Estimation of Investment in Residential and Nonresidential Structures v2.0

Size: px
Start display at page:

Download "Estimation of Investment in Residential and Nonresidential Structures v2.0"

Transcription

1 Esimaion of Invesmen in Residenial and Nonresidenial Srucures v2.0 Ocober 2015 In he REMI model, he invesmen expendiures depends on he gap beween he opimal capial socks and he acual capial socks. The general expressions of boh residenial and nonresidenial invesmen can be wrien as I = α(k K (1 I = invesmen in ime period ; K = opimal capial socks in ime period ; K = acual capial socks in ime period ; α = he speed of adjusmen. The speed of adjusmen measures he proporion of gap ha is eliminaed by invesmen each year and is he coefficien o be esimaed. In each ime period, he acual capial socks equal he capial socks a he end of he previous ime period depreciaed during he curren ime period, so he invesmen equaion can be expressed as I = α[k (1 d K 1 ] (2 d = depreciaion rae of ime period ; K 1 = capial socks of he previous ime period 1. The acual capial socks equal he depreciaed capial socks of he las ime period plus invesmen, such ha K = (1 d K 1 + I (3 1

2 Similarly, K 1 is calculaed as K 1 = (1 d 1 K 2 + I 1 (4 Using equaion (3 and (4, we replace he acual capial socks wih he depreciaed capial socks and invesmen of previous ime periods, so ha acual capial socks can be wrien as 1 K = K 0 (1 d i + I i (1 d j (5 K 0 = he inial capial socks. Subsiuing equaion (5 ino equaion (2 produces 1 I = α {K K 0 (1 d i + I i [ (1 d j ]} (6 The opimal regional capial socks for residenial and nonresidenial srucures are calculaed as shares of opimal naional capial socks. The opimal residenial capial sock depends on he regional share of real disposable income and he regional capial preference facor, such ha K R,r = β R r ( RYD,r R K RYD,u (7,u RYD = real disposable income; β = regional preference for residenial capial; R denoes residenial capial; r denoes regional; and u denoes capial. The opimal nonresidenial capial socks depend on he regional share of employmen, labor coss and capial coss, such ha ( ARW,r ( AE,r K N,r = β N ARW,u AE,u r [ ( ARC N ] K,u (8,r ARC,u 2

3 ARW = labor coss; AE = employmen; ARC = capial coss; N denoes nonresidenial capial. Subsiuing equaion (7 and (8 ino (6, we could ransform he invesmen equaion for residenial and nonresidenial invesmen ino I R,r = α R {β R ( RYD,r K R RYD,u K R 0 (1 d R i,u 1 + I R i [ (1 d R j ]} (9 ( ARW,r ( AE,r I N,r = α N {β N ARW,u AE,u [ ( ARC N ] K,u K N 0 (1 d N i,r ARC,u 1 + I N i [ (1 d N j ]} (10 α, β and K 0 are unknown parameers o be esimaed. To solve he equaions, we specify equaion (1 for he naion and he opimal naional capial socks can be expressed as K u = I u α + K u (11 Using equaion (9 and (11, we could solve for he invesmen in residenial srucures. For simpliciy, we le A R = (1 d i R 1 B R = I R i [ (1 d R j ] C R = β R ( RYD,r [ I u RYD,u α R + (1 d i R K 1,u R ] so he invesmen equaion for residenial srucures can be wrien as 3

4 I R,r = K R 0 ( α R A R α R B R + β R α R R C (12 Similarly, we use equaion (10 and (11 o solve for invesmen in nonresidenial srucures. Assuming A N = (1 d i N 1 B R = I R i [ (1 d R j ] ( ARW,r C N = β N ARW ( AE,r,u AE,u [ ( ARC,r ARC,u N ] ] [ I u α N + (1 d i N K 1,u we rewrie he invesmen equaion for nonresidenial invesmen as I N,r = K N 0 ( α N A N α N B N + β N α N N C (13 Therefore, he general final invesmen equaion for boh residenial and nonresidenial invesmen is I = K 0 ( α A αb + β αc (14 Daa We use panel daa of 50 saes and Washing D.C. from 1999 o 2013 o esimae he invesmen equaions for residenial and nonresidenial srucures separaely. The naional capial socks and real depreciaion rae for residenial and nonresidenial fixed asses are from he Bureau of Economic Analysis (BEA. The real disposable income in he residenial invesmen equaion is from he REMI PI+ V1.7. We use he privae nonfarm employmen, relaive composie labor coss, and he relaive capial coss daa from PI+ V1.7 for he employmen share, relaive labor coss, and relaive capial coss in he nonresidenial invesmen equaion. Real invesmen daa a he naional level are from he BEA privae fixed invesmen in residenial and nonresidenial srucures. The sae-level invesmen daa need o be consruced. For he invesmen in residenial srucures, we uilize he building permis daa from Census new privaely owned housing unis auhorized valuaion o esimae he regional share of he oal naional invesmen. Census provides daa 4

