Educational program
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BasicEconometrics program 2016
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Basic Econometrics
Section 1:General information
Section 2: Academic Content
Section 3: Delivery of Subject and timetable
The timing/scheduling of topics may be varied depending on student feedback and progress. Topic 1. Introduction. The Nature of Regression Analysis We discuss the historical as well as the modern interpretation of the term regression and illustrate the difference between the two interpretations with several examples drawn from economics and other fields. Topic 2. Two-Variable Regression Models and Analysis We introduce some fundamental concepts of regression analysis with the aid of the two-variable linear regression model, a model in which the dependent variable is expressed as a linear function of only a single explanatory variable. We continue to deal with the two-variable model and introduce what is known as the classical linear regression model, a model that makes several simplifying assumptions. With these assumptions, we introduce the method of ordinary least squares (OLS) to estimate the parameters of the two-variable regression model. The method of OLS is simple to apply, yet it has some very desirable statistical properties. Topic 3. Classical Normal Linear Regression Model We introduce the (two-variable) classical normal linear regression model, a model that assumes that the random dependent variable follows the normal probability distribution. With this assumption, the OLS estimators obtained in Topic 3 possess some stronger statistical properties than the non normal classical linear regression model—properties that enable us to engage in statistical inference, namely, hypothesis testing. Topic 4. Two-Variable Regression: Interval Estimation and Hypothesis Testing Devoted to the topic of hypothesis testing. In this Topic, we try to find out whether the estimated regression coefficients are compatible with the hypothesized values of such coefficients, the hypothesized values being suggested by theory and/or prior empirical work. Topic 5. Extensions of the Two-Variable Linear Regression Model Considers some extensions of the two-variable regression model. In particular, it discusses topics such as (1) regression through the origin, (2) scaling and units of measurement, and (3) functional forms of regression models such as double-log, semi log, and reciprocal models. Topic 6. Multiple Regression Analysis: The Problem of Estimation We consider the multiple regression model, a model in which there is more than one explanatory variable, and show how the method of OLS can be extended to estimate the parameters of such models. Topic 7. Multiple Regression Analysis: The Problem of Inference We extend the concepts introduced in Topic 5 to the multiple regression model and point out some of the complications arising from the introduction of several explanatory variables. Topic 8. Dummy Variable Regression Models on dummy, or qualitative, explanatory variables concludes Part I of the text. This Topic emphasizes that not all explanatory variables need to be quantitative (i.e., ratio scale). Variables, such as gender, race, religion, nationality, and region of residence, cannot be readily quantified, yet they play a valuable role in explaining many an economic phenomenon. Topic 9. Multicollinearity In this chapter we take a critical look at this assumption by seeking answers to the following questions: 1. What is the nature of multicollinearity? 2. Is multicollinearity really a problem? 3. What are its practical consequences? 4. How does one detect it? 5. What remedial measures can be taken to alleviate the problem of multicollinearity? Topic 10. Heteroscedasticity In this chapter we examine the validity of this assumption and find out what happens if this assumption is not fulfilled. As in Chapter 10, we seek answers to the following questions: 1. What is the nature of heteroscedasticity? 2. What are its consequences? 3. How does one detect it? 4. What are the remedial measures? Topic 11. Autocorrelation In this chapter we take a critical look at this assumption with a view to answering the following questions: 1. What is the nature of autocorrelation? 2. What are the theoretical and practical consequences of autocorrelation? 3. Since the assumption of no autocorrelation relates to the unobservable disturbances ut, how does one know that there is autocorrelation in any given situation? Notice that we now use the subscript t to emphasize that we are dealing with time series data. 4. How does one remedy the problem of autocorrelation? Topic 12. Econometric Modeling: Model Specification and Diagnostic Testing In this chapter we take a close and critical look at this assumption, because searching for the correct model is like searching for the Holy Grail. In particular we examine the following questions: 1. How does one go about finding the “correct” model? In other words, what are the criteria in choosing a model for empirical analysis? 2. What types of model specification errors is one likely to encounter in practice? 3. What are the consequences of specification errors? 