Learning Goals Econometrics Overview in Econometrics, Students

Total Page：16

File Type：pdf, Size：1020Kb

Learning Goals Econometrics Overview In Econometrics, students learn to test economic theory and models using empirical data. Students will learn how to construct econometric models from the economic model, and how to estimate the model’s parameters. Econometrics is largely based on the probability and statistics theory taught in the statistics prerequisite for the course. Students will learn introductory regression analysis starting from the ordinary least squares (OLS) estimation method, which is based on the classical linear regression assumptions. The Gauss-Markov Theorem is introduced. Students also learn generalized least squares (GLS) estimation methods when classical linear regression assumptions are violated or relaxed. Instrumental variables (IV) methods, panel data techniques, and time series econometrics are also presented. Students will be introduced to statistical software packages used to estimate regression models, such as Stata, SPSS, and SAS. Specific Learning Goals 1. Difference between economic model and econometric model a. Introducing unobserved random error terms into the economic model to make it an econometric model. b. Difference between population and random sample to estimate the unknown population parameters. This should have been introduced in the pre-requisite Economic Statistics, and needs a brief refresh of the concepts. c. Linear regression model and estimating model parameters. 2. Classical linear regression assumptions a. Define the linear regression model based on the classical linear regression assumptions. b. Homoskedasticity and no autocorrelation. c. Normal distribution of the random error term. 3. Ordinary least squares (OLS) estimation a. Intuition behind the OLS estimation and derivation of OLS estimates. b. Statistical properties of OLS estimators. c. Finite sample properties of OLS estimators. d. Gauss-Markov Theorem: Best Linear Unbiased Estimators (BLUE). e. Asymptotic properties of OLS estimators based on the Law of Large Numbers and the Central Limit theorem. 4. Statistical inferences a. Hypothesis testing and confidence interval construction for the population parameters based on the finite sample properties of OLS estimators. This section is heavily based on material from the pre-requisite Economic Statistics. b. Asymptotic tests and confidence intervals. 5. Extensions to the linear regression model a. Regression model specification and its tests. b. Dummy variable regression. 6. Generalized least squares (GLS) estimation a. Heteroskedasticity b. Autocorrelation c. Robust standard error estimation. 7. Instrumental variable (IV) regression a. The endogenous regressor problem 8. Time-series regression a. Stationary series b. Weak exogeneity and asymptotically independent errors 9. Panel regression model a. Pooled regression estimation: Simple panel regression model b. Fixed effect model estimation for a simple panel regression model c. Difference-in-difference estimation 10. Computer skills: At the end of this course, students should be proficient in not only econometric theory, but also empirical estimation of the model using statistical software. This will greatly enhance students’ job market prospects and better prepare them for graduate degree programs. .

Copy Link

Recommended publications

Nber Working Paper Series Financial Markets and The
NBER WORKING PAPER SERIES FINANCIAL MARKETS AND THE REAL ECONOMY John H. Cochrane Working Paper 11193 http://www.nber.org/papers/w11193 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 March 2005 This review will introduce a volume by the same title in the Edward Elgar series “The International Library of Critical Writings in Financial Economics” edited by Richard Roll. I encourage comments. Please write promptly so I can include your comments in the final version. I gratefully acknowledge research support from the NSF in a grant administered by the NBER and from the CRSP. I thank Monika Piazzesi and Motohiro Yogo for comments. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. © 2005 by John H. Cochrane. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Financial Markets and the Real Economy John H. Cochrane NBER Working Paper No. 11193 March 2005, Revised September 2006 JEL No. G1, E3 ABSTRACT I survey work on the intersection between macroeconomics and finance. The challenge is to find the right measure of "bad times," rises in the marginal value of wealth, so that we can understand high average returns or low prices as compensation for assets' tendency to pay off poorly in "bad times." I survey the literature, covering the time-series and cross-sectional facts, the equity premium, consumption-based models, general equilibrium models, and labor income/idiosyncratic risk approaches.