5 of privae nonresidenial consrucion pu in place by sae, which is used o esimae he regional nonresidenial invesmen expendiures. Esimaion and Resul To esimae he equaion, we loop over a range of values for α. In each loop, we rea α as known and plug he value of α ino he equaion. Therefore, he arge equaion is ransformed ino an equaion wih linear parameers. We apply fixed effecs models o esimae he arge equaion and record he leas sum of squared residuals for each loop. The value of α ha minimizes he sum of squared residuals is he final esimaed speed of adjusmen. The esimaed speed of adjusmen for residenial invesmen is Thus, 21.7 percen of gap beween opimal and acual socks of residenial capial are eliminaed each year. The esimaed speed of adjusmen for nonresidenial invesmen is 0.078, indicaing ha only 7.8 percen of gap beween opimal and acual socks of nonresidenial capial are eliminaed each year. The following able presens a comparison of esimaed speeds of adjusmen from differen versions of models. Our new esimaes are slighly higher for boh of he wo ses of regressions. Comparison of Esimaed Speeds of Adjusmen New Esimaes Curren Model Coefficiens 2001 Esimaes 1993 Esimaes Residenial Invesmen Non-residenial Invesmen

Macroeconomic Theory Ph.D. Qualifying Examination Fall 2005 ANSWER EACH PART IN A SEPARATE BLUE BOOK. PART ONE: ANSWER IN BOOK 1 WEIGHT 1/3

Macroeconomic Theory Ph.D. Qualifying Examination Fall 2005 ANSWER EACH PART IN A SEPARATE BLUE BOOK. PART ONE: ANSWER IN BOOK 1 WEIGHT 1/3 Macroeconomic Theory Ph.D. Qualifying Examinaion Fall 2005 Comprehensive Examinaion UCLA Dep. of Economics You have 4 hours o complee he exam. There are hree pars o he exam. Answer all pars. Each par has

More information

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models Journal of Saisical and Economeric Mehods, vol.1, no.2, 2012, 65-70 ISSN: 2241-0384 (prin), 2241-0376 (online) Scienpress Ld, 2012 A Specificaion Tes for Linear Dynamic Sochasic General Equilibrium Models

More information

Problem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims

Problem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims Problem Se 5 Graduae Macro II, Spring 2017 The Universiy of Nore Dame Professor Sims Insrucions: You may consul wih oher members of he class, bu please make sure o urn in your own work. Where applicable,

More information

Solutions to Odd Number Exercises in Chapter 6

Solutions to Odd Number Exercises in Chapter 6 1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b

More information

Problem Set on Differential Equations

Problem Set on Differential Equations Problem Se on Differenial Equaions 1. Solve he following differenial equaions (a) x () = e x (), x () = 3/ 4. (b) x () = e x (), x (1) =. (c) xe () = + (1 x ()) e, x () =.. (An asse marke model). Le p()

More information

Final Exam. Tuesday, December hours

Final Exam. Tuesday, December hours San Francisco Sae Universiy Michael Bar ECON 560 Fall 03 Final Exam Tuesday, December 7 hours Name: Insrucions. This is closed book, closed noes exam.. No calculaors of any kind are allowed. 3. Show all

More information

Economics 8105 Macroeconomic Theory Recitation 6

Economics 8105 Macroeconomic Theory Recitation 6 Economics 8105 Macroeconomic Theory Reciaion 6 Conor Ryan Ocober 11h, 2016 Ouline: Opimal Taxaion wih Governmen Invesmen 1 Governmen Expendiure in Producion In hese noes we will examine a model in which

More information

Derived Short-Run and Long-Run Softwood Lumber Demand and Supply

Derived Short-Run and Long-Run Softwood Lumber Demand and Supply Derived Shor-Run and Long-Run Sofwood Lumber Demand and Supply Nianfu Song and Sun Joseph Chang School of Renewable Naural Resources Louisiana Sae Universiy Ouline Shor-run run and long-run implied by

More information

Testing for a Single Factor Model in the Multivariate State Space Framework

Testing for a Single Factor Model in the Multivariate State Space Framework esing for a Single Facor Model in he Mulivariae Sae Space Framework Chen C.-Y. M. Chiba and M. Kobayashi Inernaional Graduae School of Social Sciences Yokohama Naional Universiy Japan Faculy of Economics

More information

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate. Inroducion Gordon Model (1962): D P = r g r = consan discoun rae, g = consan dividend growh rae. If raional expecaions of fuure discoun raes and dividend growh vary over ime, so should he D/P raio. Since

More information

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon 3..3 INRODUCION O DYNAMIC OPIMIZAION: DISCREE IME PROBLEMS A. he Hamilonian and Firs-Order Condiions in a Finie ime Horizon Define a new funcion, he Hamilonian funcion, H. H he change in he oal value of

More information

ANSWERS TO EVEN NUMBERED EXERCISES IN CHAPTER 6 SECTION 6.1: LIFE CYCLE CONSUMPTION AND WEALTH T 1. . Let ct. ) is a strictly concave function of c

ANSWERS TO EVEN NUMBERED EXERCISES IN CHAPTER 6 SECTION 6.1: LIFE CYCLE CONSUMPTION AND WEALTH T 1. . Let ct. ) is a strictly concave function of c John Riley December 00 S O EVEN NUMBERED EXERCISES IN CHAPER 6 SECION 6: LIFE CYCLE CONSUMPION AND WEALH Eercise 6-: Opimal saving wih more han one commodiy A consumer has a period uiliy funcion δ u (

More information

A Note on Public Debt, Tax-Exempt Bonds, and Ponzi Games

A Note on Public Debt, Tax-Exempt Bonds, and Ponzi Games WP/07/162 A Noe on Public Deb, Tax-Exemp Bonds, and Ponzi Games Berhold U Wigger 2007 Inernaional Moneary Fund WP/07/162 IMF Working Paper Fiscal Affairs Deparmen A Noe on Public Deb, Tax-Exemp Bonds,

