John J Heim An Econometric Model of the US Economy Structural Analysis in 56 Equations John J Heim University at Albany-SUNY, Albany, New York, USA ISBN 978-3-319-50680-7 e-ISBN 978-3-319-50681-4 https://doi.org/10.1007/978-3-319-50681-4 The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Library of Congress Control Number: 2017940494 © The Editor(s) (if applicable) and The Author(s) 2017 This work is subject to copyright All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Cover illustration: Lyroky / Alamy Stock Photo Printed on acid-free paper This Palgrave Macmillan imprint is published by Springer Nature The registered company is Springer International Publishing AG This book is dedicated to Susan who has given me so much Preface I left academic life in 1972, after getting my Ph.D At that time large-scale econometric modeling of the economy was the rage; everyone thought it would be just a matter of time before we had “done enough science” to allow economists to discuss economics in the classroom, not in terms of the alphas and betas of theoretical models, but in terms of the real-world coefficients they represent Economics would become the next branch of engineering, or so many thought Much to my surprise, when I returned to academic life 25 years later things had not much progressed Most economists were still using alphas and betas to describe how one variable affects another in economics For lack of vigorous, concerted effort over those 25 years to pursue the hard numbers underlying the theories, and their statistical significance, economists were still just discussing theories with the best “numbers” we had – the abstract alphas and betas of pure theoretical discourse Because we hadn’t disciplined our presentation of theories to those scientifically proven to work, even more theories abounded than was the case in 1972 Worse, the overriding emphasis in economic theory was not on “what works?”, but on “what’s new?” My engineering students knew the difference When I tried to describe macroeconomics as real science , and then described the coefficients that connect one variable to another in alphas and betas, instead of real numbers, they just snickered “Yes, but what is the real relationship?” they would ask, meaning what are the real numbers? “And if you don’t have them, why you call this science?” they would ask Certainly in their engineering courses, where every equation describes what actually works, they were getting real numbers This book attempts to meet that very standard by focusing on what works It attempts to move forward the empirical efforts of Tinbergen, Goldberger, Klein, Eckstein, and Fair the past 80 years to determine what works That is, the effort to convert economics from just theory to hard (by which I mean reliable) science Doing so requires three things First, it requires that the postulates we test have some economic meaning, and not be just some collection of variables we are “running up the flagpole,” to see what happens Second, it requires that the theory-based postulates we test are structured loosely enough so that the data determine what is real, i.e., the exact shape and content of the theory being tested It is not for us to say a priori by how we structure the model we test, whether Keynes’ consumption function, whose principal determinant is current income, is correct, or whether Freidman’s, whose principal determinant is average income (permanent income) is correct Third, it is not for us to claim some empirical result proves some theory is correct, simply because it explains some variation in the economy, in some time period, in some economic model To be correct, it should explain most variance, in most or all time periods, in most or all models This book tries to adhere to these three rules, we think successfully To meet the first condition, its model is built around the theory that we found most consistent with the data To meet the second, the shape (and inclusion) of each equation in the model is data-determined, e.g., there are no predetermined assumptions about what drives consumer or investment spending Third, a large-scale econometric model is needed to capture all the sources of economic variation, and that’s what is used Extensive robustness testing was used to prove that any initial statistical finding was real and not just some spurious artifact of the time period or particular model tested I hope the reader will agree that the models developed in this book adhere to these rules for good engineering science SUNY, AlbanyJohn J Heim Acknowledgements Most of all, I am indebted to Nobel Laureate Robert Solow for providing review comments and suggestions on an earlier draft, as did David Colander and Ray Fair They were a source of inspiration and without their involvement and support, especially Robert Solow’s, this book probably would