[Show full text]
Fritz Machlup's Construction of a Synthetic Concept
The Knowledge Economy: Fritz Machlup’s Construction of a Synthetic Concept Benoît Godin 385 rue Sherbrooke Est Montreal, Quebec Canada H2X 1E3 [email protected] Project on the History and Sociology of S&T Statistics Working Paper No. 37 2008 Previous Papers in the Series: 1. B. Godin, Outlines for a History of Science Measurement. 2. B. Godin, The Measure of Science and the Construction of a Statistical Territory: The Case of the National Capital Region (NCR). 3. B. Godin, Measuring Science: Is There Basic Research Without Statistics? 4. B. Godin, Neglected Scientific Activities: The (Non) Measurement of Related Scientific Activities. 5. H. Stead, The Development of S&T Statistics in Canada: An Informal Account. 6. B. Godin, The Disappearance of Statistics on Basic Research in Canada: A Note. 7. B. Godin, Defining R&D: Is Research Always Systematic? 8. B. Godin, The Emergence of Science and Technology Indicators: Why Did Governments Supplement Statistics With Indicators? 9. B. Godin, The Number Makers: A Short History of Official Science and Technology Statistics. 10. B. Godin, Metadata: How Footnotes Make for Doubtful Numbers. 11. B. Godin, Innovation and Tradition: The Historical Contingency of R&D Statistical Classifications. 12. B. Godin, Taking Demand Seriously: OECD and the Role of Users in Science and Technology Statistics. 13. B. Godin, What’s So Difficult About International Statistics? UNESCO and the Measurement of Scientific and Technological Activities. 14. B. Godin, Measuring Output: When Economics Drives Science and Technology Measurements. 15. B. Godin, Highly Qualified Personnel: Should We Really Believe in Shortages? 16. B. Godin, The Rise of Innovation Surveys: Measuring a Fuzzy Concept.
[Show full text]
Economics 329: ECONOMIC STATISTICS Fall 2016, Unique Number 34060 T, Th 3:30 – 5:00 (WCH 1.120) Instructor: Dr
University of Texas at Austin Course Outline Economics 329: ECONOMIC STATISTICS Fall 2016, Unique number 34060 T, Th 3:30 – 5:00 (WCH 1.120) Instructor: Dr. Valerie R. Bencivenga Office: BRB 3.102C Office hours (Fall 2016): T, Th 11:00 – 12:30 Phone: 512-475-8509 Email: [email protected] (course email) or [email protected] The best ways to contact me are by email and in person after class or in office hours. If I don’t answer my phone, do not leave a phone message – please send an email. Use the course email for most emails, including exam scheduling. COURSE OBJECTIVES Economic Statistics is a first course in quantitative methods that are widely-used in economics and business. The main objectives of this course are to explore methods for describing data teach students how to build and analyze probability models of economic and business situations introduce a variety of statistical methods used to draw conclusions from economic data, and to convey the conceptual and mathematical foundations of these methods lay a foundation for econometrics COURSE DESCRIPTION SEGMENT 1. The unit on descriptive statistics covers methods for describing the distribution of data on one or more variables, including measures of central tendency and dispersion, correlation, frequency distributions, percentiles, and histograms. In economics and business, we usually specify a probability model for the random process that generated the data (data-generating process, or DGP). The unit on probability theory covers the set-theoretic foundations of probability and the axioms of probability; rules of probability derived from the axioms, including Bayes’ Rule; counting rules; and joint probability distributions.
[Show full text]
Recurrent Hyperinflations and Learning
Recurrent Hyperin‡ations and Learning Albert Marcet Universitat Pompeu Fabra and CEMFI Juan Pablo Nicolini Universidad Torcuato Di Tella and Universitat Pompeu Fabra Working Paper No. 9721 December 1997 We thank Tony Braun, Jim Bullard, George Evans, Seppo Honkapohja, Rodi Manuelli, Ramon Marimon, Tom Sargent, Harald Uhlig, Neil Wallace and Car- los Zarazaga for helpful conversations and Marcelo Delajara and Ignacio Ponce Ocampo for research assistance. All errors are our own. Part of this work was done when both authors were visiting the Federal Reserve Bank of Minneapolis. Research support from DGICYT, CIRIT and HCM is greatly appreciated. E-mail addresses: [email protected], [email protected]. CEMFI, Casado del Alisal 5, 28014 Madrid, Spain. Tel: 341 4290551, fax: 341 4291056, http://www.cem….es. Abstract This paper uses a model of boundedly rational learning to account for the observations of recurrent hyperin‡ations in the last decade. We study a standard monetary model where the fully rational expectations assumption is replaced by a formal de…nition of quasi-rational learning. The model under learning is able to match remarkably well some crucial stylized facts observed during the recurrent hyperin‡ations experienced by several countries in the 80’s. We argue that, despite being a small departure from rational expec- tations, quasi-rational learning does not preclude falsi…ability of the model and it does not violate reasonable rationality requirements. Keywords: Hyperin‡ations, convertibility, stabilization plans, quasi-rationality. JEL classi…cation: D83, E17, E31. 1 Introduction The goal of this paper is to develop a model that accounts for the main fea- tures of the hyperin°ations of last decade and to study the policy recomen- dations that arise from it.