4. How does one detect specification errors? In other words, what are some of the diagnostic tools that one can use? 5. Having detected specification errors, what remedies can one adopt and with what benefits? 6. How does one evaluate the performance of competing models? Topic 13. Nonlinear Regression Models The major emphasis of this course is on linear regression models, that is, models that are linear in the parameters and/or models that can be transformed so that they are linear in the parameters. On occasions, however, for theoretical or empirical reasons we have to consider models that are nonlinear in the parameters. In this topic we take a look at such models and study their special features. Topic 14. Qualitative Response Regression Models In this topic we consider several models in which the regressand itself is qualitative in nature. Although increasingly used in various areas of social sciences and medical research, qualitative response regression models pose interesting estimation and interpretation challenges. In this topic we only touch on some of the major themes in this area, leaving the details to more specialized books. Topic 15. Panel Data Regression Models In topic 1 we discussed briefly the types of data that are generally available for empirical analysis, namely, time series, cross section, and panel. In time series data we observe the values of one or more variables over a period of time. In cross-section data, values of one or more variables are collected for several sample units, or entities, at the same point in time. In panel data the same cross-sectional unit is surveyed over time. In short, panel data have space as well as time dimensions. Topic 16. Dynamic Econometric Model: Autoregressive and Distributed-Lag Models Autoregressive and distributed-lag models are used extensively in econometric analysis, and in this chapter we take a close look at such models with a view to finding out the following: 1. What is the role of lags in economics? 2. What are the reasons for the lags? 3. Is there any theoretical justification for the commonly used lagged models in empirical econometrics? 4. What is the relationship, if any, between autoregressive and distributed-lag models? Can one be derived from the other? 5. What are some of the statistical problems involved in estimating such models? 6. Does a lead–lag relationship between variables imply causality? If so, how does one measure it? Topic 17. Simultaneous-Equation Models and Methods In these two chapters we discuss the simultaneous equation models. In particular, we discuss their special features, their estimation, and some of the statistical problems associated with them. Having discussed the nature of the simultaneous-equation models we turn to the problem of estimation of the parameters of such models. At the outset it may be noted that the estimation problem is rather complex because there are a variety of estimation techniques with varying statistical properties. In view of the introductory nature of this text, we shall consider only a few of these techniques. Our discussion will be simple and often heuristic, the finer points being left to the references. Topic 18. The Identification Problem In this topic we consider the nature and significance of the identification problem. The crux of the identification problem is as follows: Recall the demand-and-supply model introduced in Suppose that we have time series data on Q and P only and no additional information. The identification problem then consists in seeking an answer to this question: Given only the data on P and Q, how do we know whether we are estimating the demand function or the supply function? Alternatively, if we think we are fitting a demand function, how do we guarantee that it is, in fact, the demand function that we are estimating and not something else? Topic 19. Time Series Econometrics: Some Basic Concepts We noted in Topic 1 that one of the important types of data used in empirical analysis is time series data. In this and the following chapter we take a closer look at such data not only because of the frequency with which they are used in practice but also because they pose several challenges to econometricians and practitioners. Topic 20. Time Series Econometrics: Forecasting We noted in the Introduction that forecasting is an important part of econometric analysis, for some people probably the most important. How do we forecast economic variables, such as GDP, inflation, exchange rates, stock prices, unemployment rates, and myriad other economic variables? In this topic we discuss two methods of forecasting that have become quite popular: autoregressive integrated moving average (ARIMA), popularly known as the Box–Jenkins methodology and vector autoregression (VAR). Section 4: Subject Resources
Section 5: Assessment/coursework All assessment will comply with the university Assessment Rules and Regulations. There will be two midterms and one exam in each semester. Remember to take special note of the rules regarding plagiarism. Specific for this Subject are the following requirements per semester:
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