not have been finished I am also indebted to distinguished econometrician, Kajal Lahiri, for bringing me to SUNY Albany and providing a place where I could work on this book with a minimum of other distractions He has provided a very supportive and intellectually stimulating atmosphere within which to work, and provided guidance on econometric issues through his careful review of an earlier draft I would also be remiss if I did not mention the long line of earlier economists who toiled long and hard as both macroeconomists and econometricians to turn macroeconomics from philosophy into science These economists include Jan Tinbergen, Lawrence Klein, Frank deLeeuw, Arthur Goldberger, and, more recently, Ray Fair Fair has had the doubly difficult job of keeping the strongly scientific Cowles tradition alive during recent decades, when many economists turned to different, less scientific approaches We owe him much For similar reasons, we owe Greg Mankiw much His 2006 article in the Journal of Economic Perspectives convinced many that the detour in the 1980s away from Cowles modeling and toward DSGE has proven unproductive, and helped resurrect interest in Cowles modeling again Solow’s (2010) testimony to Congress reached the same conclusion about DSGE and helped in the same way Nor could the book have been written without the strong support of my wife Sue This book required years full-time work, and before that, considerable part-time work The problems to be resolved required endless long hours at work, and endlessly preoccupied my mind, even at home Sue was always willing to make the sacrifices necessary to cope with all that Finally, I must acknowledge the secretarial assistance provided by Annemarie Hebert She has helped pull together, duplicate, and send out endless drafts of this work Summary The book has two parts: Part I contains 45 equations describing in detail the “product side” of the National Income and Product Accounts (NIPA) It contains tested models of the GDP and its major components, and the determinants of their level of production (Chapters – 19 ) Part II provides 11 additional equations describing how the value of the product generated producing the GDP is distributed among the factors of production For each factor of production there are two equations The first describes the variables that were found to determine each factor’s percentage share of national income The second describes the variables found to determine the total amount (the level ) of each factor’s total income These models describe the variables whose own changes cause the distribution of income among factors to shift from one factor to another over time ( Chapter 20 ) Chapter 19 provides a summary of the substantive findings as to the determinants of GDP and its components Chapter 20 , Section 20.​5 , summarizes the determinants of factor shares and levels of income The Production Side Model Production is treated as a response to aggregate demand (AD) Hence the key determinants of GDP production are expressed as determinants of AD Supply shortages can also affect the level of production, but the empirical evidence indicates that demand is far more commonly the driving factor Fully 85–95% of the variation of GDP over the 50-year period 1960–2010 appears to stem from variation in AD Demand-driven models are commonly thought of as Keynesian models, and to that extent this is a Keynesian model However, when a variable to measure “crowd out” is added to standard Keynesian consumption and investment equations, this model’s conclusions about the effectiveness of fiscal policy in stimulating the economy are just the opposite of Keynes’ Its conclusions about monetary policy conclusions are also not the same The model indicates the stimulus effects of changes in the money supply to be modest at best The 45-equation first part (the production side) includes 30 behavioral equations and 15 identities The identities connect the behavioral equations into a comprehensive model of the real U.S economy The behavioral equations were generally estimated applying strong instrument 2SLS to 1960–2010 data The model includes eight consumption and nine investment equations, including three for personal, corporate, and depreciation allowance savings Two interest rate determination models based on the Taylor rule or the Keynesian LM curve are included Also included are two unemployment determination models, a Phillips curve model, one export function, and two “IS” curve functions determining GDP Other behavioral models are provided for taxes and government spending, recognizing that part of these variables levels is endogenously determined by the state of the economy Two functions describe the determinants of M1 and M2 velocity These are included to show mathematically how fiscal policy can shift the AD curve Extensive efforts were