Econometrics: Economic Data and Econometric Modeling
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Making Sense of Unemployment Data
PAGE ONE Economics® Making Sense of Unemployment Data Scott A. Wolla, Ph.D., Senior Economic Education Specialist GLOSSARY “Unemployment is like a headache or a high temperature—unpleasant and Cyclical unemployment: Unemployment exhausting but not carrying in itself any explanation of its cause.” associated with recessions in the business —William Henry Beveridge, Causes and Cures of Unemployment cycle. Discouraged worker: Someone who is not working and is not looking for work because of a belief that there are no jobs Job growth has been healthy for five years.1 However, many people still available to him or her. express concern over the health of the overall labor market. For example, Employed: People 16 years and older who Jim Clifton, CEO of Gallup, states that the “official unemployment rate, as have jobs. reported by the U.S. Department of Labor, is extremely misleading.”2 He Frictional unemployment: Unemployment proposes the Gallup Good Jobs rate as a better indicator of the health of the that results when people are new to the labor market. At the heart of Clifton and others’ concern is what the official job market, including recent graduates, or are transitioning from one job to another. unemployment rate actually measures and whether it is a reliable indicator. Labor force: The total number of workers, including both the employed and the The Labor Force: Are You In or Out? unemployed. To measure the unemployment rate, the U.S. Bureau of Labor Statistics Labor force participation rate: The percent- (BLS) surveys 60,000 households—about 110,000 individuals—which age of the working-age population that is in the labor force. -
Ten Tips for Interpreting Economic Data F Jason Furman Chairman, Council of Economic Advisers
Ten Tips for Interpreting Economic Data f Jason Furman Chairman, Council of Economic Advisers July 24, 2015 1. Data is Noisy: Look at Data With Less Volatility and Larger Samples Monthly Employment Growth, 2014-Present Thousands of Jobs 1,000 800 Oct-14: +836/-221 600 400 200 0 -200 Mar-15: Establishment Survey -502/+119 -400 Household Survey (Payroll Concept) -600 Jan-14 Apr-14 Jul-14 Oct-14 Jan-15 Apr-15 Source: Bureau of Labor Statistics. • Some commentators—and even some economists—tend to focus too closely on individual monthly or weekly data releases. But economic data are notoriously volatile. In many cases, a longer-term average paints a clearer picture, reducing the influence of less informative short-term fluctuations. • The household employment survey samples only 60,000 households, whereas the establishment employment survey samples 588,000 worksites, representing millions of workers. 1 2. Data is Noisy: Look Over Longer Periods Private Sector Payroll Employment, 2008-Present Monthly Job Gain/Loss, Seasonally Adjusted 600,000 400,000 200,000 0 -200,000 12-month -400,000 moving average -600,000 -800,000 -1,000,000 2008 2010 2012 2014 Source: Bureau of Labor Statistics. • Long-term moving averages can smooth out short-term volatility. Over the past year, our businesses have added 240,000 jobs per month on average, more than the 217,000 per month added over the prior 12 months. The evolving moving average provides a less noisy underlying picture of economic developments. 2 2. Data is Noisy: Look Over Longer Periods Weekly Unemployment Insurance Claims, 2012-2015 Thousands 450 400 Weekly Initial Jobless Claims 350 300 Four-Week Moving Average 7/18 250 2012 2013 2014 2015 Source: Bureau of Labor Statistics. -
Macroeconomics Course Outline and Syllabus
City University of New York (CUNY) CUNY Academic Works Open Educational Resources New York City College of Technology 2018 Macroeconomics Course Outline and Syllabus Sean P. MacDonald CUNY New York City College of Technology How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/ny_oers/8 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] COURSE OUTLINE FOR ECON 1101 – MACROECONOMICS New York City College of Technology Social Science Department COURSE CODE: 1101 TITLE: Macroeconomics Class Hours: 3, Credits: 3 COURSE DESCRIPTION: Fundamental economic ideas and the operation of the economy on a national scale. Production, distribution and consumption of goods and services, the exchange process, the role of government, the national income and its distribution, GDP, consumption function, savings function, investment spending, the multiplier principle and the influence of government spending on income and output. Analysis of monetary policy, including the banking system and the Federal Reserve System. COURSE PREREQUISITE: CUNY proficiency in reading and writing RECOMMENDED TEXTBOOK and MATERIALS* Krugman and Wells, Eds., Macroeconomics 3rd. ed, Worth Publishers, 2012 Leeds, Michael A., von Allmen, Peter and Schiming, Richard C., Macroeconomics, Pearson Education, Inc., 2006 Supplemental Reading (optional, but informative): Krugman, Paul, End This Depression -
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. -
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. -
Methodology for the National Accounts Main Aggregates Database CONTENTS
