Introductory Econometrics, a modern approach. Thomson. Stock, James/Watson,
Mark. Introduction to Econometrics,. Third Edition (older is possible as well).
Econometrics Lecture 1: Introduction
Lars-H. Siemers Only for internal use at the University of Siegen, all rights reserved by the author
WS 2013/2014
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Organization Compulsory module, SPECIAL Tutorial: Benjamin Schäfer, each week (!), this week: Thursday Tutorials will include practical hours at the PC, probably using MATLAB. (in PC room!) Time for questions: after lectures or on appointment Problem sets and slides can be downloaded from http://www.uni-siegen.de/fb5/ewp/ and in LSF.
→ Password required, only announced in lecture.
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Exam There will be two exam parts: 1 2
Mid-term exam (30%): probably December 3, 2013 Final exam (70%)
You have to register for exams, in order to participate! Date of final exam: cf. homepage of Prüfungsamt Fakultät III (Prüfungen) (examination office) Midterm exam result is relevant for final grade (see above), irrespective of participating in examination date 1 or 2. → form learning groups, learn together! → read the relevant pages in the books!
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Basic Literature Wooldridge, Jeffrey. Introductory Econometrics, a modern approach. Thomson. Stock, James/Watson, Mark. Introduction to Econometrics, Third Edition (older is possible as well). Pearson. Literature for Stata Baum, Christopher. An Introduction to Modern Econometrics Using Stata. Stata Press. Kohler, Ulrich/Kreuter, Frauke. Datenanalyse mit Stata, 3. Auflage. Oldenbourg. See list of literature at the Chair’s homepage for further literature, that can be used!
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1 Introduction 1.1 Basic Ideas on Econometrics
Three pillars of economics education: 1
microeconomics
2
macroeconomics
3
econometrics
A basic root is Empirism (philosophy of sciences): A central concept in science and the scientific method is that it must be empirically based on the evidence of the senses. Both natural and social sciences use working hypotheses that are testable by observation and experiment.
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Empirism versus Rationalism Philosophical empiricists hold no knowledge to be properly inferred or deduced unless it is derived from one’s sense-based experience. This view is commonly contrasted with Rationalism, which asserts that knowledge may be derived from reason independently of the senses.
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Remind Karl Popper: Theories cannot be proved. Humans are limited in cognition, so that we cannot provide final certainty (critical rationalism). All we can do is shifting closer to the truth by trial & error. That is, we must try to falsify a theory. If we cannot falsify it, it holds, but only as long as it is falsified (empirical falsification). Example: For 200 years Newton’s gravitational theory was accepted as true, until Albert Einstein falsified it in particular areas by his famous theory of relativity. → Stay critical! Take nothing for granted!
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“Econometrics” means “measurement in economics”
Definition in Maddala (1992) The application of statistical and mathematical methods to the analysis of economic data, with the purpose of giving empirical content to economic theories and verifying them or refuting them.
(Source: Maddala, G. S. (1992). Introduction to Econometrics. Prentice Hall.)
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Empirical research gained markedly importance in economics in the 1930th (roughly): “empirical revolution” In the last two decades the number and fraction of empirical research in economics increased significantly. Empirical research covers more than only econometrics in a narrow sense: simulations, descriptive analyses Empirical research bases on statistical methods Statistical analysis of economic (and related) data
→ econometrics is applied statistics and probability theory
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Example: analysis of economic policy Aim: qualitative effects (nil, positive, negative) and/or quantify the effect (how big is it?); Empirical analyses require formal model-based foundations! ⇒ Mathematics, Models Identification strategy imperative: based on economic theory, probability theory, and mathematical statistics, how should the research question be addressed? (research design)
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All empirical analysis is based on data. Data are observations in the past. Empirical results (evidence) first of all tells us something about the past. However, learning from the relationships and developments in the past, we can deduce well-grounded conclusions for current and future developments and effects. Aim of the course: I I I
learn basic econometrics: methods, tests etc. be aware of the limitations of econometrics being critical when reading about studies
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1.2 Function of Econometrics Traditional Function Theoretical economics suggests important theories on relationships, often with crucial policy implication. For instance, I I
I
investments increase economic growth; public expenditures in infrastructure increase gross domestic income and growth; in most cases, minimum wages cause unemployment.
At first stage, these theories are hypotheses! → Are they true? We must test the validity and quality of the models! Confront them with data!
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Following Maddala, aims of econometrics are: 1. Formulation of econometric model, that is, an empirically testable theory. (specification) 2. Estimation and testing of the model with observed data. (inference) 3. Apply model for predictions and policy conclusions.
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Source: Maddala (1992), p. 7 14 / 22
Provide policy makers with empirical evidence If true, how big is the asserted effect? For instance, if corporate taxes on profits rise by 1%, what will be the effect on corporate investment, wages, dividends, employment? What have been the actual effects of the last labor market reform (e.g. Harz IV in Germany)? How does a university degree change my earnings? etc. pp.
→ Theory virtually never tells us anything about quantitative magnitudes of causal effects!
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Data link theory to the real world. What can we say, if we look at the past observations and exploit statistical methods? This can provide empirical evidence on relationships that are not yet addressed by theory! → beneficial relationship between theoretical and empirical research! This is what econometrics is about.
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Be careful: many difficulties arise! In this lecture, we can only address first main steps and methods: Introductory Econometrics Focus on applied econometrics. However, theory is required and must be understood well. Otherwise, risk of meaningless and misleading results, that do not make sense!
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1.3 Causality vs. Correlation
Providing evidence for a relationship, that is correlation, between variables is important, but rarely sufficient. Is it causal? Which change of variable Y is caused by a 1% increase of X? (causality) Especially policy makers must be informed by causal effects, for instance, with regard to some public investment. (policy conclusions) While every regression may inform about a specific statistical correlation, deducing the causal effects is often difficult.
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Example Theory: Public infrastructure investment causes economic growth. Using data, the simplest test for this hypothesis is estimating the following model: growth = β1 ∗ infrastr .inv. +
|
{z
}
growth effects of infrastr.
β0 |{z}
other effects
+
u |{z}
by random
The result for coefficient β1 would be the average marginal return to public investment in infrastructure. Is it the causal effect?
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Very unlikely! We all know that economic growth is determined by many further aspects, such as private investments in human and physical capital, research and development etc. Terms β0 and u “control” for other factors, but these are potentially correlated with each other: we have to control for as many determinants as possible. Some things of the story are still unobserved: this can be problematic
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Data Quality and Quality of Statistical Methods Given that we have to rely on data, the result of any analysis is determined by the quality of the data used. Given the data available, it is decisive to use the most adequate statistical method to analyze the data: economic and statistical/probability theory. Therefore, “in practice good results depend as much on the input of sound and imaginative economic theory as on the application of correct statistical methods.” (Kennedy, p. 2)
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Reading for Chapter 1 Stock/Watson: Chapt. 1 Wooldridge: Chapt. 1 Bauer et al.: Kapitel 1.1
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