Assessing Applied Econometric Results
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Integrated Regional Econometric and Input-Output Modeling
Integrated Regional Econometric and Input-Output Modeling Sergio J. Rey12 Department of Geography San Diego State University San Diego, CA 92182 [email protected] January 1999 1Part of this research was supported by funding from the San Diego State Uni- versity Foundation Defense Conversion Center, which is gratefully acknowledged. 2This paper is dedicated to the memory of Philip R. Israilevich. Abstract Recent research on integrated econometric+input-output modeling for re- gional economies is reviewed. The motivations for and the alternative method- ological approaches to this type of analysis are examined. Particular atten- tion is given to the issues arising from multiregional linkages and spatial effects in the implementation of these frameworks at the sub-national scale. The linkages between integrated modeling and spatial econometrics are out- lined. Directions for future research on integrated econometric and input- output modeling are identified. Key Words: Regional, integrated, econometric, input-output, multire- gional. Integrated Regional Econometric+Input-Output Modeling 1 1 Introduction Since the inception of the field of regional science some forty years ago, the synthesis of different methodological approaches to the study of a region has been a perennial theme. In his original “Channels of Synthesis” Isard conceptualized a number of ways in which different regional analysis tools and techniques relating to particular subsystems of regions could be inte- grated to achieve a comprehensive modeling framework (Isard et al., 1960). As the field of regional science has developed, the term integrated model has been used in a variety of ways. For some scholars, integrated denotes a model that considers more than a single substantive process in a regional context. -
2015: What Is Made in America?
U.S. Department of Commerce Economics and2015: Statistics What is Made Administration in America? Office of the Chief Economist 2015: What is Made in America? In October 2014, we issued a report titled “What is Made in America?” which provided several estimates of the domestic share of the value of U.S. gross output of manufactured goods in 2012. In response to numerous requests for more current estimates, we have updated the report to provide 2015 data. We have also revised the report to clarify the methodological discussion. The original report is available at: www.esa.gov/sites/default/files/whatismadeinamerica_0.pdf. More detailed industry By profiles can be found at: www.esa.gov/Reports/what-made-america. Jessica R. Nicholson Executive Summary Accurately determining how much of our economy’s total manufacturing production is American-made can be a daunting task. However, data from the Commerce Department’s Bureau of Economic Analysis (BEA) can help shed light on what percentage of the manufacturing sector’s gross output ESA Issue Brief is considered domestic. This report works through several estimates of #01-17 how to measure the domestic content of the U.S. gross output of manufactured goods, starting from the most basic estimates and working up to the more complex estimate, domestic content. Gross output is defined as the value of intermediate goods and services used in production plus the industry’s value added. The value of domestic content, or what is “made in America,” excludes from gross output the value of all foreign-sourced inputs used throughout the supply March 28, 2017 chains of U.S. -
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. -
Economics 2 Professor Christina Romer Spring 2019 Professor David Romer LECTURE 16 TECHNOLOGICAL CHANGE and ECONOMIC GROWTH Ma
Economics 2 Professor Christina Romer Spring 2019 Professor David Romer LECTURE 16 TECHNOLOGICAL CHANGE AND ECONOMIC GROWTH March 19, 2019 I. OVERVIEW A. Two central topics of macroeconomics B. The key determinants of potential output C. The enormous variation in potential output per person across countries and over time D. Discussion of the paper by William Nordhaus II. THE AGGREGATE PRODUCTION FUNCTION A. Decomposition of Y*/POP into normal average labor productivity (Y*/N*) and the normal employment-to-population ratio (N*/POP) B. Determinants of average labor productivity: capital per worker and technology C. What we include in “capital” and “technology” III. EXPLAINING THE VARIATION IN THE LEVEL OF Y*/POP ACROSS COUNTRIES A. Limited contribution of N*/POP B. Crucial role of normal capital per worker (K*/N*) C. Crucial role of technology—especially institutions IV. DETERMINANTS OF ECONOMIC GROWTH A. Limited contribution of N*/POP B. Important, but limited contribution of K*/N* C. Crucial role of technological change V. HISTORICAL EVIDENCE OF TECHNOLOGICAL CHANGE A. New production techniques B. New goods C. Better institutions VI. SOURCES OF TECHNOLOGICAL PROGRESS A. Supply and demand diagram for invention B. Factors that could shift the demand and supply curves C. Does the market produce the efficient amount of invention? D. Policies to encourage technological progress Economics 2 Christina Romer Spring 2019 David Romer LECTURE 16 Technological Change and Economic Growth March 19, 2019 Announcements • Problem Set 4 is being handed out. • It is due at the beginning of lecture on Tuesday, April 2. • The ground rules are the same as on previous problem sets. -
