Logistic regression models and other econometric methods for estimating relationships and causal linkages between a categorical dependent variable and independent variables are important tools and techniques to explore variety of social and economic conditions. In this simple tutorial, we will explain to show when to use a given regression model for a given situation with Stata as a main software and sometimes Eviews.

Logistic Regression Model is applied to analyse the effect of some independent variables on the likelihood of favorable outcome against the non-favorable in a binary dependent variable (binomial and dichotomous variable). It means when the dependent variable in a regression model has two possible values like (Yes, No) or (1 , 0), then we use Logistic Regression. The logistic regression model can be used to estimate the effect of independent variables on the likelihood of outcomes in form of coefficients or odds ratio. The reporting objective of coefficients and odds ratio are different in nature, though.

Using Stata for Logistic Regression Models

Click File, Open and locate to open data file

Click on Statistic, Click on Binary outcomes

To estimate coefficients, Click on Report Outcomes

To estimate Odds Ratios, click on Odds Ratio

Follow the windows, and select DepVar and IndepVars

Click on options, select any and click on OK to finish.

Multinomial Logistic Regression Models is used to analyse the effect of some independent variables on the likelihood of favorable outcome against the non-favorable in a categorical dependent variable (multiple categories where each category is taken as a binary outcome). The multinomial logistic regression model can be used to estimate the effect of independent variables on the likelihood of outcomes in form of coefficients or odds ratio. The reporting objective of coefficients and odds ratio are different in nature, though.

Use Stata for Multinomial Logistic Regression Models

Click File, Open and locate to open data file

Click on Statistic, Click on Categorical outcomes

Then click on Multinomial Logistic Regression

To estimate, select the DepVar and IndepVar

Click on Options, select relevant options

To finish the estimation, click on OK to finish.

Ordered Logistic Regression Models is used to analyse the effect of some independent variables on the likelihood of favorable outcome against the non-favorable in a categorical dependent variable where the categories can be ordered. The ordered logistic regression model is used to estimate the effect of independent variables on the likelihood of outcomes where the odds of occurrences for outcomes is rank-able. The reporting objective of coefficients and odds ratio are different in nature, though but the estimation itself remains very important.

Using Stata for Ordered Logistic Regression Models

Click File, Open and locate to open data file

Click on Statistic, Click on Ordinal outcomes

Then click on Ordered Logistic Regression

To estimate, select the DepVar and IndepVar

Click on Options, select relevant options

To finish the estimation, click on OK to finish.

Panel Data Logistic Regression Models is used to analyse the effect of some independent variables on the likelihood of favorable outcome against the non-favorable in a binary or binomial variable . The ordered logistic regression model for panel data is used to estimate the effect of independent variables on the likelihood of outcomes on a panel structure of data. The reporting objective of coefficients and odds ratio are different in nature, though but the estimation itself remains very important because the variations are over t across i..

Using Stata for Ordered Logistic Regression Models

Click File, Open and locate to open data file

Click on Statistic, Click on Longitudinal/panel data

Then click on Binary Outcomes

Then click on Logistic Regression (FE, RE etc)

Select your depvar and indepvar and other options

To finish the estimation, click on OK.

Enroll for Econometrics using Stata

Our recommended Logistic Regression Models book is by Joseph Hilbe. You can buy it here. We will provide a copy in PDF directly rented for you from the publisher for use in the courses. You can enroll for our courses here.

Learn structural equation modeling using SPSS AMOS in private and instructor led online courses at AnEc Center for Econometrics Research.

The course aims to develop a strong foundation of the theory and application of modeling relationships between observed and unobserved variables, path analysis and confirmatory factor analysis using structural equation modeling with SPSS and AMOS.

Structural Equation Modeling using SPSS AMOS is advanced level research course aiming to train PhD and MS students in Economics, Business, Finance and Social Sciences to develop modeling skills for analysis of latent variables and observed variables with multi-equation and endogenous covariates. The course also aims to use SPSS and AMOS at advanced level.

Understanding the theory and practice of SEM

Developing the skills of modeling latent variables

Analysis of data using complex path analysis

Using SPSS and AMOS at advanced level

Writing effective research reports and thesis

Structural Equation Modeling using SPSS AMOS is an online, private and instructor led course from AnEc Center for Econometrics Research led by Professor Anees Muhammad since 2010.

