Interactive Monte Carlo Experimentation in Econometrics Using PcNaive
by Doornik, J.A. and Hendry, D.F., (2001)

Publisher: Timberlake Consultants Press
ISBN 0-9533394-8-3
Pages: 189 pages
Price: £25.00 + p&p

Contents

Table of Contents
Book Order Form

Table of Contents

1 Introduction to PcNaive

1.1 General information
1.2 The special features of PcNaive
1.3 An overview of PcNaive
1.4 Documentation conventions
1.5 Using PcNaive documentation
1.6 Citation
1.7 World Wide Web
1.8 Installation and run-time issues

2 The Data Generation Processes and Models of PcNaive
2.1 AR(1) DGP
2.2 Static DGP
2.3 PcNaive and General DGP

Part II: PcNaive Tutorials

3 Introduction to Monte Carlo Experimentation

3.1 PcNaive
3.2 Monte Carlo
3.3 The data generation process
3.4 Simulation methods
3.5 The output of PcNaive

4 Tutorial for an IN[mu,sigma^2] Process

4.1 Introduction
4.2 Starting PcNaive
4.3 Designing the IN[mu,sigma^2] experiment
4.4 Saving the IN[mu,sigma^2] experiment
4.5 Running the IN[mu,sigma^2] experiment
4.6 Output from the IN[mu,sigma^2] experiment
4.7 Extended IN[mu,sigma^2] experiment
4.8 Graphical output

5 Tutorial on the Static DGP

5.1 Introduction
5.2 Designing the Static experiment

6 Tutorial for the AR(1) DGP

6.1 Introduction
6.2 Designing the AR(1) experiment
6.3 Recursive Monte Carlo

7 Tutorial on the PcNaive DGP

7.1 Introduction .
7.2 Example 1: AR(1) process
7.3 Example 2: unit roots
7.4 Example 3: autoregressive error and asymptotic analysis
7.5 Example 4: simultaneity and inter-estimator comparison
7.6 Example 5: cointegration analysis with dummies
7.7 Example 6: structural breaks

8 Tutorial on the General DGP

8.1 Introduction
8.2 Implementing the DGP
8.3 Specifying the equilibrium correction model

9 Tutorial on the PcNaive Code

9.1 Introduction
9.2 Program and class structure
9.3 A generated program
9.4 Extending PcNaive

Part III: Learning Econometrics Using PcNaive

10 Introduction

11 Elementary Econometrics

11.1 The concept of variation
11.2 Shapes of some statistical distributions
11.3 How sample size affects distributional shape
11.4 Comparing t and normal
11.5 Convergence to normality: A Central Limit theorem at work
11.6 Bivariate regression theory really works
11.7 The accuracy of estimated coefficient standard errors
11.8 Fixed versus stochastic regressors
11.9 Omitted variables: compounding bias and variance
11.10 The effects of non-normal equation errors
11.11 The effects of data measurement errors

12 Intermediate econometrics

12.1 The impact of time: bias in autoregressive model estimation
12.2 Autocorrelated errors in regression equations
12.3 Inter-estimator comparisons: OLS and IV in a simultaneous system
12.4 The theory of Monte Carlo
12.5 Recursion in Monte Carlo applications
12.6 Test power: the impact of increasing sample size
12.7 The impact of dynamics on Chow test rejection frequencies
12.8 Nonsense regressions: the impact of evolution over time
12.9 Testing for unit roots
12.10 Testing for cointegration
12.11 Invalid weak exogeneity in a cointegration equation

13 Advanced econometrics

13.1 The role of asymptotic distribution theory in Monte Carlo
13.2 Distributions of inconsistent estimators
13.3 The impacts of structural breaks on econometric modelling
13.4 Testing the Lucas critique
13.5 Encompassing and non-nested hypothesis tests
13.6 Non-existence of moments
13.7 Cointegration analysis

Part IV: Monte Carlo Theory

14 Monte Carlo Methods

14.1 Stochastic solutions to deterministic problems
14.2 Distribution sampling
14.3 Sophisticated Monte Carlo
14.4 Invariance
14.5 Asymptotic analysis
14.6 Recursive Monte Carlo
14.7 Experimental design
14.8 Post-simulation analysis
14.9 Random number generation

15 Response surfaces

15.1 Introduction
15.2 The general approach
15.3 Experimental design, simulation, and post-simulation analysis
15.4 Heteroscedasticity
15.5 Testing the statistical adequacy of response surfaces
15.6 Numerical accuracy of response surfaces
15.7 Response surface formulations
15.8 Simpler forms of response surfaces
15.9 Conclusion

16 Asymptotic Analysis

16.1 Introduction
16.2 The DGP for asymptotic analysis
16.3 Companion form
16.4 Asymptotic moments
16.5 Asymptotic statistics

References
Author Index
Subject Index