made to ensure that all identification issues were resolved by replacing Hausman-endogenous variables with Waldstrong instruments which were Sargan-tested to ensure they also were not endogenously determined There are 75 variables (or different lags of the same variables) in the 45 equations Robustness testing, a non-negotiable requirement of good science, was exhaustive All models were tested in four different time periods to ensure estimated effects were consistent over time, i.e., immune to Lucas critique All coefficients were also tested for robustness to changes in the model being tested, i.e., to see how additions and subtractions of variables from the model affected the remaining variables estimated effects Because of the pervasiveness of the multicollinearity problem, this type of robustness testing is also a non-negotiable requirement of good science Finally, almost all were tested using OLS as well as 2SLS techniques to allow comparisons with literature of an earlier day, which sometimes used OLS DSGE and VAR methodologies are currently more popular methodologies for macroeconomic modeling Therefore, a lengthy section is included in Chapter discussing the advantages of the older Cowles methodology and why it is used here Chapter is literally a paper within a paper It deals with what may be the most pressing unresolved methodological issue facing macroeconomic modelers today: how to successfully model the macroeconomy the way it actually works , so that models can be reliably used by policy makers to predict consequences of decision-making Early models designed to this were referred to as Cowles Commission models and were very good at explaining the data, though not always 100% successful Cowles models dominated model building from the advent of the econometric revolution up to the mid-1980s However, in the last 30 years, many economists have turned away from Cowles types of modeling in favor of DSGE and VAR Which of these three methods for discerning economic reality is to be preferred? To shed some light on this question, the statistical performance of several VAR and DSGE models are compared with Cowles-type structural models Comparisons are made, or reported from other studies, and include comparisons with a Sims (1980) VAR model, the Smets-Wouters model, FRB/US, and a simplified version of the FRB/NY model These tests overwhelmingly indicate the more Keynesian (Cowles) structural models outperform the others in accurately modeling the actual year-to-year fluctuations of the economy Therefore, they should become the models of choice in future macroeconomic studies analyzing the consequences of changes in economic variables Nobel Laureate economist Robert Solow (2016) concurs; he has said Cowles models far better explain the data than DSGE or VAR models: after reviewing this paper’s analysis of the three methods, Solow wrote … Your arguments in favor of Cowles-type models as against VAR and DSGE models have real weight … I think that you get across that whatever can be said for DSGE models … they are inferior at explaining the facts … You the same for general VAR models After Keynes himself, Solow is arguably the greatest economist of the twentieth century The Income Shares Model Part II of this book ( Chapter 20 ) describes how the income generated producing the GDP is distributed Four equations describe the variables found to determine the level of income received as labor, profit rent, and interest income An additional four equations describe the variables found to affect the percentage share of national income received by each of these factors, that causes factor shares to vary from decade to decade A summary of findings is presented at the beginning of Chapter 20 The econometric methodology used, including exhaustive robustness testing, was the same as used in Part I of the book Methodology Good science requires replicability of results This chapter’s goal was to provide, to the best extent possible, models whose results meet the replicability standard Largely, this goal appears to be achieved, though in some areas more remains to be done Hopefully, future generations of researchers will find it worthwhile to take up where this study leaves off In particular, in some equations we were not able to fully resolve the “left out” variables and multicollinearity problems that affects the credibility of parameter estimates in any economic model In most models 85–95% of the variance is explained However, in some models, there are definitely some “left out” explanatory variables remaining to be found Less of the total variance in the model than we would like is explained by the variables Models with this problem are identified in the text In addition, the problem of multicollinearity needs to be better resolved It is perhaps the most serious impediment to doing good science in economics