发帖
雷达卡
Author(s): Bjørnar Karlsen Kivedal
This accessible textbook introduces the foundations of applied econometrics and statistics for undergraduate students. It covers key topics in econometrics by using step-by-step examples in Gretl and R, providing a guide to using statistical software and the tools for econometric analysis in one self-contained resource. Taking a concise, non-technical approach, the book covers topics including simple regression and hypothesis testing, multiple regression with control variables and isolating effects, instrumental variables, dummy variables, non-linear effects, probability models, heteroskedasticity, time series analysis, and other applied statistical tools such as t-tests and chi squared tests. The book uses small data sets to easily facilitate students'transition from manual statistical calculations to using and understanding statistical software, including step-by-step examples of regression analysis, as well as additional chapters to aid with econometric notation and mathematical prerequisites, and accompanying online exercises and data sets. This book will be a valuable resource for upper undergraduate students taking courses in introductory econometrics and statistics, as well as students in business administration and other fields of study in social sciences utilising quantitative methods. Graduate students may also benefit from the book.
Preface
Contents
List of Figures
List of Tables
1 Introduction
1.1 Structure of the Book
1.2 Why Gretl and R?
2 Start Using Gretl and R
2.1 Gretl
2.1.1 Installing Gretl
2.1.2 Startup and Loading of Datasets
Exporting Datasets
2.1.3 Data Visualization
2.1.4 Datasets
2.2 R and RStudio
2.2.1 Installation of R and RStudio
2.2.2 Startup and Loading Datasets
2.2.3 Some Simple R Codes
2.2.4 Loading Datasets
2.2.5 Data Visualization
3 Basic Material
3.1 Applied Statistics and Econometrics
3.2 Prerequisites
3.2.1 Summation
3.2.2 Derivation
Derivation Rules
3.2.3 Expectations
3.3 Different Summary Statistics
3.3.1 Central Tendency and Dispersion
Measures of Central Tendency
Measures of Dispersion
Central Tendency and Dispersion in Gretl
Central Tendency and Dispersion in R
3.3.2 Interval Estimate: Confidence Interval
3.3.3 Measures of Covariation: Covariance and Correlation
Example
Correlation in Gretl
Correlation in R
3.4 Probability Distributions
3.4.1 Normal Distribution
3.4.2 Standard Normal Distribution
3.4.3 Central Limit Theorem
3.4.4 t-distribution
3.4.5 Probability Distributions in Gretl
3.4.6 Probability Distributions in R
References
4 Hypothesis Testing
4.1 Formulating Hypotheses
4.2 Test Statistic and Critical Value(s)
4.2.1 Example: Height of 9 Men—Normal Distribution
4.3 Examples in Gretl and R
4.3.1 Height of 9 Men: t-Distribution
Hypothesis Testing for Men’s Height in Gretl
Hypothesis Testing for Men’s Height in R
4.3.2 Example Batteries: “Less Than” Alternative Hypothesis
Battery Example with Unknown Population Standard Deviation
Hypothesis Testing, Batteries, in Gretl
Hypothesis Testing, Batteries, in R
4.3.3 Example with a Two-Sided Hypothesis Test
Two-Tailed Hypothesis Test in Gretl
Two-Tailed Hypothesis Test in R
4.4 Hypothesis Testing for Correlation
4.5 p-Value
5 Simple Linear Regression
5.1 The Regression Line
5.1.1 A Straight Line: Math
5.1.2 A Straight Line: Regression
Example
5.1.3 Interpreting the Estimated Coefficients
Example
5.2 Simple Regression in Gretl
5.3 Simple Regression in R
5.4 Prediction
5.5 Explanatory Power of the Model
5.6 Hypothesis Testing and Confidence Intervals
5.6.1 Hypothesis Testing of the Slope Coefficient
Example
5.6.2 Confidence Interval for a Slope Coefficient
5.6.3 Hypothesis Test for the Constant Term
5.6.4 Prediction Interval
Prediction Interval in Gretl and R
5.7 Assumptions for the Regression Model