More information

Lecture Notes 5: Investment

Lecture Notes 5: Investment Lecure Noes 5: Invesmen Zhiwei Xu (xuzhiwei@sju.edu.cn) Invesmen decisions made by rms are one of he mos imporan behaviors in he economy. As he invesmen deermines how he capials accumulae along he ime,

More information

Estimation Uncertainty

Estimation Uncertainty Esimaion Uncerainy The sample mean is an esimae of β = E(y +h ) The esimaion error is = + = T h y T b ( ) = = + = + = = = T T h T h e T y T y T b β β β Esimaion Variance Under classical condiions, where

More information

Lecture Notes 3: Quantitative Analysis in DSGE Models: New Keynesian Model

Lecture Notes 3: Quantitative Analysis in DSGE Models: New Keynesian Model Lecure Noes 3: Quaniaive Analysis in DSGE Models: New Keynesian Model Zhiwei Xu, Email: xuzhiwei@sju.edu.cn The moneary policy plays lile role in he basic moneary model wihou price sickiness. We now urn

More information

Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model:

Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model: Dynamic Economeric Models: A. Auoregressive Model: Y = + 0 X 1 Y -1 + 2 Y -2 + k Y -k + e (Wih lagged dependen variable(s) on he RHS) B. Disribued-lag Model: Y = + 0 X + 1 X -1 + 2 X -2 + + k X -k + e

More information

15.023J / J / ESD.128J Global Climate Change: Economics, Science, and Policy Spring 2008

15.023J / J / ESD.128J Global Climate Change: Economics, Science, and Policy Spring 2008 MIT OpenCourseWare hp://ocw.mi.edu 15.023J / 12.848J / ESD.128J Global Climae Change: Economics, Science, and Policy Spring 2008 For informaion abou ciing hese maerials or our Terms of Use, visi: hp://ocw.mi.edu/erms.

More information

The Brock-Mirman Stochastic Growth Model

The Brock-Mirman Stochastic Growth Model c December 3, 208, Chrisopher D. Carroll BrockMirman The Brock-Mirman Sochasic Growh Model Brock and Mirman (972) provided he firs opimizing growh model wih unpredicable (sochasic) shocks. The social planner

More information

FINM 6900 Finance Theory

FINM 6900 Finance Theory FINM 6900 Finance Theory Universiy of Queensland Lecure Noe 4 The Lucas Model 1. Inroducion In his lecure we consider a simple endowmen economy in which an unspecified number of raional invesors rade asses

More information

T. J. HOLMES AND T. J. KEHOE INTERNATIONAL TRADE AND PAYMENTS THEORY FALL 2011 EXAMINATION

T. J. HOLMES AND T. J. KEHOE INTERNATIONAL TRADE AND PAYMENTS THEORY FALL 2011 EXAMINATION ECON 841 T. J. HOLMES AND T. J. KEHOE INTERNATIONAL TRADE AND PAYMENTS THEORY FALL 211 EXAMINATION This exam has wo pars. Each par has wo quesions. Please answer one of he wo quesions in each par for a

More information

OPTIMAL TIME-CONSISTENT FISCAL POLICY IN AN ENDOGENOUS GROWTH ECONOMY WITH PUBLIC CONSUMPTION AND CAPITAL

OPTIMAL TIME-CONSISTENT FISCAL POLICY IN AN ENDOGENOUS GROWTH ECONOMY WITH PUBLIC CONSUMPTION AND CAPITAL OPTIMAL TIME-CONSISTENT FISCAL POLICY IN AN ENDOGENOUS GROWTH ECONOMY WITH PUBLIC CONSUMPTION AND CAPITAL Alfonso Novales Rafaela Pérez 2 Jesus Ruiz 3 This version: July 5, 204 ABSTRACT In an endogenous

More information

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3 and d = c b - b c c d = c b - b c c This process is coninued unil he nh row has been compleed. The complee array of coefficiens is riangular. Noe ha in developing he array an enire row may be divided or

More information

Online Appendix to Solution Methods for Models with Rare Disasters

Online Appendix to Solution Methods for Models with Rare Disasters Online Appendix o Soluion Mehods for Models wih Rare Disasers Jesús Fernández-Villaverde and Oren Levinal In his Online Appendix, we presen he Euler condiions of he model, we develop he pricing Calvo block,

More information

Decomposing Value Added Growth Over Sectors into Explanatory Factors

Decomposing Value Added Growth Over Sectors into Explanatory Factors Business School Decomposing Value Added Growh Over Secors ino Explanaory Facors W. Erwin Diewer (UBC and UNSW Ausralia) and Kevin J. Fox (UNSW Ausralia) EMG Workshop UNSW 2 December 2016 Summary Decompose

More information

The Goals of his Research To undersand financial crises wih a model of muliple seady sae equilibria To undersand he role of fiscal policy in resoring

The Goals of his Research To undersand financial crises wih a model of muliple seady sae equilibria To undersand he role of fiscal policy in resoring Fiscal Policy Can Reduce Unemploymen: Bu There is a Beer Alernaive Federal Reserve Bank of Alana January 9 h 2010 Roger E. A. Farmer Deparmen of Economics UCLA 1 The Goals of his Research To undersand

More information

1 Derivation of Gravity equations 4. 2 List of countries included in the sample 5

1 Derivation of Gravity equations 4. 2 List of countries included in the sample 5 Appendix Conens 1 Derivaion of Graviy equaions 4 2 Lis of counries included in he sample 5 3 Procedure o consruc in-sample shares for srucural esimaion 5 4 More deails on srucural esimaion 6 5 Algorihm

More information

Solutions Problem Set 3 Macro II (14.452)

Solutions Problem Set 3 Macro II (14.452) Soluions Problem Se 3 Macro II (14.452) Francisco A. Gallego 04/27/2005 1 Q heory of invesmen in coninuous ime and no uncerainy Consider he in nie horizon model of a rm facing adjusmen coss o invesmen.