Methodology for the National Accounts Main Aggregates Database CONTENTS Page I. INTRODUCTION A. Background ................................................................................................................................................................ 2 B. System of National Accounts ..................................................................................................................................... 2 C. Scope of the database ................................................................................................................................................. 2 D. Collection of data ....................................................................................................................................................... 3 E. Comparability of the national estimates ..................................................................................................................... 3 F. Nomenclature ............................................................................................................................................................. 4 G. Country coverage ....................................................................................................................................................... 5 H. Country groupings ...................................................................................................................................................... 5 I. Revisions ................................................................................................................................................................... -
Chapter 1 the Demand for Economic Statistics
Chapter 1 The Demand for Economic Statistics The media publish economic data on a daily basis. But who decides which statistics are useful and which are not? Why is housework not included in the national income, and why are financial data available in real time, while to know the number of people in employment analysts have to wait for weeks? Contrary to popular belief, both the availability and the nature of economic statistics are closely linked to developments in economic theory, the requirements of political decision-makers, and each country’s way of looking at itself. In practice, statistics are based on theoretical and interpretative reference models, and if these change, so does the picture the statistics paint of the economic system. Thus, the data we have today represent the supply and demand sides of statistical information constantly attempting to catch up with each other, with both sides being strongly influenced by the changes taking place in society and political life. This chapter offers a descriptive summary of how the demand for economic statistics has evolved from the end of the Second World War to the present, characterised by the new challenges brought about by globalisation and the rise of the services sector. 1 THE DEMAND FOR ECONOMIC STATISTICS One of the major functions of economic statistics is to develop concepts, definitions, classifications and methods that can be used to produce statistical information that describes the state of and movements in economic phenomena, both in time and space. This information is then used to analyse the behaviour of economic operators, forecast likely movements of the economy as a whole, make economic policy and business decisions, weigh the pros and cons of alternative investments, etc. -
Linear Cointegration of Nonlinear Time Series with an Application to Interest Rate Dynamics
Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Linear Cointegration of Nonlinear Time Series with an Application to Interest Rate Dynamics Barry E. Jones and Travis D. Nesmith 2007-03 NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. Linear Cointegration of Nonlinear Time Series with an Application to Interest Rate Dynamics Barry E. Jones Binghamton University Travis D. Nesmith Board of Governors of the Federal Reserve System November 29, 2006 Abstract We derive a denition of linear cointegration for nonlinear stochastic processes using a martingale representation theorem. The result shows that stationary linear cointegrations can exhibit nonlinear dynamics, in contrast with the normal assump- tion of linearity. We propose a sequential nonparametric method to test rst for cointegration and second for nonlinear dynamics in the cointegrated system. We apply this method to weekly US interest rates constructed using a multirate lter rather than averaging. The Treasury Bill, Commercial Paper and Federal Funds rates are cointegrated, with two cointegrating vectors. Both cointegrations behave nonlinearly. Consequently, linear models will not fully replicate the dynamics of monetary policy transmission. JEL Classication: C14; C32; C51; C82; E4 Keywords: cointegration; nonlinearity; interest rates; nonparametric estimation Corresponding author: 20th and C Sts., NW, Mail Stop 188, Washington, DC 20551 E-mail: [email protected] Melvin Hinich provided technical advice on his bispectrum computer program. -
An Integration of Econometric and Behavioral Economic Research
This PDF is a selection from an out-of-print volume from the National Bureau of Economic Research Volume Title: The Economic Analysis of Substance Use and Abuse: An Integration of Econometrics and Behavioral Economic Research Volume Author/Editor: Frank J. Chaloupka, Michael Grossman, Warren K. Bickel and Henry Saffer, editors Volume Publisher: University of Chicago Press Volume ISBN: 0-262-10047-2 Volume URL: http://www.nber.org/books/chal99-1 Conference Date: March 27-28, 1997 Publication Date: January 1999 Chapter Title: The Behavioral Economics of Smoking Chapter Author: Warren K. Bickel, Gregory J. Madden Chapter URL: http://www.nber.org/chapters/c11155 Chapter pages in book: (p. 31 - 74) 2 The Behavioral Economics of Smoking Warren K. Bickel and Gregory J. Madden an adequate science of behavior should supply a satisfactory ac- count of individual behavior which is responsible for the data of economics.... -B. F. Skinner (1953) The above quote addresses a point central to our discussion; namely, the rela- tion between the behavior ofindividuals and groups. Traditionally, the behavior of individuals and groups have been the domain of different professions. Indi- vidual behavior was the domain of psychology, while group behavior, in terms of the allocation of scarce resources, was the domain of economics. However, some psychologists in the late 1970s began to observe similarities between the phenomena that they studied and economic concepts and principles (e.g., Alli- son 1979; Green and Rachlin 1975; Hursh 1980; Lea 1978). This precipitated the development of behavioral economics. In the late 1980s, behavioral eco- nomics began to be consistently applied to the study of drug abuse and depen- dence, and today it is an active area of investigation (e.g., Bickel et al. -