[Show full text]
Recent Developments in Macro-Econometric Modeling: Theory and Applications
econometrics Editorial Recent Developments in Macro-Econometric Modeling: Theory and Applications Gilles Dufrénot 1,*, Fredj Jawadi 2,* and Alexander Mihailov 3,* ID 1 Faculty of Economics and Management, Aix-Marseille School of Economics, 13205 Marseille, France 2 Department of Finance, University of Evry-Paris Saclay, 2 rue du Facteur Cheval, 91025 Évry, France 3 Department of Economics, University of Reading, Whiteknights, Reading RG6 6AA, UK * Correspondence: [email protected] (G.D.); [email protected] (F.J.); [email protected] (A.M.) Received: 6 February 2018; Accepted: 2 May 2018; Published: 14 May 2018 Developments in macro-econometrics have been evolving since the aftermath of the Second World War. Essentially, macro-econometrics benefited from the development of mathematical, statistical, and econometric tools. Such a research programme has attained a meaningful success as the methods of macro-econometrics have been used widely over about half a century now to check the implications of economic theories, to model macroeconomic relationships, to forecast business cycles, and to help policymakers to make appropriate decisions. However, despite this progress, important methodological and interpretative questions in macro-econometrics remain (Stock 2001). Additionally, the recent global financial crisis of 2008–2009 and the subsequent deep and long economic recession signaled the end of the “great moderation” (early 1990s–2007) and suggested some limitations of the widely employed and by then dominant macro-econometric framework. One of these deficiencies was that current macroeconomic models have failed to predict this huge economic downturn, perhaps because they did not take into account indicators of contagion, systemic risk, and the financial cycle, or the inter-connectedness of asset markets, in particular housing, with the macro-economy.
[Show full text]
Neoliberalism and Regional Development in Latin America
Neoliberalism and Regional Development in Latin America Robert N.Gwynne School of Geography University of Birmingham Edgbaston Birmingham B15 2TT Abstract This paper is an attempt to promote discussion on the contemporary relationship between the adoption of neoliberal policies by many Latin American governments and the social and economic development of space within those countries. The shift towards neoliberal policies in Latin America is also associated with increasing economic integration between countries but this topic is not the priority in this paper. Rather the emphasis is to see if any generalizations can be made on how the productive response to neoliberalism is taking place within the regions of Latin American countries. After brief discussions on the political economy and origins of neoliberalism, the paper seeks to examine some broad themes of the political economy of regional development within Latin America. Issues of regional production have very much focused on the growth of non-traditional exports. It is argued that these can be best divided into two groups - those based on manufacturing products and those based on renewable resources. This paper pursues analysis of the latter category, mainly in relation to regional development in Chile - first in relation to the wide range of primary exports and then more specifically in terms of the impact of agricultural export growth on regional land and labour markets. Brief Perspectives on the political economy of neoliberalism The political economy of Latin American countries seems increasingly characterized by neoliberal approaches. The use of the term neoliberal has numerous problems in terms of its ideological connotations. For example in international policy circles, the term Washington consensus (Williamson, 1990) tends to be used, indicating virtually the same package of reforms.
[Show full text]
Pandemic 101:A Roadmap to Help Students Grasp an Economic Shock
Social Education 85(2) , pp.64–71 ©2021 National Council for the Social Studies Teaching the Economic Effects of the Pandemic Pandemic 101: A Roadmap to Help Students Grasp an Economic Shock Kim Holder and Scott Niederjohn This article focuses on the major national economic indicators and how they changed within the United States real GDP; the over the course of the COVID-19 pandemic. The indicators that we discuss include output from a Ford plant in Canada is not. Gross Domestic Product (GDP), the unemployment rate, interest rates, inflation, and Economists typically measure real GDP other variations of these measures. We will also present data that sheds light on the as a growth rate per quarter: Is GDP get- monetary and fiscal policy responses to the pandemic. Graphs of these statistics are ting bigger or smaller compared to a prior sure to grab teachers’ and students’ attention due to the dramatic shock fueled by the quarter? In fact, a common definition of pandemic. We will explain these economic indicators with additional attention to what a recession is two quarters in a row of they measure and the limitations they may present. Teachers will be introduced to the declining real GDP. Incidentally, it also Federal Reserve Economic Database (FRED), which is a rich source of graphs and measures total U.S. income (and spend- information for teacher instruction and student research. Further classroom-based ing) and that explains why real GDP per resources related to understanding the economic effects of the COVID-19 pandemic capita is a well-established measure used are also presented.