More information

Introduction to choice over time

Introduction to choice over time Microeconomic Theory -- Choice over ime Inroducion o choice over ime Individual choice Income and subsiuion effecs 7 Walrasian equilibrium ineres rae 9 pages John Riley Ocober 9, 08 Microeconomic Theory

More information

A Dynamic Model of Economic Fluctuations

A Dynamic Model of Economic Fluctuations CHAPTER 15 A Dynamic Model of Economic Flucuaions Modified for ECON 2204 by Bob Murphy 2016 Worh Publishers, all righs reserved IN THIS CHAPTER, OU WILL LEARN: how o incorporae dynamics ino he AD-AS model

More information

Macroeconomics I, UPF Professor Antonio Ciccone SOLUTIONS PROBLEM SET 1

Macroeconomics I, UPF Professor Antonio Ciccone SOLUTIONS PROBLEM SET 1 Macroeconomics I, UPF Professor Anonio Ciccone SOUTIONS PROBEM SET. (from Romer Advanced Macroeconomics Chaper ) Basic properies of growh raes which will be used over and over again. Use he fac ha he growh

More information

BOKDSGE: A DSGE Model for the Korean Economy

BOKDSGE: A DSGE Model for the Korean Economy BOKDSGE: A DSGE Model for he Korean Economy June 4, 2008 Joong Shik Lee, Head Macroeconomeric Model Secion Research Deparmen The Bank of Korea Ouline 1. Background 2. Model srucure & parameer values 3.

More information

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates Biol. 356 Lab 8. Moraliy, Recruimen, and Migraion Raes (modified from Cox, 00, General Ecology Lab Manual, McGraw Hill) Las week we esimaed populaion size hrough several mehods. One assumpion of all hese

More information

Reliability of Technical Systems

Reliability of Technical Systems eliabiliy of Technical Sysems Main Topics Inroducion, Key erms, framing he problem eliabiliy parameers: Failure ae, Failure Probabiliy, Availabiliy, ec. Some imporan reliabiliy disribuions Componen reliabiliy

More information

STATE-SPACE MODELLING. A mass balance across the tank gives:

STATE-SPACE MODELLING. A mass balance across the tank gives: B. Lennox and N.F. Thornhill, 9, Sae Space Modelling, IChemE Process Managemen and Conrol Subjec Group Newsleer STE-SPACE MODELLING Inroducion: Over he pas decade or so here has been an ever increasing

More information

Problem Set 1 "Working with the Solow model"

Problem Set 1 Working with the Solow model Problem Se "Working wih he Solow model" Le's define he following exogenous variables: s δ n savings rae depreciaion rae of physical capial populaion growh rae L labor supply e n (Normalizing labor supply

More information

Solutions: Wednesday, November 14

Solutions: Wednesday, November 14 Amhers College Deparmen of Economics Economics 360 Fall 2012 Soluions: Wednesday, November 14 Judicial Daa: Cross secion daa of judicial and economic saisics for he fify saes in 2000. JudExp CrimesAll

More information

Presentation Overview

Presentation Overview Acion Refinemen in Reinforcemen Learning by Probabiliy Smoohing By Thomas G. Dieerich & Didac Busques Speaer: Kai Xu Presenaion Overview Bacground The Probabiliy Smoohing Mehod Experimenal Sudy of Acion

More information

MONOPOLISTIC COMPETITION IN A DSGE MODEL: PART II OCTOBER 4, 2011 BUILDING THE EQUILIBRIUM. p = 1. Dixit-Stiglitz Model

MONOPOLISTIC COMPETITION IN A DSGE MODEL: PART II OCTOBER 4, 2011 BUILDING THE EQUILIBRIUM. p = 1. Dixit-Stiglitz Model MONOPOLISTIC COMPETITION IN A DSGE MODEL: PART II OCTOBER 4, 211 Dixi-Sigliz Model BUILDING THE EQUILIBRIUM DS MODEL I or II Puing hings ogeher impose symmery across all i 1 pzf k( k, n) = r & 1 pzf n(

More information

( ) (, ) F K L = F, Y K N N N N. 8. Economic growth 8.1. Production function: Capital as production factor

( ) (, ) F K L = F, Y K N N N N. 8. Economic growth 8.1. Production function: Capital as production factor 8. Economic growh 8.. Producion funcion: Capial as producion facor Y = α N Y (, ) = F K N Diminishing marginal produciviy of capial and labor: (, ) F K L F K 2 ( K, L) K 2 (, ) F K L F L 2 ( K, L) L 2