An Econometric Model of the Yield Curve with Macroeconomic Jump Effects
An Econometric Model of the Yield Curve with Macroeconomic Jump Effects Monika Piazzesi∗ UCLA and NBER First Draft: October 15, 1999 This Draft: April 10, 2001 Abstract This paper develops an arbitrage-free time-series model of yields in continuous time that incorporates central bank policy. Policy-related events, such as FOMC meetings and releases of macroeconomic news the Fed cares about, are modeled as jumps. The model introduces a class of linear-quadratic jump-diffusions as state variables, which allows for a wide variety of jump types but still leads to tractable solutions for bond prices. I estimate a version of this model with U.S. interest rates, the Federal Reserve's target rate, and key macroeconomic aggregates. The estimated model improves bond pricing, especially at short maturities. The \snake-shape" of the volatility curve is linked to monetary policy inertia. A new monetary policy shock series is obtained by assuming that the Fed reacts to information available right before the FOMC meeting. According to the estimated policy rule, the Fed is mainly reacting to information contained in the yield-curve. Surprises in analyst forecasts turn out to be merely temporary components of macro variables, so that the \hump-shaped" yield response to these surprises is not consistent with a Taylor-type policy rule. ∗I am still looking for words that express my gratitude to Darrell Duffie. I would like to thank Andrew Ang, Michael Brandt, John Cochrane, Heber Farnsworth, Silverio Foresi, Lars Hansen, Ken Judd, Thomas Sargent, Ken Singleton, John Shoven, John Taylor, and Harald Uhlig for helpful suggestions; and Martin Schneider for extensive discussions. -
Econometric Theory
ECONOMETRIC THEORY MODULE – I Lecture - 1 Introduction to Econometrics Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Econometrics deals with the measurement of economic relationships. It is an integration of economic theories, mathematical economics and statistics with an objective to provide numerical values to the parameters of economic relationships. The relationships of economic theories are usually expressed in mathematical forms and combined with empirical economics. The econometrics methods are used to obtain the values of parameters which are essentially the coefficients of mathematical form of the economic relationships. The statistical methods which help in explaining the economic phenomenon are adapted as econometric methods. The econometric relationships depict the random behaviour of economic relationships which are generally not considered in economics and mathematical formulations. It may be pointed out that the econometric methods can be used in other areas like engineering sciences, biological sciences, medical sciences, geosciences, agricultural sciences etc. In simple words, whenever there is a need of finding the stochastic relationship in mathematical format, the econometric methods and tools help. The econometric tools are helpful in explaining the relationships among variables. 3 Econometric models A model is a simplified representation of a real world process. It should be representative in the sense that it should contain the salient features of the phenomena under study. In general, one of the objectives in modeling is to have a simple model to explain a complex phenomenon. Such an objective may sometimes lead to oversimplified model and sometimes the assumptions made are unrealistic. In practice, generally all the variables which the experimenter thinks are relevant to explain the phenomenon are included in the model. -
An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting
An Econometric Model of International Growth Dynamics for Long-horizon Forecasting June 3, 2020 Ulrich K. Müller Department of Economics, Princeton University James H. Stock Department of Economics, Harvard University and the National Bureau of Economic Research and Mark W. Watson* Department of Economics and the Woodrow Wilson School, Princeton University and the National Bureau of Economic Research * For helpful comments we thank participants at several seminars and in particular those at the Resources for the Future workshop on Long-Run Projections of Economic Growth and Discounting. Müller acknowledges financial support from the National Science Foundation grant SES-1919336. An earlier version of this paper used the title "An Econometric Model of International Growth Dynamics." Abstract We develop a Bayesian latent factor model of the joint long-run evolution of GDP per capita for 113 countries over the 118 years from 1900 to 2017. We find considerable heterogeneity in rates of convergence, including rates for some countries that are so slow that they might not converge (or diverge) in century-long samples, and a sparse correlation pattern (“convergence clubs”) between countries. The joint Bayesian structure allows us to compute a joint predictive distribution for the output paths of these countries over the next 100 years. This predictive distribution can be used for simulations requiring projections into the deep future, such as estimating the costs of climate change. The model’s pooling of information across countries results in tighter prediction intervals than are achieved using univariate information sets. Still, even using more than a century of data on many countries, the 100-year growth paths exhibit very wide uncertainty.