today To mitigate the problem in this study, we use first differencing, and careful selection of combinations of explanatory variables used In addition, we extensive robustness testing, by adding and subtracting explanatory variables to a model, to ensure (reasonable) model changes not cause marked changes in other parameter estimates For most of our parameter estimates we are able to show these techniques achieved the desired level of stability, but not for all For some models, parameter estimates are still sensitive to exactly what other variables are included in the model (these models are identified in the text) Economists needs to develop better scientific methods for dealing with this problem to Different Factors? Determinants of Labor’s Income Level The only variable found related to the level of labor income in at least three of the four time periods sample was the GDP, significant in all four This makes some sense, since over the long run, labor income can only grow if productivity is growing, causing real GDP to grow The ups and downs of the business cycle, important in explaining short-term variation, largely cancel each other out over the long run In some preliminary testing, without the GDP variable included, the labor force participation rate and unemployment rate both showed a relationship to the level of labor income, but when the GDP was added as a third explanatory variable, they became insignificant, suggesting their variation’s effect on total labor income is more a proxy for GDP variation Determinants of Profits’ Income Level Here again, the major factor affecting the level of total income was the level of GDP But the rate of growth in GDP understates the rate of growth in profit income Profits grew at a faster rate because of the extraordinary growth of profits derived from foreign operations, which causes profit income to grow much faster Determinants of Rent’s Income Level Initial testing in 50-year models indicated the higher mortgage interest rates, the higher rental income, reflecting the fact that mortgage rates reflect the cost of home ownership, and high costs cause consumers to shift housing preferences to rental property In addition, the same preliminary testing indicated that both total labor income growth (and growth in average wages), relative to national income, were negatively related to rental income This indicates a preference for home ownership over renting as incomes rise These three variables were found to be significant determinants in at least three of the four sample periods tested Unfortunately, none of these three variables remained significant when only they alone were rerun in a new regression as determinants of rental income levels So, by this study’s usual standard for robustness, we cannot assure the reader our findings for rental income levels are robust Determinants of the Level of Interest Income The level of consumer and business debt, the level of the prime interest rate, and the level of inflation were all found positively related to the total level of income earned by all interest income recipients A fourth variable was also found significant: the unemployment rate was found negatively related to total interest income Proprietor’s Income In concluding this summary, we note no attempt to analyze the determinants of proprietor income was made Part of proprietor’s income is labor, and part is profit, and we would have liked to include the appropriate share of proprietor income in the analysis of both those categories, but could not This was unfortunate, but necessary, because of the difficulty of sorting out labor from profit income components when analyzing this type of income References Fox, K (1968) Intermediate Economic Statistics New York: John Wiley {\&} Sons Gollin, Douglas (2002) “Getting Income Shares Right” Journal of Political Economy 119(2),458–474 [Crossref] Griffiths, W., Hill, R., and Lim, G (2008, 2011) Principles of Econometrics and Using EViews for Principles of Econometrics Hoboken: John Wiley and Sons Guscina, Anastasia (2006) “Effects of globalization on Labor's Share in National Income” IMF Working Paper 06/94 Hein, Eckhard (2009) Financialisation, Distribution, Capital Accumulation and Productivity Growth in a Post-Kaleckian Model Working Paper, Institute for International Political Economy Berlin, No 01/2009 ILO (2011) “The Labor Share of Income: Determinants and Potential Contribution to Exiting the financial Crisis” Cptr of World of Work Report 2011: Making Markets Work for Jobs Imf (April 2007) “The Globalization of Labor” Chap of World Economic Outlook April 2007 Jaumotte, Florence, and Tytell, Irina (2007) “How Has the Globalization of Labour Affected the Labor Income Share in Advanced Countries? IMF Working Paper No.298 IMF, Washington, DC Krauss, Lawrence M (2012) A Universe From Nothing New York: Atria Press (Simon and Schuster) Mankiw, N.G (2015) “Yes, r>g So What?” American Economic Review: Papers and Proceedings 2015 105(5),43–47 [Crossref] Piketty, Thomas (2014) Capital in the Twenty – First Century Cambridge, MA: Belknap Press of the Harvard University Press [Crossref] Piketty, Thomas (2015) “About Capital in the Twenty – first Century” American Economic Review: Papers and Proceedings 105(5),48–53 [Crossref] Pindyck, R., and Rubinfeld, D (1991) Econometric Models and Economic Forecasts Boston: Irwin McGraw-Hill Solow, Robert M (1956) “A Contribution to the Theory of Economic Growth” Quarterly Journal of Economics 70(1),65–94 Feb 1956 [Crossref] Stockhammer, Engelbert (2012) “Why Have Wage Shares Fallen?” New Perspectives on Wages and Economic Growth International labour Office (ILO), Conditions of Wok and Employment Branch, Geneva References Barro, R.J (1987) “Government Spending, Interest Rates, Prices and Budget Deficits in the United Kingdom, 1701–1918” Journal of Monetary Economics 20, 221–47 Jan 1987 [Crossref] Baumol, W (1952) “The Transactions Demand for Cash: An Inventory Theoretic Approach” Quarterly Journal of Economics 66(4),546–56 [Crossref] Bentolila, Samuel, and Saint-Paul, Gilles (2003) “Explaining Movements in the Labor Share” Contribution to Macroeconomics 3(1) Berkeley, CA Berkeley Press Brayton, F., Levin, A., Tryon, R., and Williams, J.C (1997) “The Evolution of Macro Models at the Federal Reserve Board” Originally Prepared for Carnegie Rochester Conference on Public Policy 47(1),43–81 Nov 22–23 1996, 1997 Revised Feb 7, 1997 Brayton, F., Laubach, T., and Reifschneider, D (2014) “The FRB/US Model: A Tool for Macroeconomic Policy Analysis” FEDS Notes Washington, DC: Board of Governors of the Federal Reserve System Apr 2014 Brayton, F, and Tinsley, P., ed (1996) “A Guide to the FRB/US A Macroeconomic Model of the United States” Division of Research and Statistics, Federal Reserve Board Version 1.0 Oct 1996 Chari, V V., Kehoe, Patrick J., and McGrattan, Ellen R (2009) “New Keynesian Models: Not Yet Useful for Policy Analysis” American Economic Journal: Macroeconomics 1(1),242–66 Christoffel, K., Coenen, G., and Warne, A (2010) “Forecasting With DSGE Models” European Central Bank Working Paper Series 1185 May 2010 Chung, Hess (2015) “The Effects of Forward Guidance in Three Macro Models” FEDS Notes Washington, DC: Board of Governors of the Federal Reserve System Feb 26 Colander, D., Howitt, P, Kirman, A., Leijonhufved, A., and Mehrling, P (2008) “Beyond DSGE Models: Toward an Empirically Based Macroeconomics” American Economic Review: Papers and Proceedings 98(2),236–40 [Crossref] Colander, D (2010) “Testimony Presented to U.S House of Representatives Science and Technology Committee Hearing Entitled ” Building a Science of Economics for the Real World, July 20th, 2010 Cooley, T.F (1997) “Calibrated Models” August 1997 Lecture at UC Davis, pp.1–29 Also essentially the same as “The Nature and Use of Calibrated Models” 7th World Congress of the Econometric Society, Tokyo Aug 1995 Dawkins, C., Srinivasan, T, and Whalley, J (2001) calibration article authors in Heckman, J., and Leamer E eds Cptr 58 of Handbook of Econometrics , Vol Calibration: North Holland Press Del Negro, M, Schorfheide, Smets, F, and Wouters, R (2007) “On the Fit of New-Keynesian Models” Journal of Business \& Economic Statistics 25, 123–49 [Crossref] Del Negro, M., Eusepi, S, Giannoni, M., Sbordone, A., Tambalotti, A., Cocci, M., Hasegawa, R., and Linder, M.H The FRBNY DSGE Model Federal Reserve Bank of New York Staff Reports, 647 Oct 2013 deLeeuw, F., and Gramlich, E (1968) “The Federal Reserve – MIT Economic Model” Federal Reserve Bulletin 54(1),11–40 Jan 1968 Eckstein, O (1983) The DRI Model of the U.S Economy New York: McGraw-Hill Book Company Eckstein, O (2014) FRB/US Equation Documentation Federal Reserve Board Available at www.​federalreserve.​org/​econresdata/​ frbus/​us-models-about.​htm/​frbus/​us-models-about.​htm Eckstein, O (2015) “About (the FRB/US Model)” Federal Reserve Board Available at http://​www.​federalreserve.​gov/​econresdata/​ frbus/​us-models-about/​ Eckstein, O (1963, 2002, 2010–2014) Economic Report of the President Washington, DC: Government Printing Office Eckstein, O (2011) FRB/US Equation Documentation Federal Reserve Board Flow of Funds Accounts of the United States , Data Series TODNS, TBSDODNS, CMDEBT, FGSDODNS, SLGSDODNS, AND DODFS Washington, DC Board of Governors of the Federal Reserve System March 11, 2011 Update Available at www.​federalreserve.