Chapter 1 Introduction to Econometrics PDF

Chapter 1 Introduction  Introduction: The Nature and Purpose of Econometrics What is Econometrics? Literal meaning is “measurement in economics”. “Econometrics is about how we can use theory and data from economics, business and the social sciences, along with tools...

Chapter 1 Introduction  Introduction: The Nature and Purpose of Econometrics What is Econometrics? Literal meaning is “measurement in economics”. “Econometrics is about how we can use theory and data from economics, business and the social sciences, along with tools from statistics, to answer “how much” type questions.” (Hill, Griffiths, and Judge, Introduction to Econometrics, 2nd edition, John Wiley & Sons, Inc., 2001). Zenegnaw Abiy Hailu (PhD) 2  Types of Data There are 3 types of data which econometricians might use for analysis: 1. Time series data 2. Cross-sectional data 3. Panel data, a combination of 1. & 2. The data may be quantitative (e.g. Profit, price cost, demand), or qualitative (e.g. Motivation, satisfaction, service quality, etc). Zenegnaw Abiy Hailu (PhD) 3  Continuous and Discrete Data Continuous data can take on any value and are not confined to take specific numbers. (Eg. Profit, ) On the other hand, discrete data can only take on certain values, which are usually integers (Eg. Number of employees, quantity sold for countable) Zenegnaw Abiy Hailu (PhD) 4  Ratio, Interval, Ordinal and Nominal Numbers Another way in which we could classify numbers is according to whether they are cardinal, ordinal, or nominal. Ratio numbers are those where the actual numerical values that a particular variable takes have meaning, and where there is an equal distance between the numerical values with absolute zero. Interval numbers are those where the order has meaning, interval between any two values has meaning but there is no absolute zero. Ordinal numbers can only be interpreted as providing a position or an ordering. Zenegnaw Abiy Hailu (PhD) 5  Ratio, Ordinal and Nominal Numbers (Cont’d) Nominal numbers occur where there is no natural ordering of the values at all. Cardinal, ordinal and nominal variables may require different modeling approaches or at least different treatments, as should become evident in the subsequent chapters. Zenegnaw Abiy Hailu (PhD) 6  Examples of the kind of problems that may be solved by an Econometrician 1. Testing whether financial markets are weak-form informationally efficient. 2. Testing whether the CAPM or APT represent superior models for the determination of returns on risky assets. 3. Measuring and forecasting the volatility of bond returns. 4. Explaining the determinants of bond credit ratings used by the ratings agencies. 5. Modelling long-term relationships between prices and exchange rates Zenegnaw Abiy Hailu (PhD) 7  Examples of the kind of problems that may be solved by an Econometrician 6. Determining the optimal hedge ratio for a spot position in oil. 7. Testing technical trading rules to determine which makes the most money. 8. Testing the hypothesis that earnings or dividend announcements have no effect on stock prices. 9. Testing whether spot or futures markets react more rapidly to news. 10.Forecasting the correlation between the returns to the stock indices of two countries. Zenegnaw Abiy Hailu (PhD) 8 Steps involved in the formulation of econometric models Already Existing Theory or Empirical Evidence Formulation of an Estimable Theoretical Model Collection of Data Model Estimation Is the Model Statistically Adequate? No Yes Reformulate Model Interpret Model Zenegnaw Abiy Hailu (PhD) 9  Bayesian versus Classical Statistics The philosophical approach to model-building used here throughout is based on ‘classical statistics’ This involves postulating a theory and then setting up a model and collecting data to test that theory Based on the results from the model, the theory is supported or refuted There is, however, an entirely different approach known as Bayesian statistics Here, the theory and model are developed together Zenegnaw Abiy Hailu (PhD) 10  Bayesian versus Classical Statistics Under Bayesian approach the researcher starts with an assessment of existing knowledge or beliefs formulated as probabilities, known as priors The priors are combined with the data into a model The beliefs are then updated after estimating the model to form a set of posterior probabilities Bayesian statistics is a well established and popular approach, although less so than the classical one Zenegnaw Abiy Hailu (PhD) 11  Bayesian versus Classical Statistics (Cont’d) Some classical researchers are uncomfortable with the Bayesian use of prior probabilities based on judgement If the priors are very strong, a great deal of evidence from the data would be required to overturn them So the researcher would end up with the conclusions that he/she wanted in the first place! In the classical case by contrast, judgement is not supposed to enter the process and thus it is argued to be more objective. Zenegnaw Abiy Hailu (PhD) 12 Some Points to Consider When Reading papers in the academic literature 1. Does the paper involve the development of a theoretical model or is it merely a technique looking for an application, or an exercise in data mining? 2. Is the data of “good quality”? Is it from a reliable source? Is the size of the sample sufficiently large for asymptotic theory to be invoked? 3. Have the techniques been validly applied? Have diagnostic tests for violations of been conducted for any assumptions made in the estimation of the model? Zenegnaw Abiy Hailu (PhD) 13 Some Points to Consider when reading papers in the academic literature (cont’d) 4. Have the results been interpreted sensibly? Is the strength of the results exaggerated? Do the results actually address the questions posed by the authors? 5. Are the conclusions drawn appropriate given the results, or has the importance of the results of the paper been overstated? Zenegnaw Abiy Hailu (PhD) 14  Additional points to consider in applied econometrics Use common sense and accounting and finance theory The role of theory extends beyond the development of the specification; it is crucial to the interpretation of the results and to identification of predictions from the empirical results that should be test. Zenegnaw Abiy Hailu (PhD) 15  Additional points to consider in applied econometrics Know the context Do not try to model without understanding the non- statistical aspects of the real-life system you are trying to subject to statistical analysis. (Belsley and Welch, 1988). History, institutions, operating constraints, measurement peculiarities, cultural customs. How were the data gathered? Zenegnaw Abiy Hailu (PhD) 16  Additional points to consider in applied econometrics Keep it sensibly simple Econometricians employ the latest, most sophisticated econometric techniques, often because such techniques are novel and available, not because they are appropriate. Think first why you are doing before attacking the problem with all the technical arsenal you have and churning out a paper that may be mathematically imposing but of limited practical use. (Maddala, 1999). Zenegnaw Abiy Hailu (PhD) 17  Additional points to consider in applied econometrics Test the estimation To check that the results make sense. The signs of coefficients as expected? Important variables statistically significant? Are coefficient magnitudes reasonable? Are the results consistent with theory? Zenegnaw Abiy Hailu (PhD) 18  Additional points to consider in applied econometrics Report a sensitivity analysis Are the results sensitive to the sample period, the functional form, the set of explanatory variables, or measurement of proxies for the variables? Are robust estimation results markedly different? Zenegnaw Abiy Hailu (PhD) 19 End