[Show full text]
Occasional Paper 03 November 2020 ISSN 2515-4664
Public Understanding of Economics and Economic Statistics Johnny Runge and Nathan Hudson Advisory team: Amy Sippitt Mike Hughes Jonathan Portes ESCoE Occasional Paper 03 November 2020 ISSN 2515-4664 OCCASIONAL PAPER Public Understanding of Economics and Economic Statistics Johnny Runge and Nathan Hudson ESCoE Occasional Paper No. 03 November 2020 Abstract This study explores the public understanding of economics and economics statistics, through mixed-methods research with the UK public, including 12 focus groups with 130 participants and a nationally representative survey with 1,665 respondents. It shows that people generally understand economic issues through the lens of their familiar personal economy rather than the abstract national economy. The research shows that large parts of the UK public have misperceptions about how economic figures, such as the unemployment and inflation rate, are collected and measured, and who they are produced and published by. This sometimes affected participants’ subsequent views of the perceived accuracy and reliability of economic statistics. Broadly, the focus groups suggested that people are often sceptical and cynical about any data they see, and that official economic data are subject to the same public scrutiny as any other data. In line with other research, the survey found consistent and substantial differences in economic knowledge and interest across different groups of the UK population. This report will be followed up with an engagement exercise to discuss findings with stakeholders, in order to draw out recommendations on how to improve the communication of economics and economic statistics to the public. Keywords: public understanding, public perceptions, communication, economic statistics JEL classification: Z00, D83 Johnny Runge, National Institute of Economic and Social Research and ESCoE, [email protected] and Nathan Hudson, National Institute of Economic and Social Research and ESCoE.
[Show full text]
The Socialization of Investment, from Keynes to Minsky and Beyond
Working Paper No. 822 The Socialization of Investment, from Keynes to Minsky and Beyond by Riccardo Bellofiore* University of Bergamo December 2014 * [email protected] This paper was prepared for the project “Financing Innovation: An Application of a Keynes-Schumpeter- Minsky Synthesis,” funded in part by the Institute for New Economic Thinking, INET grant no. IN012-00036, administered through the Levy Economics Institute of Bard College. Co-principal investigators: Mariana Mazzucato (Science Policy Research Unit, University of Sussex) and L. Randall Wray (Levy Institute). The author thanks INET and the Levy Institute for support of this research. The Levy Economics Institute Working Paper Collection presents research in progress by Levy Institute scholars and conference participants. The purpose of the series is to disseminate ideas to and elicit comments from academics and professionals. Levy Economics Institute of Bard College, founded in 1986, is a nonprofit, nonpartisan, independently funded research organization devoted to public service. Through scholarship and economic research it generates viable, effective public policy responses to important economic problems that profoundly affect the quality of life in the United States and abroad. Levy Economics Institute P.O. Box 5000 Annandale-on-Hudson, NY 12504-5000 http://www.levyinstitute.org Copyright © Levy Economics Institute 2014 All rights reserved ISSN 1547-366X Abstract An understanding of, and an intervention into, the present capitalist reality requires that we put together the insights of Karl Marx on labor, as well as those of Hyman Minsky on finance. The best way to do this is within a longer-term perspective, looking at the different stages through which capitalism evolves.