​gov/​releases/​z1/​current/​ Edge, R., Kiley, M., and Laforte, P (2010) “A Comparison of Forecast Performance of between Federal Reserve Staff Forecasts, Simple Reduced Form Models, and a DSGE Model” Finance and Economics Discussion Series Division of Research and Statistics and Monetary Affairs, Federal Reserve Board, Washington, DC Edge, R., and Gurkaynak, R (2011) “How Useful are Estimated DSGE Model Forecasts for Central Bankers? Brookings Papers on Economic Activity, Fall 2010 Table 2, p 224 Also available as “How Useful are Estimated{} DSGE Model Forecasts?” Finance and Economics Discussion Series Div of Research and Statistics and Monetary Affairs Federal Reserve Board, Washington, DC, paper {\#}2011–11, Jan 2011 Fair, R.C (2004) Estimating How the Macroeconomy Works Cambridge, MA: Harvard University Press Fair, R.C (2012) “Has macro progressed?” Journal of Macroeconomics 34, 2–10 [Crossref] Fair, R.C (2007) “Evaluating Inflation targeting using a macroeconomic model Economics – ” The Open Access Open Assessment EJournal 1, 1–52, 2007–8 [Crossref] Fernandez-Villaverde, J (2010) “The Econometrics of DSGE Models” Journal of the Spanish Economics Association , SERIEs 1(3–49), Fox, K (1968) Intermediate Economic Statistics New York: John Wiley {\&} Sons Friedman, M (1957) A Theory of the Consumption Function Princeton: Princeton University Press Gale, W., and Orszag, P (2004) “Budget Deficits, National Saving, and Interest Rates” Brookings Papers on Economic Activity 2, 101–87 [Crossref] Gollin, Douglas (2002) “Getting Income Shares Right” Journal of Political Economy 119(2),458–474 [Crossref] Granger, C (1969) “Investigating Causal Relations by Econometric Models and Cross-spectral Methods” Econometrica 37(3),424– 438 [Crossref] Griffiths, W., Hill, R., and Lim, G (2008, 2011) Principles of Econometrics and Using EViews for Principles of Econometrics Hoboken: John Wiley and Sons Guscina, Anastasia (2006) “Effects of globalization on Labor's Share in National Income” IMF Working Paper 06/94 Heckman, J., and Lerner, E (2001) Handbook of Econometrics , Vol Netherlands: Elsevier Heim, J (2007) “How Much Does the Prime Interest Rate Affect U.S Investment?” Journal of the Academy of Business and Economics VII(1) Also available as “Which Interest Rate Should We Use In The “IS” Curve?” Working Paper {\#}0713, Working Papers in Economics, Department of Economics, Rensselaer Polytechnic Institute Heim, J (2008) “The Consumption Function” Review of Business Research 8(2),18–36 Also available under the same title as Working Paper {\#}0805, Working Papers in Economics, Department of Economics, Rensselaer Polytechnic Institute January 2008 Heim, J (2013) “Does `Crowd Out' Offset the Stimulus Effect of Government Deficits? A Large Scale Econometric Study” Working paper 1301, Working Papers in Economics, Department of Economics, Rensselaer Polytechnic Institute June 2013 Heim, J (2014) “Out of Sample Comparisons of Cowles Commission and VAR Models” Unpublished Excel file, 1–2 Hein, Eckhard (2009) Financialisation, Distribution, Capital Accumulation and Productivity Growth in a Post-Kaleckian Model Working Paper, Institute for International Political Economy Berlin, No 01/2009 Hildreth, C (1985) “The Cowles Commission in Chicago, 1939–1955” Discussion Paper No 225, October 1985 Center for Economic Research, Department of economics, University of Minnesota ILO (2011) “The Labor Share of Income: Determinants and Potential Contribution to Exiting the financial Crisis” Cptr of World of Work Report 2011 : Making Markets Work for Jobs Imf (April 2007) “The Globalization of Labor” Chap of World Economic Outlook April 2007 Jaumotte, Florence, and Tytell, Irina (2007) “How Has the Globalization of Labour Affected the Labor Income Share in Advanced Countries? IMF Working Paper No.298 IMF, Washington, DC Jenkins, H (2014) “Does the Fed Read the Election Returns?” Wall Street Journal Nov 7, 2014 Johnston, J (1963) Econometric Methods New York: McGraw-Hill Karabarbounis, Loukas, and Neiman, Brent (2013) “The Global Decline of the Labor Share” Quarterly Journal of Economics doi: 10.​1093/​qje/​qjt032.​ Keynes, J.M (1936) The General Theory of Employment, Interest and Money London: Macmillan Klein, L., and Evans, M (1968) The Wharton Econometric Forecasting Model Philadelphia: Wharton School of Finance and Commerce, University of Pennsylvania Krauss, Lawrence M (2012) A Universe From Nothing New York: Atria Press (Simon and Schuster) Krugman, P and Wells, R (2013) Economics New York: Worth Publishing Kuznets, S (1952) “Proportion of Capital Formation to National Product” American Economic Review 42(2),507–526 May 1952 Leontief, W (1951) The Structure of the American Economy, 1919–1939 , 2nd ed Fair Lawn, NJ: Oxford University Press Lucas, R (1976) “Econometric Policy Evaluation: A Critique” Carnegie-Rochester Conference Series on Public Policy 1, 19–46 [Crossref] Mankiw, N.G (2006) “The Macroeconomist as Scientist and Engineer” Journal of Economic Perspectives 20(4),29–46 [Crossref] Mankiw, N.G (2010) Macroeconomics New York: Worth Publishers Mankiw, N.G (2015) “Yes, r>g So What?” American Economic Review: Papers and Proceedings 