Contents
Table
of Contents
Book
Order Form
Table of Contents
Part I: PcNaive Prologue
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
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 Running the IN[mu,sigma^2] experiment
4.5
Output from the IN[mu,sigma^2] experiment
4.6 Extended
IN[mu,sigma^2] experiment
4.7 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: cointegration
- 7.5 Example 4: autoregressive error and asymptotic
analysis
7.6 Example 5: simultaneity and inter-estimator
comparison
7.7 Example 6: cointegration analysis with
dummies
7.8 Example 7: 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
- 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
|
