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25. Python - multiple regression

25. Python - multiple regression

24 Python simple regression model

24 Python simple regression model

22 Python correlation matrix

22 Python correlation matrix

23 Python crosstables

23 Python crosstables

21, Python - t-test of difference in two means

21, Python - t-test of difference in two means

20. Python - barcharts

20. Python - barcharts

19. Python - boxplots

19. Python - boxplots

18. Python - histograms

18. Python - histograms

17. Python - lineplot and scatterplot

17. Python - lineplot and scatterplot

16. Python - subsetting data

16. Python - subsetting data

15. Python - explore and clean the data

15. Python - explore and clean the data

14. Python - importing data from csv txt and excel xlsx files

14. Python - importing data from csv txt and excel xlsx files

13. Python - get user input with the input function

13. Python - get user input with the input function

12. Python - variables

12. Python - variables

11. Python - make and save plots

11. Python - make and save plots

3. Python - install VS Code and extensions

3. Python - install VS Code and extensions

4. Python - select project folder, make and run a python file

4. Python - select project folder, make and run a python file

5. Python - run code in an interactive window in VS Code

5. Python - run code in an interactive window in VS Code

6. Python - restart the kernel in case of program freeze

6. Python - restart the kernel in case of program freeze

7. Python - commenting code

7. Python - commenting code

8. Python - functions and importing modules

8. Python - functions and importing modules

9. Python - install modules

9. Python - install modules

10. Python - making random numbers and number sequences

10. Python - making random numbers and number sequences

2. Python - install python

2. Python - install python

30 R multiple regression

30 R multiple regression

29 R simple regression

29 R simple regression

28 R crosstables

28 R crosstables

27 R correlation matrix

27 R correlation matrix

26 R t test of difference in means

26 R t test of difference in means

25 R barplots

25 R barplots

24 R boxplots

24 R boxplots

23 R histograms

23 R histograms

22 R the plot function

22 R the plot function

21 R slicing or filtering data

21 R slicing or filtering data

20 get help on functions

20 get help on functions

19 R metrics functions

19 R metrics functions

18 R explore data and remove missing data

18 R explore data and remove missing data

17 R read excel files

17 R read excel files

16 R load csv or txt files

16 R load csv or txt files

15 R install and load packages

15 R install and load packages

14 R get numerical user input

14 R get numerical user input

13 R variables and vector variables

13 R variables and vector variables

12 R Studio clear console and environment

12 R Studio clear console and environment

11 R Studio organize files and workspace

11 R Studio organize files and workspace

10 R Studio opening R studio and setting working directory

10 R Studio opening R studio and setting working directory

9 R Studio installing

9 R Studio installing

8 programming in base R make and save a plot

8 programming in base R make and save a plot

6 program in base R comments

6 program in base R comments

7 program in base R generate random draws and sequences

7 program in base R generate random draws and sequences

5 programming in base R working with files

5 programming in base R working with files

4 programming in base R variable and assignment operator

4 programming in base R variable and assignment operator

3 program directly in R operators

3 program directly in R operators

2 install R

2 install R

1 introduction to r

1 introduction to r

Exercise 2.2

Exercise 2.2

Exercise 2.1

Exercise 2.1

MySQL - How to make tables

MySQL - How to make tables

MySQL: Make new database schema

MySQL: Make new database schema

MySQL Installation on Windows

MySQL Installation on Windows

python - multiple regression with statsmodels