More information

Age (x) nx lx. Age (x) nx lx dx qx

Age (x) nx lx. Age (x) nx lx dx qx Life Tables Dynamic (horizonal) cohor= cohor followed hrough ime unil all members have died Saic (verical or curren) = one census period (day, season, ec.); only equivalen o dynamic if populaion does no

More information

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1 Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies

More information

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature On Measuring Pro-Poor Growh 1. On Various Ways of Measuring Pro-Poor Growh: A Shor eview of he Lieraure During he pas en years or so here have been various suggesions concerning he way one should check

More information

The Simple Linear Regression Model: Reporting the Results and Choosing the Functional Form

The Simple Linear Regression Model: Reporting the Results and Choosing the Functional Form Chaper 6 The Simple Linear Regression Model: Reporing he Resuls and Choosing he Funcional Form To complee he analysis of he simple linear regression model, in his chaper we will consider how o measure

More information

Lecture 5. Time series: ECM. Bernardina Algieri Department Economics, Statistics and Finance

Lecture 5. Time series: ECM. Bernardina Algieri Department Economics, Statistics and Finance Lecure 5 Time series: ECM Bernardina Algieri Deparmen Economics, Saisics and Finance Conens Time Series Modelling Coinegraion Error Correcion Model Two Seps, Engle-Granger procedure Error Correcion Model

More information

( ) is the stretch factor, and x the

( ) is the stretch factor, and x the (Lecures 7-8) Liddle, Chaper 5 Simple cosmological models (i) Hubble s Law revisied Self-similar srech of he universe All universe models have his characerisic v r ; v = Hr since only his conserves homogeneiy

More information

CHAPTER 17: DYNAMIC ECONOMETRIC MODELS: AUTOREGRESSIVE AND DISTRIBUTED-LAG MODELS

CHAPTER 17: DYNAMIC ECONOMETRIC MODELS: AUTOREGRESSIVE AND DISTRIBUTED-LAG MODELS Basic Economerics, Gujarai and Porer CHAPTER 7: DYNAMIC ECONOMETRIC MODELS: AUTOREGRESSIVE AND DISTRIBUTED-LAG MODELS 7. (a) False. Economeric models are dynamic if hey porray he ime pah of he dependen

More information

Inflation-Targeting, Price-Path Targeting and Indeterminacy

Inflation-Targeting, Price-Path Targeting and Indeterminacy WORKING PAPER SERIES Inflaion-Targeing, Price-Pah Targeing and Indeerminacy Rober D. Dimar and William T. Gavin Working Paper 2004-007B hp://research.slouisfed.org/wp/2004/2004-007.pdf March 2004 Revised

More information

Time series Decomposition method

Time series Decomposition method Time series Decomposiion mehod A ime series is described using a mulifacor model such as = f (rend, cyclical, seasonal, error) = f (T, C, S, e) Long- Iner-mediaed Seasonal Irregular erm erm effec, effec,

More information

Lecture 15. Dummy variables, continued

Lecture 15. Dummy variables, continued Lecure 15. Dummy variables, coninued Seasonal effecs in ime series Consider relaion beween elecriciy consumpion Y and elecriciy price X. The daa are quarerly ime series. Firs model ln α 1 + α2 Y = ln X

More information

α = relative risk aversion

α = relative risk aversion BRAV CONSTANTNDES AND GECZY (BCG) Lucas model [ ] E R = Whils providin rea inuiion has enerally been rejeced by he empirical sudies. BCG do wo hins. ) Relax assumpion of complee consumpion insurance No

More information

(a) Set up the least squares estimation procedure for this problem, which will consist in minimizing the sum of squared residuals. 2 t.

(a) Set up the least squares estimation procedure for this problem, which will consist in minimizing the sum of squared residuals. 2 t. Insrucions: The goal of he problem se is o undersand wha you are doing raher han jus geing he correc resul. Please show your work clearly and nealy. No credi will be given o lae homework, regardless of

More information

Robert Kollmann. 6 September 2017

Robert Kollmann. 6 September 2017 Appendix: Supplemenary maerial for Tracable Likelihood-Based Esimaion of Non- Linear DSGE Models Economics Leers (available online 6 Sepember 207) hp://dx.doi.org/0.06/j.econle.207.08.027 Rober Kollmann

More information

Linear Gaussian State Space Models

Linear Gaussian State Space Models Linear Gaussian Sae Space Models Srucural Time Series Models Level and Trend Models Basic Srucural Model (BSM Dynamic Linear Models Sae Space Model Represenaion Level, Trend, and Seasonal Models Time Varying

More information

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin ACE 56 Fall 005 Lecure 4: Simple Linear Regression Model: Specificaion and Esimaion by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Simple Regression: Economic and Saisical Model

More information

= ( ) ) or a system of differential equations with continuous parametrization (T = R

= ( ) ) or a system of differential equations with continuous parametrization (T = R XIII. DIFFERENCE AND DIFFERENTIAL EQUATIONS Ofen funcions, or a sysem of funcion, are paramerized in erms of some variable, usually denoed as and inerpreed as ime. The variable is wrien as a funcion of

More information

Exponential Smoothing

Exponential Smoothing Exponenial moohing Inroducion A simple mehod for forecasing. Does no require long series. Enables o decompose he series ino a rend and seasonal effecs. Paricularly useful mehod when here is a need o forecas

More information

Explaining Total Factor Productivity. Ulrich Kohli University of Geneva December 2015