The course is designed to help the students in Economics, Finance, Business, Management, Psychology, Political Sciences, Social Sciences and Healthcare pursuing PhD and MS research. The course can be enrolled in private or in group. Private courses are recommended for students who need to use SEM during their writing of PhD thesis.

Recommended for students writing PhD Thesis

$650

For 2 Months Course

100% practical based lessons

Detailed lessons on theory

Complete supervision of thesis

Recommended for those who wish to master the use of SEM

$450

For 2 Months Course

100% practical demosntration

Fully interactive video conference

Coursework based assessment

STRUCTURAL EQUATION MODELING USING SPSS AMOS

These are only selected topics and we will cover the contents in more details so specific list of topics for each lessons and practical demonstration of SEM using SPSS AMOS

All certificates from AnEc Center for Econometrics Research are digitally signed can be verified online through the specific link given to the candidates or manually through emailing us back

All certificate courses bear a verifiable certificates personally signed by the instructors and managing director of AnEc Center for Econometrics Research

The certificates can be digitally connected to our LinkedIn accounts and will display on your LinkedIn profiles for more international value.

All certificates are issued digitally and bear a digital signature, verification code from Accredible and AnEc Center for Econometrics Research, Attock.

Personalized contents

Advanced course contents

Verifiable Certificates

One to one video conference

Completely Privately Discussions

AnEc Membership

100% practical demos

Weekly Live Lectures

Paid freelance projects

Instant Feedback and Answers

Each lecture is recorded

Research co-authorship

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As a member of Data Science Central (DSC), American Economic Association (AES), Royal Economic Society (RES), International Health Economics Association (iHEA) and The Econometrics Society, I have been working closely with top academics in Economics, Econometrics, Statistics and Research Methods. Also, I am providing supervision in Applied Econometrics and Statistics to PhD candidates in Project Management, Business Management, Finance, Corporate Governance and Social Sciences.

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I have a teaching and academic research experience of more than 11 years at a QS Ranked University. I teach modules in Economics, Statistics, Econometrics and Quantitative Analysis. Key themes and topics of my teaching are Qualitative Data Analysis, Factor Analysis, Principle Component Analysis, Power and Sample Size determination for Survival Studies, Analysis of Open ended surveys and interviews, Multivariate Time Series techniques in VAR/VECM, VARX, SVAR, Multivariate GARCH, ARDL and Bayesian Multivariate Time Series Methods. So far, more than 70 PhD and MS/MRes candidates completed their courses in Applied Econometrics and Applied Statistics under my supervision.

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Regards

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Assistant Professor

Founder Econometricians Club

Founder AnEconomist

Founder Stata Pro Help

Muhammad Anees

Assistant Professor

Founder Econometricians Club

Founder AnEconomist

Founder Stata Pro Help

Statistics for Business and Management professionals and academic researchers is of critical importance. Although there are specialized courses in Statistics for the students of Business and Management but none of the courses offer specialization in either, Stata, Eviews or SPSS and other computer packages. The current course introduces the application of Stata, Eviews and SPSS and other computer application while emphasis is laid on the development of theoretical background of the learners as well as with strong application of the techniques in Statistics for Business. The contents of the course offered to students include (but are not limited) to:

Basic Data Management and Presentation

Hypothesis Testing and Research Problems

Causal Relationships, Regression Analysis and Applications

Qualitative Data Analysis for Binary and Multi-chotomous variables

Also the students of the course would be provided additional support via other mediums like phone, SMS, email beside the traditional and favorite WizIQ!

This course will benefit the students of MBA, BBA and Doctorate students in Business and Management courses have to study and develop the skills of Quantitative Data Analysis. This course will master these students in their Statistical courses.

Course Highlights

Apply Statistical Techniques to real world data

Apply Data Analysis to relevant research problems

Wide range of contents which are usually required in broader contexts

The course package:

15 LIVE online classes

Duration: Two to Four Weeks (Negotiable)

All Days: 0500 PM to 1000 PM GMT (1000 PM to 0200 AM Pakistan Times)

The course outline:

Deterministic Data and Random Data

Multivariate Tables, Scatter Plots and D Plots

Measures of Association for Continuous Variables

Measures of Association for Ordinal Variables

Measures of Association for Nominal Variables

Estimating Data Parameters

Parametric Tests of Hypotheses

Non-Parametric Tests of Hypotheses

Statistical Classification

Multiple Regression

General Linear Regression Model

General Linear Regression in Matrix Terms

Multiple Correlation

Inferences on Regression Parameters

ANOVA and Extra Sums of Squares

Polynomial Regression and Other Models

Building and Evaluating the Regression Model

Principal Components

Dimensional Reduction

Principal Components of Correlation Matrices

Factor Analysis

Survival Analysis

The Exponential Model

The Weibull Model

The Cox Regression Model

Recommended books for this course:

Probability and Statistics, Fourth Edition by Morris H. DeGroot & Mark J. Schervish

SPSS for Introductory Statistics Second Edition by Morgan et al

A First Course in Business Statistics, Eight Edition by JAMES T. McCLAVE, P. GEORGE BENSON & TERRYSlNClCH

Note: Students must purchase these books themselves. These are not available as part of the course. Discount coupons may be asked from the teacher who would be happy to share if any available right now.

In this simple outline on the issue of commonly raised douts on how to select an economic model, I would like to sum the issue in this 300 words article. Econometric Models Selection is one key problem for all. The model selection is based on the type of data and type of variables. Here, we mention an order following which helps an econometric model strategy development for us. We assume to use Panel data to develop econometric model selection strategy. Initially, to determine the right set of models, the nature of data structure for the panel data follows like this: A. Check data structure. 1. if N>T, then follows B series of steps. 2. If T>N, then follow the steps in C series.

If we assume the panel data has N sufficiently greater than T, then simple panel data or instrumental/GMM models for panel data can be achieved using the following order. This helps in Econometric Models selection looking into the key steps of the econometric analysis of the given panel data.

For Case 1, follow these steps.

B1. The first stepOLS

B2. RE/FE

B3. Hausman

B4. Assumptions of RE or FE from B3.

B5. Endogeneity Tests

B6. Instrumental Regression

B7. GMM if seens dynamics/autocorrelation in B4.

If the data at hands is such that T is sufficiently larger than N, the panel time series structure will be evidenced and hence our econometric models selection strategy will follow the following procedures. The unit roots and cointegration is for panel time series data which are also sometimes considered as longer time panel data or longer panel data. In such cases, the time series properties in estimators are heavily observed and hence the steps to select an econometric models selection strategy will include these.

For Case 2, follow these steps

C1. Unit Root

C2. Cointegration

C3. VECM

If you have on entity (N=1) like one country, one company, one person etc and you collect data about this entity for many time periods (T=many), it is called Time Series Data. For example if we download data on GDP, Consumption, Investment, Savings, Exports and Imports for from world bank indicators (data.worldbank.org) for Pakistan only and 30 year, this will be N=Pakistan and T=(1970 to 2000) which defines the data is time series data.

If you many entities (N=many) like 10 countries, 100 companies and 1000 persons etc and you collect data about these entities for one time period (T=1), then it is called cross sectional data. Similar to earlier example of (World Bank Data), if we download data for 50 countries for only year 2010, then it becomes cross section data. For example, if I collect data for GDP, Consumption, Investment, Savings, Exports and Imports for Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Antigua and Barbuda, Arab World , Argentina , Armenia , Aruba , Australia , Austria , Azerbaijan , Bahamas, The , Bahrain , Bangladesh , Barbados , Belarus , Belgium , Belize , Benin , Bermuda , Bhutan, Bolivia , Bosnia and Herzegovina , Botswana , Brazil , British Virgin Islands, Brunei Darussalam , Bulgaria, Burkina Faso for the year 2010, then it can be cross sectional data.

If you have many entities (N=many) and collect data about these many entities for many time periods (T=many), it is called Cross Sectional Time Series data. So, if we collect data onGDP, Consumption, Investment, Savings, Exports and Imports for Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Antigua and Barbuda, Arab World , Argentina , Armenia , Aruba , Australia , Austria , Azerbaijan , Bahamas, The , Bahrain , Bangladesh , Barbados , Belarus , Belgium , Belize , Benin , Bermuda , Bhutan, Bolivia , Bosnia and Herzegovina , Botswana , Brazil , British Virgin Islands, Brunei Darussalam , Bulgaria, Burkina Faso for the year 2000 to 2010, it will be panel data. Note, panel data might include T>N or N>T so selection of a relevant method can decided here:

Note panel data is what entities remains same over the time periods. Logitudinal data might contain some entities to appear only in one or the other time periods.