python - multiple regression with statsmodels

python accessing regression output

python accessing regression output

python how to deal with missing numbers NaNs

python how to deal with missing numbers NaNs

python - statsmodels - importance of adding a constant

python - statsmodels - importance of adding a constant

python - statsmodels - simple regression

python - statsmodels - simple regression

python - correlation analysis

python - correlation analysis

python - statsmodels - overview

python - statsmodels - overview

python - pandas

python - pandas

python - matplotlib

python - matplotlib

python - numpy

python - numpy

Python - text files

Python - text files

Python - Modules

Python - Modules

Python - classes and objects

Python - classes and objects

Python - functions II

Python - functions II

Python - functions

Python - functions

Python - sets

Python - sets

Python - Dictionaries

Python - Dictionaries

Python Dictionaries

Python Dictionaries

1.3 Update Anaconda and installed packages

1.3 Update Anaconda and installed packages

1.2 Install packages by conda install or pip install

1.2 Install packages by conda install or pip install

1.1 Install Anaconda for Python

1.1 Install Anaconda for Python

lecture05 - Stata - cat food price elasticity from regression

lecture05 - Stata - cat food price elasticity from regression

p-values and how to find them

p-values and how to find them

11.1.2 Testing for Cointegration

11.1.2 Testing for Cointegration

11.4.2 Models of Volatility Clustering - GARCH

11.4.2 Models of Volatility Clustering - GARCH

11.4.1 Models of Volatility Clustering - ARCH

11.4.1 Models of Volatility Clustering - ARCH

11.1.1 Cointegration

11.1.1 Cointegration

11.3 Volatility Clustering

11.3 Volatility Clustering

11.2 Vector Error Correction Model VECM

11.2 Vector Error Correction Model VECM

10.3 Other Models of Trends and Orders of Integration

10.3 Other Models of Trends and Orders of Integration

10.2.2 Direct Forecasts

10.2.2 Direct Forecasts

10.2.1 Iterated Forecasts

10.2.1 Iterated Forecasts

10.1 Vector Autoregressions

10.1 Vector Autoregressions

10.2 Multiperiod forecasts

10.2 Multiperiod forecasts

9.6 Pseudo Out of Sample Forecasts

9.6 Pseudo Out of Sample Forecasts

9.5.2 Testing for a break at an unknown break date

9.5.2 Testing for a break at an unknown break date

9.5.1 Testing For a Break At a Known Break Date

9.5.1 Testing For a Break At a Known Break Date

9.5 Non Stationarity - Breaks

9.5 Non Stationarity - Breaks

9.4.2.2 Dickey-Fuller Test - AR(p) model

9.4.2.2 Dickey-Fuller Test - AR(p) model

9.4.2.1 Dickey-Fuller Test - AR1 Model

9.4.2.1 Dickey-Fuller Test - AR1 Model

9.4.2 Dickey-Fuller Test

9.4.2 Dickey-Fuller Test

Stata 39 - Working With Dates and Creating a Dummy Variable Based on Dates

Stata 39 - Working With Dates and Creating a Dummy Variable Based on Dates

Stata 42 - Transform a Variable

Stata 42 - Transform a Variable

Stata 41 - Get Returns From Prices

Stata 41 - Get Returns From Prices

Stata 40 - Find the Correlation of Two or More Variables

Stata 40 - Find the Correlation of Two or More Variables

9.4.1 Random Walk Model of a Trend

9.4.1 Random Walk Model of a Trend

9.4 Non stationarity - Trends

9.4 Non stationarity - Trends

9.3 Time Series - Stationarity

9.3 Time Series - Stationarity

9.2.2 ADL - Lag Length Selection Using Information Criteria

9.2.2 ADL - Lag Length Selection Using Information Criteria

9.2 Autoregressive Distributed Lag - Multiple Predictors

9.2 Autoregressive Distributed Lag - Multiple Predictors

9.2.1 General ADL - Model Specification

9.2.1 General ADL - Model Specification

9.1.3 ADL Example - Forecasting GDP Growth Rates

9.1.3 ADL Example - Forecasting GDP Growth Rates

9.1.2 Granger Causality Test

9.1.2 Granger Causality Test

9.1.1 ADL - Model Specification

9.1.1 ADL - Model Specification

9.1 Autoregressive Distributed Lag - One Additional Regressor

9.1 Autoregressive Distributed Lag - One Additional Regressor

Stata 36 - Merge two data sets

Stata 36 - Merge two data sets

Stata 38 - Append two data sets