5.7.1 Graphical Assessment of the Assumptions
Graphical Assessment of the Assumptions in Gretl
Graphical Assessment of the Assumptions in R
5.8 Supplementary Material
5.8.1 Where Do the Formulas for the Coefficients Come from?
5.8.2 Calculating the Numbers from a Regression Output
Reference
6 Multiple Regression
6.1 Manual Calculation
6.2 Interpreting Estimated Parameters in Multiple Regression
6.2.1 Partial Derivatives (Mathematics)
6.2.2 Interpretation If We Have Two Explanatory Variables
6.3 Example (in Gretl and R)
6.3.1 Example: Used Car Price and Fuel Consumption
6.4 F-Test
6.4.1 Example F-Test
Eksemplet i Gretl
Example in R
6.5 Heteroskedasticity Tests
6.5.1 The Example in Gretl
6.5.2 Example in R
6.5.3 Dealing with Heteroskedasticity
6.6 Multicollinearity
6.7 Instrumental Variable Regression
6.7.1 Example Using an Instrumental Variable
Two-Stage Least Squares in Gretl
Two-Stage Least Squares in R
6.8 Omitted Variable Bias
References
7 Regression Using Dummy Variables
7.1 Simple Regression with a Dummy Variable
7.2 Generate Dummy Variables in Gretl and R
7.3 Fixed Effects
7.3.1 Example: Wage, Education, and Sector
7.3.2 Hypothesis Testing
7.4 Interaction Effects
7.4.1 Example: Wage, Education, and Sector
7.4.2 Hypothesis Testing
7.5 Both Fixed Effects and Interaction Terms
7.5.1 Example
7.5.2 Hypothesis Testing
7.5.3 Example with Wage and City
Manual F-Test
F-Test in Gretl
F-Test in R
7.6 Dummy Variable as Dependent Variable
7.6.1 Linear Probability Models
7.6.2 Probit Models
Probit Model in Gretl
Probit Model in R
7.6.3 Logit Models
Logit Models in Gretl
Logit Model i R
7.6.4 Which Probability Model to Use?
8 Non-Linear Relationships
8.1 Logarithmic Relationships
8.1.1 Lin-Log Model
8.1.2 Log-Lin Model
8.1.3 Log-Log Model
8.1.4 Predicted Y when lnY Is the Dependent Variable
8.2 Polynomial Functions
8.3 Inverse Relationships
8.4 Which Non-Linear Model Should We Choose?
8.5 Supplementary Material
8.5.1 Logarithms and Approximation of Percentage Change
8.5.2 Lin-Log Regression by Hand
Reference
9 Time Series Data
9.1 Time Series Variables and Regressions
9.1.1 Time Series in Gretl and R
9.1.2 Regression with Time Series
9.1.3 Development Over Time: Trend
Regression with Trend as an Explanatory Variable
9.1.4 Spurious Regression
9.1.5 Transforming Time Series Variables
Lagged Variables
Lagged Variables in Gretl and R
Differenced Variables
Creating Differentiated Variables in Gretl and R
Calculating Growth
Growth Variables in Gretl and R
9.2 Autocorrelation and Stationarity
9.2.1 Autoregressive Model
9.2.2 Autoregressive Model with Multiple Lags
9.2.3 AR Models in Gretl and R
9.2.4 AR Models in Gretl and R
9.2.5 Stationarity Test
ADF Test in Gretl
ADF Test in R
9.3 Seasonal Variation
9.3.1 Seasonal Variation in Gretl and R
9.3.2 AR(1) Model with Drift and Seasonal Variation
9.4 Forecasts and Model Evaluation
9.4.1 Evaluating Forecasts in Gretl and R
9.5 Testing for Autocorrelation in the Residuals
9.5.1 Testing for Autocorrelated Residuals in Gretl and R
9.6 Parameter Stability
9.6.1 Chow Test in Gretl
9.6.2 Chow Test in R
9.7 Cointegration
9.7.1 Cointegration in Gretl
9.7.2 Cointegration in R
References
10 Other Statistical Tools
10.1 T-test
10.1.1 Example
Example by Hand
Example in Gretl and R
10.1.2 T-test or Regression Model
10.2 Chi-square Test
10.2.1 Chi-square Goodness of Fit Test
10.2.2 Gretl and R
10.2.3 Example with Data
Manual Calculation
Test in Gretl
Test in R
10.2.4 Chi-square Test or Regression Analysis
10.3 Analysis of Variance
10.3.1 Example in Gretl
10.3.2 Example in R
10.3.3 Analysis of Variance or Using Dummy Variables
Bibliography
Index
Applied Statistics and Econometrics. Basic Topics and Tools with Gretl and R Bj.pdf
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