Explaining Total Factor Productivity. Ulrich Kohli University of Geneva December 2015 Explaining Toal Facor Produciviy Ulrich Kohli Universiy of Geneva December 2015 Needed: A Theory of Toal Facor Produciviy Edward C. Presco (1998) 2 1. Inroducion Toal Facor Produciviy (TFP) has become

More information

= W e e ( ) ( ) = = ( a h) W e [ ] d. y + + <

= W e e ( ) ( ) = = ( a h) W e [ ] d. y + + < W e e W e [ ] d ( ) + y + + < ( ) b a f f (a) ( a ) ( a ) p ( ) y ( ) ( a ) ( a ) ( + ) ( ( a ) ) ( ( a ) ) ( ) ( a ) ( a ) ( )+ by + Â p ( ) ( + a ) ( + ) + + ( ( a ) )+ ( ( a ) ) ( + ) + ( ) + ( a )

More information

Reaction Order Molecularity. Rate laws, Reaction Orders. Determining Reaction Order. Determining Reaction Order. Determining Reaction Order

Reaction Order Molecularity. Rate laws, Reaction Orders. Determining Reaction Order. Determining Reaction Order. Determining Reaction Order Rae laws, Reacion Orders The rae or velociy of a chemical reacion is loss of reacan or appearance of produc in concenraion unis, per uni ime d[p] d[s] The rae law for a reacion is of he form Rae d[p] k[a]

More information

Final Exam Advanced Macroeconomics I

Final Exam Advanced Macroeconomics I Advanced Macroeconomics I WS 00/ Final Exam Advanced Macroeconomics I February 8, 0 Quesion (5%) An economy produces oupu according o α α Y = K (AL) of which a fracion s is invesed. echnology A is exogenous

More information

ψ(t) = V x (0)V x (t)

ψ(t) = V x (0)V x (t) .93 Home Work Se No. (Professor Sow-Hsin Chen Spring Term 5. Due March 7, 5. This problem concerns calculaions of analyical expressions for he self-inermediae scaering funcion (ISF of he es paricle in

More information

Lecture 2D: Rank-Size Rule

Lecture 2D: Rank-Size Rule Econ 460 Urban Economics Lecure 2D: Rank-Size Rule Insrucor: Hiroki Waanabe Summer 2012 2012 Hiroki Waanabe 1 / 56 1 Rank-Size Rule 2 Eeckhou 3 Now We Know 2012 Hiroki Waanabe 2 / 56 1 Rank-Size Rule US

More information

1 Consumption and Risky Assets

1 Consumption and Risky Assets Soluions o Problem Se 8 Econ 0A - nd Half - Fall 011 Prof David Romer, GSI: Vicoria Vanasco 1 Consumpion and Risky Asses Consumer's lifeime uiliy: U = u(c 1 )+E[u(c )] Income: Y 1 = Ȳ cerain and Y F (

More information

STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN

STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.1-3(004) STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN 001-004 OBARA, Takashi * Absrac The

More information

The Real Exchange Rate, Real Interest Rates, and the Risk Premium. Charles Engel University of Wisconsin

The Real Exchange Rate, Real Interest Rates, and the Risk Premium. Charles Engel University of Wisconsin The Real Exchange Rae, Real Ineres Raes, and he Risk Premium Charles Engel Universiy of Wisconsin How does exchange rae respond o ineres rae changes? In sandard open economy New Keynesian model, increase

More information

Navneet Saini, Mayank Goyal, Vishal Bansal (2013); Term Project AML310; Indian Institute of Technology Delhi

Navneet Saini, Mayank Goyal, Vishal Bansal (2013); Term Project AML310; Indian Institute of Technology Delhi Creep in Viscoelasic Subsances Numerical mehods o calculae he coefficiens of he Prony equaion using creep es daa and Herediary Inegrals Mehod Navnee Saini, Mayank Goyal, Vishal Bansal (23); Term Projec

More information

Online Appendix. Fixed Asset Tables (FAT-BEA) 1. Table 5.3.4: Official Price Index for Investment in Equipment (OPIEQ t )

Online Appendix. Fixed Asset Tables (FAT-BEA) 1. Table 5.3.4: Official Price Index for Investment in Equipment (OPIEQ t ) A-1 Online Appendix Appendix A. Daa Consrucion Appendix A.1. Raw Daa Series All raw daa series rerieved from he Bureau of Economic Analysis (BEA; www.bea.gov) and he Bureau of Labor Saisics (BLS; www.bls.gov)

More information

Worker flows and matching efficiency

Worker flows and matching efficiency Worker flows and maching efficiency Marcelo Veraciero Inroducion and summary One of he bes known facs abou labor marke dynamics in he US economy is ha unemploymen and vacancies are srongly negaively correlaed

More information

Has the Business Cycle Changed? Evidence and Explanations. Appendix

Has the Business Cycle Changed? Evidence and Explanations. Appendix Has he Business Ccle Changed? Evidence and Explanaions Appendix Augus 2003 James H. Sock Deparmen of Economics, Harvard Universi and he Naional Bureau of Economic Research and Mark W. Wason* Woodrow Wilson

More information

OBJECTIVES OF TIME SERIES ANALYSIS

OBJECTIVES OF TIME SERIES ANALYSIS OBJECTIVES OF TIME SERIES ANALYSIS Undersanding he dynamic or imedependen srucure of he observaions of a single series (univariae analysis) Forecasing of fuure observaions Asceraining he leading, lagging