2015 105(5),43–47 [Crossref] Mishkin, F (2009) The Economics of Money, Banking and Financial Markets New York: Addison Wesley Modigliani, F., and Ando, A (1963) “The Life Cycle Hypothesis of Saving” American Economic Review 53(1),55–84 March Mountford, A., and Uhlig, H (2008) “What Are The Effects Of Fiscal Policy Shocks?” NBER Working Paper \# 14551 , Dec 2008 Okun, A (1962) “Potential GNP: Its Measurement and Significance” Proceedings of the Business and Economics Statistics Section, American Statistical Association Washington, DC: American Statistical Association Paccagnini, A (2011) “DSGE Model Evaluation and Hybrid Models: A Comparison” European University Institute, Florence Max Weber Program EUI Working Paper MWP 2011/11 Piketty, Thomas (2014) Capital in the Twenty – First Century Cambridge, MA: Belknap Press of the Harvard University Press [Crossref] Piketty, Thomas (2015) “About Capital in the Twenty – first Century” American Economic Review: Papers and Proceedings 105(5),48–53 [Crossref] Pindyck, R., and Rubinfeld, D (1991) Econometric Models and Economic Forecasts Boston: Irwin McGraw-Hill Reiss, A., and Livio, M (2016) “The Puzzle of Dark Energy” Scientific American 314(3),38–43 March 2016 Romer, D (1996) Macroeconomics New York: McGraw Hill Romer, P (2016) “The Trouble With Macroeconomics” Delivered 1/ 5/16as the Commons Memorial lecture of the Omicron Delta Epsilon Society Forthcoming in The American Economist Sbordone, A., Tambalotti, A., Rao, K., and Walsh, K (2010) “Policy Analysis Using DSGE Models: An Introduction” FRBNY Economic Policy Review 16(2),\hbox{23–44.} Oct 2010 Sims, C (1972) “Money, Income, and Causality” The American Economic Review 62(4),540–52 Sept 1972 Sims, C (1980) “Macroeconomics and Reality” Econometrica 48(1),1–48 Jan 1980 [Crossref] Sims, C (2006) “Comment on Dl Negro, Schorfheide, Smets and Wouters” Journal of Business and Economic Statistics 25 (2) 2, 152–154 Available at http://​sims.​prinston.​edu/​yftp/​Ottawa/​SWcomment%20​Slides.​pdf Smets, F., and Wouters, R (2003) “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area” Journal of the European Economic Association 1(5),1123–75 Sept 2003 [Crossref] Smets, F, and Wouters, R (2007) “Shocks and Frictions in U.S Business Cycles: A Baysian DSGE Approach” American Economic Review 97, 568–606 [Crossref] Smith, N (2014) “The Most Damning Critique of DSGE” (Noahpinion, 1/10/14) Available at http://​noahpinionblog.​blogspot.​com/​2014/​ 01/​the-most-damning-critique-of-dsge.​html Solow, R M (2010) Testimony Presented to U.S House of Representatives Science and Technology Committee Hearing Entitled “Building a Science of Economics for the Real World”, July 20th, 2010 Solow, R.M (2016) Letter to J Heim dated 6/29/2016 Solow, Robert M (1956) “A Contribution to the Theory of Economic Growth” Quarterly Journal of Economics 70(1),65–94 Feb 1956 [Crossref] Stock, J.H, and Watson, M.W (2001) “Vector Autoregressions” Journal of Economic Perspectives 15(4),101–15 [Crossref] Stockhammer, Engelbert (2012) “Why Have Wage Shares Fallen?” New Perspectives on Wages and Economic Growth International labour Office (ILO), Conditions of Wok and Employment Branch, Geneva Tinbergen, Jan (1939) Statistical Testing of Business Cycle Theories, Volume Business Cycles in the United States of America , 1919–1932 Geneva: League of Nations Tinsley, P (2002) “Rational Error Correction” Computational Economics Tobin, J (1956) “The Interest Elasticity of the Transactions Demand for Cash” Review of Economics and Statistics 38(3),241–47 Aug 1956 [Crossref] Tovar, C (2008) “DSGE Models and Central Banks” Working Papers No 258, Monetary and Economic Department, Bank for international Settlements Triola, M (2011) Elementary Statistics New York: Pearson Uhlig, H (2005) “What are the Effects of Monetary Policy on Output The Results of an Agnostic Identification Procedure” Journal of Monetary Economics 52(2),381–419 [Crossref] Wickens, M (2012) “How Useful are DSGE Macroeconomic Models for Forecasting?” CEPR Discussion Papers 9049 and, Open Economies Review , Springer 25(1),171–93 Feb 2014 [Crossref] Wilcox, D (2015) Correspondence with J Heim dated 7/17/2015 Woodridge, J (2003) Introductory Economics: A Modern Approach , 2nd ed Mason, OH: Thomson South Western Woodridge, J (2012) Economic Report of the President Washington, DC: Government Printing Office (also reports for 1963, 2010) Woodridge, J (2012) Economic Report of the President Washington, DC: Government Printing Office (also reports for 1963, 2011) Index A Accelerator Adaptive expectations AD curve Aggregate Demand(AD) Aggregate Supply(AS) Appliances AR(1) ARIMA AS curve Astronomers Autoregressive coefficients Autos Average error of fit B Behavioral equations Bentolila, S Bond interest rate Brayton, F Brayton, F Business borrowing C Calibration Capacity Utilization Rate