Public Policy Analysis MPA 404 Lecture 9. Previous Lecture  Quantitative methods for analyzing a policy.  What is intended to be done with these and.

Slides:



Advertisements
Similar presentations
Introduction to Hypothesis Testing
Advertisements

The basics of quantifying qualitative scenarios By Gerald Harris Author, The Art of Quantum Planning.
two policy debates: Should policy be active or passive?
Functions of Statistics
Linear Regression.
© 2005 The McGraw-Hill Companies, Inc., All Rights Reserved. Chapter 14 Using Multivariate Design and Analysis.
1 Reason for Forecasting and Its Techniques 2 Overview Why Forecast? An Overview of forecasting techniques Basic steps in a forecasting task.
Economics 20 - Prof. Anderson
The Role of Financial System in Economic Growth Presented By: Saumil Nihalani.
DTC Quantitative Research Methods Three (or more) Variables: Extensions to Cross- tabular Analyses Thursday 13 th November 2014.
Unit 2 – Measures of Risk and Return The purpose of this unit is for the student to understand, be able to compute, and interpret basic statistical measures.
Lecture 2 Research Questions: Defining and Justifying Problems; Defining Hypotheses.
Business Statistics - QBM117 Statistical inference for regression.
MBA & MBA – Banking and Finance (Term-IV) Course : Security Analysis and Portfolio Management Unit I: Introduction to Security Analysis Lesson No. 1.3–
Chapter 14 Inferential Data Analysis
DESIGNING, CONDUCTING, ANALYZING & INTERPRETING DESCRIPTIVE RESEARCH CHAPTERS 7 & 11 Kristina Feldner.
Business Forecasting Chapter 5 Forecasting with Smoothing Techniques.
Public Policy Analysis MPA 404 Lecture 5. Brief Summary of previous lecture  Remaining models of the Public Policy, namely the Group Theory, Elite Theory,
RESEARCH DESIGN.
LEARNING PROGRAMME Hypothesis testing Intermediate Training in Quantitative Analysis Bangkok November 2007.
Chapter 2 – Business Forecasting Takesh Luckho. What is Business Forecasting?  Forecasting is about predicting the future as accurately as possible,
Sociological Research Methods and Techniques
Benno Benno Mwitumba Ulingeta Obadia Mbamba
Understanding Statistics
Chapter 5 Demand Forecasting.
Why Normal Matters AEIC Load Research Workshop Why Normal Matters By Tim Hennessy RLW Analytics, Inc. April 12, 2005.
Ch. 2: Planning a Study (cont’d) pp THE RESEARCH PROPOSAL  In all empirical research studies, you systematically collect and analyze data 
EE325 Introductory Econometrics1 Welcome to EE325 Introductory Econometrics Introduction Why study Econometrics? What is Econometrics? Methodology of Econometrics.
Scientific Inquiry & Skills
Banking crises and recessions: What can leading indicators tell us? Dr. Martin Weale.
Educational Research: Competencies for Analysis and Application, 9 th edition. Gay, Mills, & Airasian © 2009 Pearson Education, Inc. All rights reserved.
Comm W. Suo Slide 1. Comm W. Suo Slide 2 Diversification  Random selection  The effect of diversification  Markowitz diversification.
Public Policy Analysis MPA 404 Lecture 8. Previous Lecture  We went through some of the ways that a particular public policy can have effects upon citizens.
Econometrics ECO 54 History of Economic Thought Udayan Roy.
Chapter 5 Demand Forecasting 1. 1.Importance of Forecasting  Helps planning for long-term growth  Helps in gauging the economic activity (auto sales,
Chapter 13. Some b usiness cycle facts ECON320 Prof Mike Kennedy.
Multiple Regression. Multiple Regression  Usually several variables influence the dependent variable  Example: income is influenced by years of education.
CHAPTER 12 Descriptive, Program Evaluation, and Advanced Methods.
1 SOCIAL SECURITY & THE NATIONAL ECONOMY CHAPTER SIX 6 SOCIAL SECURITY & ECONOMIC DEVELOPMENT.
Research Design. Selecting the Appropriate Research Design A research design is basically a plan or strategy for conducting one’s research. It serves.
What Do Scientists Do? Quiz 1C.
Forecasting Parameters of a firm (input, output and products)
ANOVA, Regression and Multiple Regression March
Forecasting for Water Resources Planning. Learning Objective(s):  The student will:  Understand the need for forecasts.  Be able to describe what a.
Public Policy Analysis MPA 404 Lecture 2. A brief Summary of what we learned in the previous class Definition of Public Policy Why it is difficult to.
Chapter Nine Primary Data Collection: Experimentation and
How to structure good history writing Always put an introduction which explains what you are going to talk about. Always put a conclusion which summarises.
Recent work on revisions in the UK Robin Youll Director Short Term Output Indicators Division Office for National Statistics United Kingdom.
Review of the previous lecture Exchange rates nominal: the price of a country’s currency in terms of another country’s currency real: the price of a country’s.
Lecture 8 Sections Objectives: Bivariate and Multivariate Data and Distributions − Scatter Plots − Form, Direction, Strength − Correlation − Properties.
Lecture 1 Introduction to econometrics
Review of BUSA3322 Mary M. Whiteside. Methodologies Two sample tests Analysis of variance Chi square tests Simple regression Multiple regression Time.
FORECASTIN G Qualitative Analysis ~ Quantitative Analysis.
Econ 215: Economy of Ghana National Income Accounting Lecture 2.
Lesson 3 Scientific Inquiry.
Designing & social inquiry Designing & social inquiry A Research on Protocol in the Diplomatic Corporations : The Difference between State Visits and Official.
F5 Performance Management. 2 Section C: Budgeting Designed to give you knowledge and application of: C1. Objectives C2. Budgetary systems C3. Types of.
SENG521 (Fall SENG 521 Software Reliability & Testing Preparing for Test (Part 6a) Department of Electrical & Computer Engineering,
Lecturer: Ing. Martina Hanová, PhD. Business Modeling.
Supply Chain Management for Non Supply Chain Management Professionals
Chapter 2 Doing Sociological Research
Introductory Econometrics
Chapter 1.
Brown County Financial Decision and Support Model
Chapter 17 Measurement Key Concept: If you want to estimate the demand curve, you need to find cases where the supply curve shifts.
Regression Analysis.
Path Analysis for Exploring EBM Science Frameworks
Cases. Simple Regression Linear Multiple Regression.
Analyzing and Interpreting Quantitative Data
Environmental forecasting
Presentation transcript:

