PoE with R
.
1
Introduction
1.1
The RStudio Screen
1.1.1
The
Script
, or
data view
window
1.1.2
The
console
, or
output
window
1.2
How to Open a Data File
1.3
Creating Graphs
1.4
An R Cheat Sheet
2
The Simple Linear Regression Model
2.1
The General Model
2.2
Example: Food Expenditure versus Income
2.3
Estimating a Linear Regression
2.4
Prediction with the Linear Regression Model
2.5
Repeated Samples to Assess Regression Coefficients
2.6
Estimated Variances and Covariance of Regression Coefficients
2.7
Non-Linear Relationships
2.8
Using Indicator Variables in a Regression
2.9
Monte Carlo Simulation
3
Interval Estimation and Hypothesis Testing
3.1
The Estimated Distribution of Regression Coefficients
3.2
Confidence Interval in General
3.3
Example: Confidence Intervals in the
food
Model
3.4
Confidence Intervals in Repeated Samples
3.5
Hypothesis Tests
3.6
The
p
-Value
3.7
Testing Linear Combinations of Parameters
4
Prediction, R-squared, and Modeling
4.1
Forecasting (Predicting a Particular Value)
4.2
Goodness-of-Fit
4.3
Linear-Log Models
4.4
Residuals and Diagnostics
4.5
Polynomial Models
4.6
Log-Linear Models
4.7
The Log-Log Model
5
The Multiple Regression Model
5.1
The General Model
5.2
Example: Big Andy’s Hamburger Sales
5.3
Interval Estimation in Multiple Regression
5.4
Hypothesis Testing in Multiple Regression
5.5
Polynomial Regression Models
5.6
Interaction Terms in Linear Regression
5.7
Goodness-of-Fit in Multiple Regression
6
Further Inference in Multiple Regression
6.1
Joint Hypotheses and the F-statistic
6.2
Testing Simultaneous Hypotheses
6.3
Omitted Variable Bias
6.4
Irrelevant Variables
6.5
Model Selection Criteria
6.6
Collinearity
6.7
Prediction and Forecasting
7
Using Indicator Variables
7.1
Factor Variables
7.2
Examples
7.3
Comparing Two Regressions: the Chow Test
7.4
Indicator Variables in Log-Linear Models
7.5
The Linear Probability Model
7.6
Treatment Effects
7.7
The Difference-in-Differences Estimator
7.8
Using Panel Data
7.9
R Practicum
7.9.1
Extracting Various Information
7.9.2
ggplot2
, An Excellent Data Visualising Tool
8
Heteroskedasticity
8.1
Spotting Heteroskedasticity in Scatter Plots
8.2
Heteroskedasticity Tests
8.3
Heteroskedasticity-Consistent Standard Errors
8.4
GLS: Known Form of Variance
8.5
Grouped Data
8.6
GLS: Unknown Form of Variance
8.7
Heteroskedasticity in the Linear Probability Model
9
Time-Series: Stationary Variables
9.1
An Overview of Time Series Tools in
R
9.2
Finite Distributed Lags
9.3
Serial Correlation
9.4
Estimation with Serially Correlated Errors
9.5
Nonlinear Least Squares Estimation
9.6
A More General Model
9.7
Autoregressive Models
9.8
Forecasting
9.9
Multiplier Analysis
10
Random Regressors
10.1
The Instrumental Variables (IV) Method
10.2
Specification Tests
11
Simultaneous Equations Models
12
Time Series: Nonstationarity
12.1
AR(1), the First-Order Autoregressive Model
12.2
Spurious Regression
12.3
Unit Root Tests for Stationarity
12.4
Cointegration
12.5
The Error Correction Model
13
VEC and VAR Models
13.1
VAR and VEC Models
13.2
Estimating a VEC Model
13.3
Estimating a VAR Model
13.4
Impulse Responses and Variance Decompositions
14
Time-Varying Volatility and ARCH Models
14.1
The ARCH Model
14.2
The GARCH Model
15
Panel Data Models
15.1
Organizing the Data as a Panel
15.2
The Pooled Model
15.3
The Fixed Effects Model
15.4
The Random Effects Model
15.5
Grunfeld’s Investment Example
16
Qualitative and LDV Models
16.1
The Linear Probability Model
16.2
The Probit Model
16.3
The Transportation Example
16.4
The Logit Model for Binary Choice
16.5
Multinomial Logit
16.6
The Conditional Logit Model
16.7
Ordered Choice Models
16.8
Models for Count Data
16.9
The Tobit, or Censored Data Model
16.10
The Heckit, or Sample Selection Model
References
Published with bookdown
Principles of Econometrics with
\(R\)
Principles of Econometrics with
\(R\)
Constantin Colonescu
2016-09-01
.