More information

Problem set 3: Endogenous Innovation - Solutions

Problem set 3: Endogenous Innovation - Solutions Problem se 3: Endogenous Innovaion - Soluions Loïc Baé Ocober 25, 22 Opimaliy in he R & D based endogenous growh model Imporan feaure of his model: he monopoly markup is exogenous, so ha here is no need

More information

The Brock-Mirman Stochastic Growth Model

The Brock-Mirman Stochastic Growth Model c November 20, 207, Chrisopher D. Carroll BrockMirman The Brock-Mirman Sochasic Growh Model Brock and Mirman (972) provided he firs opimizing growh model wih unpredicable (sochasic) shocks. The social

More information

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n Module Fick s laws of diffusion Fick s laws of diffusion and hin film soluion Adolf Fick (1855) proposed: d J α d d d J (mole/m s) flu (m /s) diffusion coefficien and (mole/m 3 ) concenraion of ions, aoms

More information

ACE 562 Fall Lecture 8: The Simple Linear Regression Model: R 2, Reporting the Results and Prediction. by Professor Scott H.

ACE 562 Fall Lecture 8: The Simple Linear Regression Model: R 2, Reporting the Results and Prediction. by Professor Scott H. ACE 56 Fall 5 Lecure 8: The Simple Linear Regression Model: R, Reporing he Resuls and Predicion by Professor Sco H. Irwin Required Readings: Griffihs, Hill and Judge. "Explaining Variaion in he Dependen

More information

What Ties Return Volatilities to Price Valuations and Fundamentals? On-Line Appendix

What Ties Return Volatilities to Price Valuations and Fundamentals? On-Line Appendix Wha Ties Reurn Volailiies o Price Valuaions and Fundamenals? On-Line Appendix Alexander David Haskayne School of Business, Universiy of Calgary Piero Veronesi Universiy of Chicago Booh School of Business,

More information

Errata (1 st Edition)

Errata (1 st Edition) P Sandborn, os Analysis of Elecronic Sysems, s Ediion, orld Scienific, Singapore, 03 Erraa ( s Ediion) S K 05D Page 8 Equaion (7) should be, E 05D E Nu e S K he L appearing in he equaion in he book does

More information

A User s Guide to Solving Real Business Cycle Models. by a single representative agent. It is assumed that both output and factor markets are

A User s Guide to Solving Real Business Cycle Models. by a single representative agent. It is assumed that both output and factor markets are page, Harley, Hoover, Salyer, RBC Models: A User s Guide A User s Guide o Solving Real Business Cycle Models The ypical real business cycle model is based upon an economy populaed by idenical infiniely-lived

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time. Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike

More information

10. State Space Methods

10. State Space Methods . Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he

More information

BEGIN BLUEBOOK A BEGIN BLUEBOOK A BEGIN BLUEBOOK A

BEGIN BLUEBOOK A BEGIN BLUEBOOK A BEGIN BLUEBOOK A Economics 32, Sec. 1 Menzie D. Cinn Fall 21 Social Sciences 7418 Universiy of Wisconsin-Madison Final Examinaion Ansers Tis exam is 8 minues long, and is or 8 poins. ou are given 88 minues o complee i.

More information

Solutions to Exercises in Chapter 12

Solutions to Exercises in Chapter 12 Chaper in Chaper. (a) The leas-squares esimaed equaion is given by (b)!i = 6. + 0.770 Y 0.8 R R = 0.86 (.5) (0.07) (0.6) Boh b and b 3 have he expeced signs; income is expeced o have a posiive effec on

More information

Exponential and Logarithmic Functions -- ANSWERS -- Logarithms Practice Diploma ANSWERS 1

Exponential and Logarithmic Functions -- ANSWERS -- Logarithms Practice Diploma ANSWERS 1 Eponenial and Logarihmic Funcions -- ANSWERS -- Logarihms racice Diploma ANSWERS www.puremah.com Logarihms Diploma Syle racice Eam Answers. C. D 9. A 7. C. A. C. B 8. D. D. C NR. 8 9. C 4. C NR. NR 6.

More information

KEY. Math 334 Midterm I Fall 2008 sections 001 and 003 Instructor: Scott Glasgow

KEY. Math 334 Midterm I Fall 2008 sections 001 and 003 Instructor: Scott Glasgow 1 KEY Mah 4 Miderm I Fall 8 secions 1 and Insrucor: Sco Glasgow Please do NOT wrie on his eam. No credi will be given for such work. Raher wrie in a blue book, or on our own paper, preferabl engineering

More information

4.1 Other Interpretations of Ridge Regression

4.1 Other Interpretations of Ridge Regression CHAPTER 4 FURTHER RIDGE THEORY 4. Oher Inerpreaions of Ridge Regression In his secion we will presen hree inerpreaions for he use of ridge regression. The firs one is analogous o Hoerl and Kennard reasoning

More information

Generalized Least Squares

Generalized Least Squares Generalized Leas Squares Augus 006 1 Modified Model Original assumpions: 1 Specificaion: y = Xβ + ε (1) Eε =0 3 EX 0 ε =0 4 Eεε 0 = σ I In his secion, we consider relaxing assumpion (4) Insead, assume

More information

Why is Chinese Provincial Output Diverging? Joakim Westerlund, University of Gothenburg David Edgerton, Lund University Sonja Opper, Lund University