Stata 38 - Append two data sets

Stata 37 - count number of instances or missing data

Stata 37 - count number of instances or missing data

Stata 35 - J-test of overidentifying instrumental variables

Stata 35 - J-test of overidentifying instrumental variables

Stata 34 - Instrumental variable regression

Stata 34 - Instrumental variable regression

Stata 33 Only keep some variables with the keep function

Stata 33 Only keep some variables with the keep function

Stata 32 Only keep some observations with the keep function

Stata 32 Only keep some observations with the keep function

Stata 31 Get an overview of a variables values with the tab function

Stata 31 Get an overview of a variables values with the tab function

Stata 30 Download Financial Data with getsymbols

Stata 30 Download Financial Data with getsymbols

8.3.3 Time Series - Example - Forecasting GDP Growth Rates - Information Criteria

8.3.3 Time Series - Example - Forecasting GDP Growth Rates - Information Criteria

8.3.2 Time Series - Lag Length Selection Using Information Criteria

8.3.2 Time Series - Lag Length Selection Using Information Criteria

z-tables - two versions

z-tables - two versions

8.3.1 Time Series - Autoregressions - AR(p) Model - Model Specification

8.3.1 Time Series - Autoregressions - AR(p) Model - Model Specification

8.1 Time Series - Basic Concepts and Terminology

8.1 Time Series - Basic Concepts and Terminology

8.2.6 Time Series - Example - Forecasting of GDP Growth Rates

8.2.6 Time Series - Example - Forecasting of GDP Growth Rates

8.2.5 Time Series - Forecast Intervals

8.2.5 Time Series - Forecast Intervals

8.2.4 Time Series - Mean Squared Forecast Error - MSFE

8.2.4 Time Series - Mean Squared Forecast Error - MSFE

8.2.3 Time Series - Forecasts and Forecast Errors

8.2.3 Time Series - Forecasts and Forecast Errors

8.2.2 Time Series - Estimation

8.2.2 Time Series - Estimation

8.2.1 Time Series - Model Specification

8.2.1 Time Series - Model Specification

8.2 Time Series - Autoregressions - AR1 model

8.2 Time Series - Autoregressions - AR1 model

7.1.3.2 Fuzzy Regression Discontinuity

7.1.3.2 Fuzzy Regression Discontinuity

7.1.3.1 Sharp Regression Discontinuity

7.1.3.1 Sharp Regression Discontinuity

7.1.3 Quasi Experiments and Regression Discontinuity

7.1.3 Quasi Experiments and Regression Discontinuity

7.1.2 Quasi Experiments and Instrumental Variable Regression

7.1.2 Quasi Experiments and Instrumental Variable Regression

7.1.1 Difference in Difference Estimator with Additional Regressors

7.1.1 Difference in Difference Estimator with Additional Regressors

7.1 Quasi Experiments - continued

7.1 Quasi Experiments - continued

6.2.1 Quasi Experiments - Differences in Differences

6.2.1 Quasi Experiments - Differences in Differences

6.1.3 Experiments - Threats to Internal Validity

6.1.3 Experiments - Threats to Internal Validity

6.2 Quasi Experiments

6.2 Quasi Experiments

6.1.4 Experiments - Threats to External Validity

6.1.4 Experiments - Threats to External Validity

6.1 Experiments - overview

6.1 Experiments - overview

6.1.2 Experiments - Difference Estimator with Additional Regressors

6.1.2 Experiments - Difference Estimator with Additional Regressors

6.1.1 Experiments - Differences Estimator

6.1.1 Experiments - Differences Estimator

5.4.2 J-test

5.4.2 J-test

5 5 General Instrumental Variable Regression

5 5 General Instrumental Variable Regression

5 6 Instrumental variables assumptions

5 6 Instrumental variables assumptions

5 4 3 Example estimating the price elasticity of demand for cigarettes

5 4 3 Example estimating the price elasticity of demand for cigarettes

Stata 29 Reporting results from several models to a table with extra stats

Stata 29 Reporting results from several models to a table with extra stats

Stata 28 test joint hypothesis with test and testparm functions

Stata 28 test joint hypothesis with test and testparm functions

Stata 27 Panel data Clustered robust standard errors

Stata 27 Panel data Clustered robust standard errors

Stata 26 Panel data entity fixed effects and time fixed effects