Public Policy Analysis MPA 404 Lecture 9

Previous Lecture  Quantitative methods for analyzing a policy.  What is intended to be done with these and a bit of background on its development.  Frequently used quantitative techniques  Univariate, Bivariate and ANOVA techniques  Quant's and the great recession

 Regression Analysis: Probably the most widely used technique in analysis of policy. One of the main reason for this is the fact that predicting an outcome of a policy is very much in demand, and regression analysis is considered the best technique for it. Also, it is considered a good test for predicting quality determinants of a policy. Regression analysis can either be based on simple regression or multiple regression.  An example: it is used to predict or forecast the economic growth rates. The economic growth rate (measured of changes in real GDP) is a tremendously important number for policymakers and other groups of people (like investors). It has substantial repercussions for a well being of a country.  A very important step before going ahead with regression is to satisfy some of the assumptions for carrying out this test, otherwise the results will be biased or spurious.  Time Series Analysis: As the name can tell, this statistical analysis is related to patterns that emerge over time. It normally is used in long term studies, but within these, it has the power to predict the causation between variables

(dependent and independent) over much shorter time spans. For example, one of the areas in which the government is most interested in is the relation between unemployment and its effects on growth. Time series analysis lets patterns emerge over time, and within that time period one can also get a statistical picture of relation between variables over a quarter or half-year.  Based on the patterns gauged from long term time series and analysis, policy makers can get a better handle of historical causation variables and lessons for future program implementation. It can also be used for forecasting based on past trends (weather people do that a lot).  For use of time series analysis, it is critically important that the historical data is of good quality and thorough. Otherwise, results can provide misleading trends and thus prove to be a problematic for purposes of present day policy making.  Six steps in the time series test (page 358).

 Event History Analysis: A sub-series of time series analysis that is more geared towards rare events in a time series, and why some individuals/groups/organizations tend to be more affected by these events.  What is different about this test from time series analysis is that certain unique terms/sets are used to test the hypothesis. These include a risk set (who is likely at risk), survivor set (measures the decline of risk over time) and a hazard set (the frequency of occurrence of an event at a certain time).  Began to be used in the 1970’s for statistical inquiry into matters related to international affairs (like occurrence of diplomatic events or rare events like war).  Factor Analysis: This one is related to the intangible variables like trust, deception, satisfaction, envy, etc. It is based on the assumption that there are some specific, unobservable factors that underlie the relation (as measured by statistical tests) that is not visible to the observer by just looking at the numbers. Once these factors or variables are taken into consideration, properly adjusted in the model and tested, then we get a

much more vibrant result.  It is of two types; Exploratory and Confirmatory. The difference between the two basically boils down to the assumption of effectiveness or strength of unobserved variables. Confirmatory analysis is more towards unobserved variables, and vice versa.  Path Analysis: To put it in simple words, path analysis is the test for gauging the effect of an intermediary (or a third factor between the independent and dependent variable). Normally what we have seen with the above tests is that they are all concentrated on the link between dependent and independent variable. This test differs in the sense that it takes into account the fact that the independent variable may affect an unknown (path) variable that in turn significantly affects an independent variable. Path variable measures this ‘indirect’ relation between the two variables.  The earlier example of vote polling in Baluchistan. There is the path variable (security) that affects both citizens’ participation due to government’s public policies.

Game Theory:  A few helpful concepts from Economics