Presentation on theme: "INSTRUCTOR INTRODUCTIOON"— Presentation transcript:
1 INSTRUCTOR INTRODUCTIOON DR. MUHAMMAD IFTIKHAR UL HUSNAINASSISTANT PROFESOR ,DEPARTMENT OF MANAGEMENT SCIENCES COMSATS, ISLAMABAD SINCE 2012LECTURER IN ECONOMICS PUNJAB HIGHER EDUCATION DEPARTMENTAREAS OF INTERSTMICRO ECONOMICSMACRO ECONOMICSMATHEMATICAL ECONOMICSSTATISTICSPUBLIC FINANCE
2 Course: MGT 605 Quantitative Techniques Instructor: Dr. Muhammad Iftikhar ul HusnainCourse overviewDefinition of Quantitative TechniquesNature and Scope of Quantitative TechniquesDataSamplingSimple regressionMultiple regressionMulticollinearityAuto correlationHetrosekasdicityDummy variables.Logit modelProbit modelPanel data
3 Quantitative Methods for Business by Anderson Sweeny and Williams Course Overview ….BooksQuantitative Methods for Business by Anderson Sweeny and WilliamsQuantitative analysis for management by Render, Stair, HannaJohnston, J. and J. DiNardo (n.d). Econometric Methods (4th Eds.) The McGraw Hill Companies.Maddala, G. S. (1992). Introduction to Econometrics, (2nd Edition), Macmillan Publishing Company, New York.J. Wooldridge, Introductory Econometrics, 4th Edition, South Western College Press
4 Introduction Back Ground Definition Quantitative tools have been used for thousands of years (Taylor and Frederick)Quantitative analysis can be applied to a wide variety of problems acrossdesciplineIt’s not enough to just know the mathematics of a techniqueOne must understand the specific applicability of the technique, its limitations, and its assumptionsDefinitionQuantitative Technique: Any procedure, formulae, instrument, model that helps in analyzing any hypothesis, theory, statement is called quantitative techniques.In Quantitative analysis, we may be interested in predicting Y with X, or with the casual effect of X on Y.Quantitative analysis is a scientific approach to decision making whereby raw data are processed and manipulated resulting in meaningful information.Raw data--application of QT----Meaningful information
5 Qualitative versus quantitative analysis It gives a general and unspecified viewFocus on wordsSubjectiveCase studiesfewer respondentsNo standardized data analysisExplores impact (why)It gives clear, specific and numerical prediction of the problemFocus on numberObjectiveStatistical Analysishigher number of respondentsStandardized data analysisSuggests/quantify the impact
6 Quantitative vs. Qualitative factors Quantitative factorsMight be different investment levels, interest rates, inventory levels, demand, or labor costQualitative factorssuch as the weather, state and federal legislation, and technology breakthroughs should also be considered. Political instabilityInformation may be difficult to quantify but can affect the decision-making processWhy Study Quantitative TechniquesIn your everyday life, it will help you make sense of what to heed and what to ignore in statistical information provided in news reports, surveys, political campaigns, advertisementsFrom house to stock market
7 Advantages of Quantitative Analysis 1- It can accurately represent reality2- It can help a decision maker formulate problems3- It can give us insight and information4- It can save time and money in decision making and problem solving5- It may be the only way to solve large or complex problems in a timely fashion6- It can be used to communicate problems and solutions to others7- it can help in forecasting the future on the basis of available information
8 How the Quantitative Approach Works It is a systematic process1- Define the problemIt is a starting pointDevelop a clear and concise statement.Most important and difficult stepselecting the right problems is very importantSpecific and measurable objectives may have to be developedIn the real world, quantitative analysis models can be complex, expensive and time consuming.
9 2- Developing a Mathematical Model What a Model is?A model is simply a set of mathematical equations.A model is a mathematical representation of a theory/reality.Single equation modelIf a model has only one equation it is called a single-equation model.Multiple-equation modelIf it has more than one equationModels generally contain variables, and parametersParameters are unknown quantities but there value is fixed.
10 How to Develop a Quantitative Model…. An important part of the quantitative analysis approachMathematical Models have two major types.Mathematical models that do not involve risk are called deterministic models.We know all the values used in the model with complete certainty.Mathematical models that involve risk, chance, or uncertainty are called probabilistic modelsIn this course our concern is with the probabilistic model.Example: Demand functionProfit function
11 3- Speciﬁcation of the probabilistic/econometric Model Mathematical model assumes that there is an exact relationship between the variables.However it is not of much interest in social sciencesBut relationships between variables are generally inexact in social sciences.The introduction of error termIt captures the effect of unquantifiable forces.