Why is Chinese Provincial Output Diverging? Joakim Westerlund, University of Gothenburg David Edgerton, Lund University Sonja Opper, Lund University Why is Chinese Provincial Oupu Diverging? Joakim Weserlund, Universiy of Gohenburg David Edgeron, Lund Universiy Sonja Opper, Lund Universiy Purpose of his paper. We re-examine he resul of Pedroni and

More information

Wednesday, November 7 Handout: Heteroskedasticity

Wednesday, November 7 Handout: Heteroskedasticity Amhers College Deparmen of Economics Economics 360 Fall 202 Wednesday, November 7 Handou: Heeroskedasiciy Preview Review o Regression Model o Sandard Ordinary Leas Squares (OLS) Premises o Esimaion Procedures

More information

Curling Stress Equation for Transverse Joint Edge of a Concrete Pavement Slab Based on Finite-Element Method Analysis

Curling Stress Equation for Transverse Joint Edge of a Concrete Pavement Slab Based on Finite-Element Method Analysis TRANSPORTATION RESEARCH RECORD 155 35 Curling Sress Equaion for Transverse Join Edge of a Concree Pavemen Slab Based on Finie-Elemen Mehod Analysis TATSUO NISHIZAWA, TADASHI FUKUDA, SABURO MATSUNO, AND

More information

(MS, ) Problem 1

(MS, ) Problem 1 MS, 7.6.4) AKTUAREKSAMEN KONTROL I FINANSIERING OG LIVSFORSIKRING ved Københavns Universie Sommer 24 Skriflig prøve den 4. juni 24 kl..-4.. All wrien aids are allowed. The wo problems of oally 3 quesions

More information

Depreciation, Deterioration and Obsolescence when there is Embodied or Disembodied Technical Change Revised July 12, 2007

Depreciation, Deterioration and Obsolescence when there is Embodied or Disembodied Technical Change Revised July 12, 2007 1 Depreciaion, Deerioraion and Obsolescence when here is Embodied or Disembodied Technical Change Revised July 12, 2007 Erwin Diewer, 1 Frank C. Wykoff, Discussion Paper 06-02, Deparmen of Economics, Deparmen

More information

22. Inbreeding. related measures: = coefficient of kinship, a measure of relatedness of individuals of a population; panmictic index, P = 1 F;

22. Inbreeding. related measures: = coefficient of kinship, a measure of relatedness of individuals of a population; panmictic index, P = 1 F; . Inbreeding Inbreeding: maing beween relaives. has predicable consequences for gene and genoype frequencies; increases he frequency of homozygous genoypes a he expense of heerozygous genoypes; hus decreases

More information

Cooperative Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS. August 8, :45 a.m. to 1:00 p.m.

Cooperative Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS. August 8, :45 a.m. to 1:00 p.m. Cooperaive Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS Augus 8, 213 8:45 a.m. o 1: p.m. THERE ARE FIVE QUESTIONS ANSWER ANY FOUR OUT OF FIVE PROBLEMS.

More information

Anti-Disturbance Control for Multiple Disturbances

Anti-Disturbance Control for Multiple Disturbances Workshop a 3 ACC Ani-Disurbance Conrol for Muliple Disurbances Lei Guo (lguo@buaa.edu.cn) Naional Key Laboraory on Science and Technology on Aircraf Conrol, Beihang Universiy, Beijing, 9, P.R. China. Presened

More information

2.9 Modeling: Electric Circuits

2.9 Modeling: Electric Circuits SE. 2.9 Modeling: Elecric ircuis 93 2.9 Modeling: Elecric ircuis Designing good models is a ask he compuer canno do. Hence seing up models has become an imporan ask in modern applied mahemaics. The bes

More information

1. Consider a pure-exchange economy with stochastic endowments. The state of the economy

1. Consider a pure-exchange economy with stochastic endowments. The state of the economy Answer 4 of he following 5 quesions. 1. Consider a pure-exchange economy wih sochasic endowmens. The sae of he economy in period, 0,1,..., is he hisory of evens s ( s0, s1,..., s ). The iniial sae is given.

More information

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H. ACE 564 Spring 2006 Lecure 7 Exensions of The Muliple Regression Model: Dumm Independen Variables b Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Dumm Variables and Varing Coefficien Models

More information

THE GLOBAL DECLINE OF THE LABOR SHARE

THE GLOBAL DECLINE OF THE LABOR SHARE THE GLOBAL DECLINE OF THE LABOR SHARE Shi Zhengyang, Huang Yiguo, Ma Chengchao, Xie Yuchen June 5, 208 Auhor-Loukas Karabarbounis Academic Posiion Associae professor, Deparmen of Economics, Universiy of

More information

( ) a system of differential equations with continuous parametrization ( T = R + These look like, respectively:

( ) a system of differential equations with continuous parametrization ( T = R + These look like, respectively: XIII. DIFFERENCE AND DIFFERENTIAL EQUATIONS Ofen funcions, or a sysem of funcion, are paramerized in erms of some variable, usually denoed as and inerpreed as ime. The variable is wrien as a funcion of

More information

Das House-Kapital: A Long Term Housing & Macro Model

Das House-Kapital: A Long Term Housing & Macro Model Das House-Kapial: A Long Term Housing & Macro Model Volker Grossmann (Universiy of Fribourg, CESifo, IZA, CReAM) Thomas Seger (Leipzig Universiy, IWH, CESifo) European Summer Symposium in Inernaional Macroeconomics

More information