A Better Measure of Economic Growth: Gross Domestic Output (Gdo)
COUNCIL OF ECONOMIC ADVISERS ISSUE BRIEF JULY 2015 A BETTER MEASURE OF ECONOMIC GROWTH: GROSS DOMESTIC OUTPUT (GDO) The growth of total economic output affects our assessment of current well-being as well as decisions about the future. Measuring the strength of the economy, however, can be difficult as it depends on surveys and administrative source data that are necessarily imperfect and incomplete. The total output of the economy can be measured in two distinct ways—Gross Domestic Product (GDP), which adds consumption, investment, government spending, and net exports; and Gross Domestic Income (GDI), which adds labor compensation, business profits, and other sources of income. In theory these two measures of output should be identical; however, they differ in practice because of measurement error. With today’s annual revision, the Bureau of Economic Analysis (BEA) began publishing a new measure of U.S. output—the “average of GDP and GDI”—which the Council of Economic Advisers (CEA) will refer to as Gross Domestic Output (GDO).1 This issue brief describes GDO, reviews its recent trends, and explains why it can be a more accurate measure of current economic growth and a better predictor of future economic growth than either GDP or GDI alone. What is Gross Domestic Output (GDO)? The first estimate of quarterly GDP is released nearly a month after each quarter’s end. Owing to data lags, GDI What we are calling “GDO” is the average of two existing is generally first released nearly two months after series, the headline Gross Domestic Product (GDP) and quarter’s end, along with the second estimate of GDP.2 its lesser-known counterpart, Gross Domestic Income As a result, with today’s advance GDP release, GDI and (GDI). -
Input-Output Models and Economic Impact Analysis: What They Can and Cannot Tell Us by Aaron Mcnay, Economist
Montana Economy at a Glance April 2013 Input-Output Models and Economic Impact Analysis: What they can and cannot tell us by Aaron McNay, Economist What economic impacts does a new business have in a region when it first opens its doors? What happens to business creation, or job growth, These questions have at when income taxes are increased or tax credits are least one thing in common, provided to businesses? they can each be examined in detail through a process How much does traffic decline on highways known as economic impact and roads when the price of gasoline increases? analysis. At a basic level, economic impact analysis examines the sectors in the area’s economy. For example, what happens economic effects that a business, project, governmental policy, when an automobile manufacturer increases the number of or economic event has on the economy of a geographic area. cars it produces each month? To increase production, the car At a more detailed level, economic impact models work by manufacturer will need to hire more workers, which directly modeling two economies; one economy where the economic increases total employment in the area. However, the car event being examined occurred and a separate economy manufacturer will also need to purchase more aluminum, steel, where the economic event did not occur. By comparing the and other goods that are used in the manufacturing process. two modeled economies, it is possible to generate estimates As the automobile manufacturer purchases more steel and of the total impact the project, businesses, or policy had on other inputs, the manufacturers of the goods, such as steel an area’s economic output, earnings, and employment. -
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. -
Fully Dynamic Input-Output/System Dynamics Modeling for Ecological-Economic System Analysis