[Show full text]
UNEMPLOYMENT and LABOR FORCE PARTICIPATION: a PANEL COINTEGRATION ANALYSIS for EUROPEAN COUNTRIES OZERKEK, Yasemin Abstract This
Applied Econometrics and International Development Vol. 13-1 (2013) UNEMPLOYMENT AND LABOR FORCE PARTICIPATION: A PANEL COINTEGRATION ANALYSIS FOR EUROPEAN COUNTRIES OZERKEK, Yasemin* Abstract This paper investigates the long-run relationship between unemployment and labor force participation and analyzes the existence of added/discouraged worker effect, which has potential impact on economic growth and development. Using panel cointegration techniques for a panel of European countries (1983-2009), the empirical results show that this long-term relation exists for only females and there is discouraged worker effect for them. Thus, female unemployment is undercount. Keywords: labor-force participation rate, unemployment rate, discouraged worker effect, panel cointegration, economic development JEL Codes: J20, J60, O15, O52 1. Introduction The link between labor force participation and unemployment has long been a key concern in the literature. There is general agreement that unemployment tends to cause workers to leave the labor force (Schwietzer and Smith, 1974). A discouraged worker is one who stopped actively searching for jobs because he does not think he can find work. Discouraged workers are out of the labor force and hence are not taken into account in the calculation of unemployment rate. Since unemployment rate disguises discouraged workers, labor-force participation rate has a central role in giving clues about the employment market and the overall health of the economy.1 Murphy and Topel (1997) and Gustavsson and Österholm (2006) mention that discouraged workers, who have withdrawn from labor force for market-driven reasons, can considerably affect the informational value of the unemployment rate as a macroeconomic indicator. The relationship between unemployment and labor-force participation is an important concern in the fields of labor economics and development economics as well.
[Show full text]
Economic Models
Economic models Crucini/Driskill August 25, 2007 List of Figures 1 TheMonk’spaths.......................... 4 2 TheO’Rourkegraph......................... 13 d 3 Pairs of (PS,Si ) ........................... 20 4 Sd = 1 ............................... 21 i 4PS 3 5 Inverse linear demand function: Yi = 4 . .............. 24 6 Inverse demand functions with different Yi’s............ 24 1 7 PS = d ............................... 27 4Si 1 1 8 PS = d ; PS = d ......................... 27 4Si Si 9 Marketandindividualinversedemands.............. 31 10 Individualinversesupplyfunctions................. 33 11 Market and individual inverse supply . ............... 34 12 Depictingthedemand-supplymodel............... 44 13 Equilibrium with differentincome.................. 45 Contents 1LearningObjectives 2 2 Introduction 3 3 The power and ubiquitousness of models 3 4 The structure, language, and depiction of economic models. 6 4.1 Some definitions........................... 6 4.2 A problem: The effects of an end of EU sugar - production subsidies 7 4.3Elementsofmodels.......................... 7 4.3.1 Variables........................... 7 4.3.2 Logicalstructuringandrepresentation........... 8 4.4Solvingthemodel.......................... 9 4.4.1 Thecanonicalquestion.................... 9 4.4.2 Solutionstrategies...................... 12 1 5 The microeconomic partial-equilibrium model of competitive demand and supply for sugar 14 5.1Anoteonnotation.......................... 14 5.2Demandsubmodel.......................... 15 5.2.1 Variables..........................
[Show full text]
ONS Economic Statistics and Analysis Strategy
Economic statistics and analysis strategy HOTEL September 2016 www.ons.gov.uk Economic statistics and analysis strategy, Office for National Statistics, September 2016 Economic Statistics and Analysis Strategy Office for National Statistics September 2016 Introduction ONS published its Economic Statistics and Analysis Strategy (ESAS) for consultation in May 2016, building upon the National Accounts Strategy1, but going further to cover the whole of Economic Statistics Analysis in the light of the recommendations of the Bean Review. This strategy will be reviewed and updated annually to reflect of changing needs and priorities, and availability of resources, in order to give a clear prioritisation for ONS’s work on economic statistics, but also our research and Joint working agendas. Making explicit ONS’s perceived priorities will allow greater scrutiny and assurance that these are the right ones. ESAS lays out research and development priorities, making it easier for external experts to see the areas where ONS has and would be particularly keen to collaborate. The strategy was published for consultation and ONS thanks all users of ONS economic statistics and other parties, including those in government, academia, and the private sector who provided comments as part of the consultation. These comments have now been taken on board and incorporated into this latest version of the ESAS. Overview The main focus of ONS’s strategy for economic statistics and analysis over the period to 2021 will be in the following areas: 1 Available at Annex 1. 1 Economic statistics and analysis strategy, Office for National Statistics, September 2016 • Taking forward the programme set out in the latest national accounts strategy and work plan to meet international reporting standards according to the agreed timetable; and to improve the measurement of Gross Domestic Product (GDP) through the introduction of constant price supply and use balancing, the adoption of double deflated estimates of gross value added and improved use and specification of price indices.
[Show full text]