12 4- Significance of the stochastic term Ui Error term is proxy for all the omitted variables but collectively affect Y.- Why not introduce all the variable explicitly?Unavailability of dataCore versus peripheral variables- joint effect of many variable is so small that for practical consideration and cost effectiveness it does not pay to introduce them explicitly in the model.Randomness in Human BehaviorPoor Proxy VariablePrinciple of parsimonyWrong functional form
13 4- Acquiring dataTo estimate the Statistical model given/ obtain the numerical values of β1 and β2 , we need data.Data may come from a variety of sources such as company reports, company documents, interviews.The quality data is extremely important.The reliability of results is directly proportional to quality of data.Data collecting is an art.Data is collected only on the required variables.
14 5. Estimation of the Statistical Model To estimate the values of the parameters we need some techniques.Common techniques areSolving equationsTrial and error – trying various approaches and picking the best result
15 6- Hypothesis Testing/testing the solution Model should be tested for accuracy before analysis and implementation.Whether the results are according to the theory .Results should be logical, consistent, and represent the real situation.We use statistical inference (hypothesis testing) to know the significance of the parameter.
16 7- Forecasting or Prediction If the chosen model does not refute the hypothesis or theory under consideration,we may use it to predict the future value(s) of the dependent variable Y on the basis of known variables.8- implementing the results/ policyImplementation can be very difficultPeople can resist changesMany quantitative analysis efforts have failed because a good, workable solution was not properly implemented
17 Possible Problems in the Quantitative Analysis Approach Problems are not easily identifiedConflicting viewpoints-linear or non linear relationshipBeginning assumptionsFitting the textbook modelsUnderstanding the modelValidity of dataHard-to-understand mathematics and statistics
18 Variables and their Types Ratio scaleTwo values of a variable say X1 and X2,(i)X1/X2 (ii)(X1-X2) (iii) X1≤ X2 and vice versa are meaningful quantities. Most economic variable are ratio scale.Interval ScaleSatisfies last two properties. Distance b/w two time periods ( ) is meaningful. But 1990/2012 is senseless.Ordinal ScaleOnly it satisfies the third property of ratio scale. Grades, A,B,C,D. Upper, Middle, Lower. Example: Indifference curve in EconomicsNominal Scale:None of the feature of ratio scale. Gender, male, female, marital status, single, married, divorced, unmarried simply denote categories
19 ProbabilityA probability is a numerical statement about the chance that an event will occur.Two basic rules regarding the probability1- The probability, p, of any event is greater than or equal to 0 and less than or equal to ≤p≤1A- 0 means that an event is never expected to occurb- 1 means that an event is always to occur.2- The sum of the simple probabilities for all possible outcomes of an activity must be equal to 1.Examples:Quantity Demanded Number of daysp=40/200 (.20)p=80/200 (.40)p=(50/200) (.25)
20 Types of probability Two different ways to determine the probability Objective p(event)= number of occurrence of the event /total number of eventsExamples: tossing of a fair coin- it is based on the previous logic.Subjective: logic and history are not appropriate. So subjectivity arises.Exampleswhat is the probability that floods will come?What is the probability that depression will come in an economy?For this opinion polls are conducted and then probabilities are found.
21 Mutually exclusive and collectively exhaustive events Mutually exclusive events : If only one of the event can occur on any one trial.Collectively exhaustive events: They are said to be mutually exhaustive if the list include all the possible outcomes i.e. A U B= S.Not mutually exclusiveThe occurrence of one event does not restrict the occurrence of the other event.Examples: Drawing a 5 and drawing a diamond from a deck of cards- it can be both 5 and diamond
22 Adding mutually exclusive events We are interested in whether one event or second event will occur.When events are mutually exclusive the law of addition is simply as follows.P(event A or event B)= p(event A)+ p(event B)Drawing spade or drawing a club out of a deck card are mutually exclusive. 13/52+13/52=1/2Venn diagramAddition of not mutually exclusive events.P(A or B)= P(A)+P(B)-P(A and B)Examples:In a math class of 30 students, 17 are boys and 13 are girls. On a unit test, 4 boys and 5 girls made an A grade. If a student is chosen at random from the class, what is the probability of choosing a girl or an A student?P(girl or A)= P(girl)+ P(A)- P(girl and A)= 13/30+9/30-5/30=17/30
23 Some Basic Concepts in Mathematics DerivativesDefinitionMaximaMinimaRules of Derivatives1- Constant function rule2- Power function rule3- Sum difference rule4- Product rule5- Quotient rule6- Chain rule7- Inverse function rule