Stata 26 Panel data entity fixed effects and time fixed effects

Stata 25 Panel data entity fixed effects and no time fixed effects

Stata 25 Panel data entity fixed effects and no time fixed effects

Stata 24 Panel data Define state and time variables

Stata 24 Panel data Define state and time variables

Stata 20 Logarithmic transforms of variables by use of a for loop

Stata 20 Logarithmic transforms of variables by use of a for loop

Stata 23 Panel data Encode string variable to be used in panel data regression

Stata 23 Panel data Encode string variable to be used in panel data regression

Stata 21 Transform numbers stored as text to numbers

Stata 21 Transform numbers stored as text to numbers

Stata 22 How to label a variable

Stata 22 How to label a variable

5 3 1 IV Regression 1 endogenous regressor 1 instrument and control variables

5 3 1 IV Regression 1 endogenous regressor 1 instrument and control variables

5 4 1 IV Regression 1 endogenous regressor multiple instruments and control variables

5 4 1 IV Regression 1 endogenous regressor multiple instruments and control variables

5 3 2 Price elasticity of demand for cigarettes with one endogenous one instrument and one control v

5 3 2 Price elasticity of demand for cigarettes with one endogenous one instrument and one control v

Stata 19 test combinations of coefficient values

Stata 19 test combinations of coefficient values

Stata 18 change the value of the null hypothesis for coefficient tests

Stata 18 change the value of the null hypothesis for coefficient tests

Stata 17 change confidence interval in regression

Stata 17 change confidence interval in regression

Stata 16 regression with robust standard errors

Stata 16 regression with robust standard errors

Stata 15 simple regression

Stata 15 simple regression

Stata 14 make dummy variables

Stata 14 make dummy variables

Stata 13 describe your data

Stata 13 describe your data

Stata 11 manually insert data

Stata 11 manually insert data

Stata 12 summary statistics conditioned on the value of another variable

Stata 12 summary statistics conditioned on the value of another variable

Stata 10 import csv data file

Stata 10 import csv data file

Stata 09 import xls excel data file

Stata 09 import xls excel data file

Stata 08 import dta data file

Stata 08 import dta data file

Stata 04b how to create a do file

Stata 04b how to create a do file

5 2 3 Example estimating the price elasticity of demand for cigarettes

5 2 3 Example estimating the price elasticity of demand for cigarettes

Stata 07 semicolon delimiter

Stata 07 semicolon delimiter

Stata 06 log files

Stata 06 log files

Stata 05 comment your code

Stata 05 comment your code

Stata 04 do files

Stata 04 do files

Stata 03 filetypes

Stata 03 filetypes

Stata 02 working directory

Stata 02 working directory

Stata 01 user interface

Stata 01 user interface

5 2 2 IV regression, 1 endogenous regressor, one instrument estimation

5 2 2 IV regression, 1 endogenous regressor, one instrument estimation

p value from t table

p value from t table

5 2 1 IV regression, 1 endogenous and 1 instrument overview

5 2 1 IV regression, 1 endogenous and 1 instrument overview

5 1 Instrumental variables intro

5 1 Instrumental variables intro

4 6 Serial Correlation in X and u in panel data

4 6 Serial Correlation in X and u in panel data

4 5 Fixed Effects Regression Assumptions

4 5 Fixed Effects Regression Assumptions

4 4 Regressions with both Entity and Time Fixed Effects

4 4 Regressions with both Entity and Time Fixed Effects

4.2.1 Regressions with Entity Fixed Effects - Overview

4.2.1 Regressions with Entity Fixed Effects - Overview

4 3 Regressions with Time Fixed Effects

4 3 Regressions with Time Fixed Effects

4 2 2 Regressions with Entity Fixed Effects Estimation

4 2 2 Regressions with Entity Fixed Effects Estimation

4.1 Introduction to Panel Data Regression

4.1 Introduction to Panel Data Regression

3 4 Predictive Modelling or Forecasting

3 4 Predictive Modelling or Forecasting

3 3 External Validity

3 3 External Validity

3 2 6 2 Correlated Errors

3 2 6 2 Correlated Errors