Christoffel, K Chung, H Clothing Coenen, G Colander, D Consumer borrowing Consumer confidence index Consumer durables Consumer Goods Consumer nondurables Consumer services Consumption Cooley, T F Corporate profits Correlation Cost push inflation Cowles Cowles Commission Cowles Commission Models Cowles Foundation Cowles model Cowles–type model Cowls methodology Crowd out Current income D DeLeeuw, F Del Negro, M Dependent variable Depreciation Depreciation allowance Disposable income Domestically Produced Consumer Goods Domestically produced investment goods DRI Model DSGE methodology DSGE modeling Durbin Watson test Dynamic Stochastic General Equilibrium (DSGE) E Eckstein, Otto Econometric model Economic philosophy Economic Report of the President Economic science Edge, R Endogeneity Endogenous Engineering Engineering Manual Engineering manual Equation of Exchange Euler condition Exchange rate Exogenous Explained variance Explanatory model Explanatory variable Export demand Exports F Factories Factor Shares Factors of production Fair, Ray Federal Funds interest rate Federal Reserve Board Fernandez–Villaverde, J st In Stepwise st Order Autocorrelation st Out Stepwise Fiscal policy Fisher, Irving Fixed Plant and equipment investment Flow of Funds Food Products Forecasting model Foreign borrowing Foreign Profits FRB/NY FRB/US Friedman, M Furniture G Gale, W GDP Identity GDP, income side GDP, product side Gollin, D Government deficit Government receipts Government spending deficits Government spending, goods and services Government spending, total Government spending, transfers Gramlich, E Granger, C Griffiths,W Gross Domestic Product (GDP) Gurnayak, R Guscina, A H Hausman Heim, J Heteroskedasticity Hill, R Housing I Identification Identities ILO IMF Imported Consumer goods Imported investment goods Imports Income distribution Instruments Instruments Interest income Interest income, level Interest income, share interest rate Inventory investment Investment IS curve IS curve J Jaumotte, F K Katrina Keynesian Keynesian Mechanics Keynes, J M Klein, Lawrence Krauss, L L Labor income Labor income, level Labor income, share Labor productivity Lagged variables Large scale econometric model “left out” variables Leontief, V Lifetime income Lim, G Livio, M LM curve LM curve Loanable funds Lucas Critique Lucas, R M M1 M1 Velocity M2 M2 Velocity Macroeconomics Macroeconomy Macro Foundations Mankiw, N.G Manufactured goods Manufacturing Marginal product of Capital Marginal Product of Labor Mean square error Methodology Microeconomics Micro-foundations Model specification Model specification robust Modigliani, F Monetary policy Monetary policy Mortgage interest rate Mountford, A N National income National Income and Product Accounts Neoclassical Mechanics Neoclassical models New Keynesian models NIPA Nonstationarity NYSE Composite Index O Okun Okun’s Law “Old” Keynesian models OLS OPEC Orszag, P P Paccagnini, A Participation Rate % Unionized Phillips Curve Piketty, T Population Population Age Distribution Prime Interest Rate Profit income Profit income, level Profit income, share Profits R R2 Rational Expectations Real Wage Regression Coefficients Reiss, A Rental income Rental income, level Rental income, share Residential construction Residential investment RHS Robustness Robustness testing Rule of thumb S Saint-Paul, G Sample period Samuelson accelerator Sargan Savings, corporate Savings, depreciation Savings, personal Sbordone, A Scientific Self Evident Truths Semi-manufactured goods Serial correlation Shoc08 Shock05 Shock09 Shock73 Shock78 Shock86 Shock93 Shoes Significance levels Sims, C Smets, F Smith, N Solow, R Stationarity Stepwise regression Stockhammer, E Stock, J and Watson, M Strong instruments Structural Models Structural vector autoregressive model (SVAR) SVAR methodology T Tax deficits Taxes Taylor Rule Technology Shocks Time period robust Tinbergen, Jan Tinsley, P Tobin’s q Total Consumer Spending Total investment Total investment spending Tovar, C Treasury bill interest rate Triola, M Two Stage Least Squares (2SLS) Tytell, I U Uhlig, H Unemployment inflation V Variance VAR methodology Vector autoregressive model (VAR) Velocity W Wald Warne, A Wharton Econometric Model Wickens, M Wilcox, D Wouters, R ... Estimates of the Determinants of Investment Borrowing 5.​9 Determinants of Spending on Fixed Plant and Equipment Investment (OLS) 5.​10 Determinants of Spending on Fixed Plant and Equipment Investment... Identifying the Determinants of Investment Spending and Borrowing 5.​1 OLS Estimates of the Determinants of Total Investment Spending 5.​2 2SLS Estimates of the Determinants of Total Investment 5.​3... contains 45 equations describing in detail the “product side” of the National Income and Product Accounts (NIPA) It contains tested models of the GDP and its major components, and the determinants