . . . . . . 327<br/>15.B Monte Carlo Integration . . . . . . . . . . . . . . . . . . . . . . . . . 332<br/>15.C Johansen's Maximum Likelihood Estimation and Cointegration Rank<br/>Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334<br/>16 PANEL AND CROSS-SECTIONAL DATA 343<br/>16.1 Generalized Method of Moments . . . . . . . . . . . . . . . . . . . . . 343<br/>16.2 Tests of Risk Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . 345<br/>16.3 Decreasing Relative Risk Aversion and Risk Sharing . . . . . . . . . . 347<br/>16.4 Euler Equation Approach . . . . . . . . . . . . . . . . . . . . . . . . 349<br/>16.5 Panel Unit Root Tests . . . . . . . . . . . . . . . . . . . . . . . . . . 350<br/>16.6 Cointegration and Panel Data . . . . . . . . . . . . . . . . . . . . . . 352<br/>A INTRODUCTION TO GAUSS 357<br/>A.1 Starting and Exiting GAUSS . . . . . . . . . . . . . . . . . . . . . . . 357<br/>A.1.1 The Windows Version . . . . . . . . . . . . . . . . . . . . . . 357<br/>A.1.2 The DOS Version . . . . . . . . . . . . . . . . . . . . . . . . . 357<br/>A.2 Running a Program Stored in a File from the COMMAND Mode . . 358<br/>A.3 Editing a File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358<br/>A.4 Rules of Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358<br/>A.4.1 Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358<br/>A.4.2 Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358<br/>A.4.3 Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359<br/>A.4.4 Symbol Names . . . . . . . . . . . . . . . . . . . . . . . . . . 359<br/>A.5 Reading and Storing Data . . . . . . . . . . . . . . . . . . . . . . . . 359<br/>A.6 Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359<br/>A.6.1 Operators for Matrix Manipulations . . . . . . . . . . . . . . . 359<br/>A.6.2 Numeric Operators . . . . . . . . . . . . . . . . . . . . . . . . 361<br/>A.7 Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362<br/>A.7.1 Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362<br/>A.7.2 Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363<br/>A.7.3 Preparing an Output File . . . . . . . . . . . . . . . . . . . . 364<br/>A.8 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364<br/>A.9 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364<br/>B COMPLEX VARIABLES, THE SPECTRUM, AND LAG OPERA-<br/>TOR 365<br/>B.1 Complex Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366<br/>B.1.1 Complex Numbers . . . . . . . . . . . . . . . . . . . . . . . . 366<br/>CONTENTS ix<br/>B.1.2 Analytic Functions . . . . . . . . . . . . . . . . . . . . . . . . 367<br/>B.2 Hilbert Spaces on C . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372<br/>B.3 Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373<br/>B.4 Lag Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376<br/>C ANSWERS TO SELECTED QUESTIONS 379</p><p></p><p></p><p></p>

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关键词：econometrics Econometric Structural MacroEcon macroeco Random

 haoshu haoren!!!

xixie

Thanks.

Masao Ogaki
The Ohio State University

Kyungho Jang
The University of Alabama at Birmingham

Hyoung-Seok Lim
The Bank of Korea

First draft: May, 2000
This version: February 29, 2004

本文来自: 人大经济论坛 Gauss专版 版，详细出处参考：http://www.pinggu.org/bbs/viewth ... 1&from^^uid=12573

不错的书，先下来学习学习

谢谢分享 不错的书

謝謝樓主的分享

不错不错 好啊 学习了

It's good for me. Thanks~

非常感谢楼主~~~~