sustainability Article Fully Dynamic Input-Output/System Dynamics Modeling for Ecological-Economic System Analysis Takuro Uehara 1,* ID , Mateo Cordier 2,3 and Bertrand Hamaide 4 1 College of Policy Science, Ritsumeikan University, 2-150 Iwakura-Cho, Ibaraki City, 567-8570 Osaka, Japan 2 Research Centre Cultures–Environnements–Arctique–Représentations–Climat (CEARC), Université de Versailles-Saint-Quentin-en-Yvelines, UVSQ, 11 Boulevard d’Alembert, 78280 Guyancourt, France; [email protected] 3 Centre d’Etudes Economiques et Sociales de l’Environnement-Centre Emile Bernheim (CEESE-CEB), Université Libre de Bruxelles, 44 Avenue Jeanne, C.P. 124, 1050 Brussels, Belgium 4 Centre de Recherche en Economie (CEREC), Université Saint-Louis, 43 Boulevard du Jardin botanique, 1000 Brussels, Belgium; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +81-754663347 Received: 1 May 2018; Accepted: 25 May 2018; Published: 28 May 2018 Abstract: The complexity of ecological-economic systems significantly reduces our ability to investigate their behavior and propose policies aimed at various environmental and/or economic objectives. Following recent suggestions for integrating nonlinear dynamic modeling with input-output (IO) modeling, we develop a fully dynamic ecological-economic model by integrating IO with system dynamics (SD) for better capturing critical attributes of ecological-economic systems. We also develop and evaluate various scenarios using policy impact and policy sensitivity analyses. The model and analysis are applied to the degradation of fish nursery habitats by industrial harbors in the Seine estuary (Haute-Normandie region, France). The modeling technique, dynamization, and scenarios allow us to show trade-offs between economic and ecological outcomes and evaluate the impacts of restoration scenarios and water quality improvement on the fish population. -
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. -
PRINCIPLES of MICROECONOMICS 2E
PRINCIPLES OF MICROECONOMICS 2e Chapter 8 Perfect Competition PowerPoint Image Slideshow Competition in Farming Depending upon the competition and prices offered, a wheat farmer may choose to grow a different crop. (Credit: modification of work by Daniel X. O'Neil/Flickr Creative Commons) 8.1 Perfect Competition and Why It Matters ● Market structure - the conditions in an industry, such as number of sellers, how easy or difficult it is for a new firm to enter, and the type of products that are sold. ● Perfect competition - each firm faces many competitors that sell identical products. • 4 criteria: • many firms produce identical products, • many buyers and many sellers are available, • sellers and buyers have all relevant information to make rational decisions, • firms can enter and leave the market without any restrictions. ● Price taker - a firm in a perfectly competitive market that must take the prevailing market price as given. 8.2 How Perfectly Competitive Firms Make Output Decisions ● A perfectly competitive firm has only one major decision to make - what quantity to produce? ● A perfectly competitive firm must accept the price for its output as determined by the product’s market demand and supply. ● The maximum profit will occur at the quantity where the difference between total revenue and total cost is largest. Total Cost and Total Revenue at a Raspberry Farm ● Total revenue for a perfectly competitive firm is a straight line sloping up; the slope is equal to the price of the good. ● Total cost also slopes up, but with some curvature. ● At higher levels of output, total cost begins to slope upward more steeply because of diminishing marginal returns. -
The Revenue Functions of a Monopoly
SOLUTIONS 3 Microeconomics ACTIVITY 3-10 The Revenue Functions of a Monopoly At the opposite end of the market spectrum from perfect competition is monopoly. A monopoly exists when only one firm sells the good or service. This means the monopolist faces the market demand curve since it has no competition from other firms. If the monopolist wants to sell more of its product, it will have to lower its price. As a result, the price (P) at which an extra unit of output (Q) is sold will be greater than the marginal revenue (MR) from that unit. Student Alert: P is greater than MR for a monopolist. 1. Table 3-10.1 has information about the demand and revenue functions of the Moonglow Monopoly Company. Complete the table. Assume the monopoly charges each buyer the same P (i.e., there is no price discrimination). Enter the MR values at the higher of the two Q levels. For example, since total revenue (TR) increases by $37.50 when the firm increases Q from two to three units, put “+$37.50” in the MR column for Q = 3. Table 3-10.1 The Moonglow Monopoly Company Average revenue Q P TR MR (AR) 0 $100.00 $0.00 – – 1 $87.50 $87.50 +$87.50 $87.50 2 $75.00 $150.00 +$62.50 $75.00 3 $62.50 $187.50 +$37.50 $62.50 4 $50.00 $200.00 +$12.50 $50.00 5 $37.50 $187.50 –$12.50 $37.50 6 $25.00 $150.00 –$37.50 $25.00 7 $12.50 $87.50 –$62.50 $12.50 8 $0.00 $0.00 –$87.50 $0.00 2.