Essays in applied macroeconomics Author: Nicola Francesco Lostumbo Persistent link: http://hdl.handle.net/2345/1747 This work is posted on eScholarship@BC, Boston College University Libraries. Boston College Electronic Thesis or Dissertation, 2008 Copyright is held by the author, with all rights reserved, unless otherwise noted. Boston College The Graduate School of Arts and Sciences Department of Economics ESSAYS IN APPLIED MACROECONOMICS a dissertation by NICOLA FRANCESCO LOSTUMBO submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy December 2008 c copyright by NICOLA FRANCESCO LOSTUMBO 2008 ESSAYS IN APPLIED MACROECONOMICS by NICOLA FRANCESCO LOSTUMBO Dissertation Advisors: MATTEO IACOVIELLO, PETER IRELAND, and SCOTT SCHUH Abstract These three essays are concerned with macroeconomic and monetary policy issues relating to the housing market and inflation-targeting. The essays can be character- ized as applied macroeconomics in nature as they use insights from theory to construct macroeconomic models, which are then taken to the data. The first chapter in this study utilizes microeconomic evidence that nominal loss aversion plays a role in the pricing of housing services and explores the extent to which this phenomenon in the housing sector affects the macroeconomy as a whole. A two- sector Dynamic Stochastic General Equilibrium model of housing and consumption goods with downward nominal price rigidity in the housing sector is constructed to examine how asymmetries in the nominal pricing of housing services affects monetary policy in stabilizing the economy in response to shocks. A calibration exercise is also performed to gain insight to what degree pricing dynamics in the housing sector are driven by the tendency of sellers to be nominally loss averse. The second chapter explores the disparities in the success rate in hitting an explicit inflation target among OECD and Emerging Market inflation targeters. The study proposes a framework to try to circumvent the \good luck"/\good policy" forces as drivers of better inflation-targeting outcomes by estimating a measure of central bank credibility in targeting regimes. Two main findings are that Emerging Market targeting banks are less successful than their OECD counterparts in establishing credibility in targeting inflation and that credible regimes last on the order of five to ten times as long as the relatively short-lived incredible regimes for the two groups of targeting countries. The third chapter, co-authored with Scott Schuh of the Federal Reserve Bank of Boston, takes a preliminary empirical step to model inflation outcomes for infla- tion band-targeting countries which allows us to isolate the empirical determinants of inflation escaping from the targeted band. We also use our framework to deter- mine whether US inflation is consistent with inflation under an explicit targeting regime. Our model generates the result that US inflation during the last decade is well predicted by a model of inflation-targeting countries. Acknowledgments I would like to thank my advisors, Matteo Iacoviello, Peter Ireland, and Scott Schuh, for helpful advice throughout my time at Boston College, in addition to providing invaluable comments on these essays. Part of this dissertation was completed during my stays at the Federal Reserve Banks of Boston and Kansas City. I am grateful to the Banks' staff for their support and hospitality. A special thanks is extended to my parents, Rosario and Angelina, who deserve much of the credit for this and other accomplishments of mine. Finally, I would like to thank my fianc´ee,Andrea Rauch, for her patience and good nature which helped to keep me very happy and productive in the past four years. Contents I My House is Worth What! :The Asymmetric Effects of Nominal Loss Aversion in the Housing Market 1 Abstract & Acknowledgments 2 1.1 Introduction . 3 1.1.1 Asymmetric Nominal Rigidities . 6 1.2 The Model . 9 1.2.1 Patient Household . 10 1.2.2 Impatient Household . 14 1.2.3 Firms . 16 Housing Good Sector . 17 Consumption Good Sector . 21 1.2.4 Central Bank Policy . 24 1.2.5 Equilibrium . 25 1.3 Calibration Exercise . 26 1.3.1 Data and Moment Selection . 27 1.3.2 Grid Search Method . 29 1.3.3 Model Calibration . 30 1.3.4 Results . 31 1.3.5 Model Dynamics . 32 1.4 Concluding Remarks . 37 1.5 Grid Search Appendix . 39 II Success in Hitting the Inflation Target: Good Luck or Good Policy? 55 Abstract & Acknowledgments 56 2.1 Introduction . 57 2.2 Motivating Literature . 58 2.3 The Model . 60 2.3.1 Motivation . 60 2.3.2 Markov-Switching . 62 2.4 Model Estimation . 66 2.4.1 Maximum Likelihood Estimation . 67 2.4.2 Bayesian Estimation . 68 Priors . 69 2.4.3 Data . 70 2.5 Results . 71 2.5.1 Estimation . 71 Transition Probabilities . 72 The Phillips Curve . 73 2.5.2 Convergence of MCMC . 75 2.5.3 Inferences Over Regimes . 76 Industrial Targeters . 77 Emerging Market Targeters . 78 Non-Targeters . 80 2.6 Concluding Remarks . 81 III Is the US an Implicit Inflation-Targeter? (co-authored with Scott Schuh) 105 Abstract & Acknowledgments 106 3.1 Introduction . 107 3.2 Theoretical Foundations . 110 3.2.1 Inflation Point Targeting . 110 3.2.2 Inflation Band Targeting . 111 3.2.3 A Theoretical IBT Model . 113 3.3 Empirical Exercise . 116 3.3.1 Applied Work on Inflation-Targeting . 117 3.3.2 Econometric Model . 119 3.3.3 Data . 122 3.4 Results . 124 3.4.1 Ordered Logit Regressions . 124 Marginal Effects . 125 3.4.2 Potential Endogeneity of Regressors . 126 3.4.3 Heteroskedastic Errors and Simultaneity Bias . 129 3.4.4 Exchange Rates . 129 3.5 Prediction Exercise . 130 3.6 Concluding Remarks . 132 3.7 Orphanides & Wieland Appendix . 133 3.7.1 Quadratic Preferences with Linear Constraints . 133 3.7.2 Zone-Quadratic Preferences . 134 3.7.3 Quadratic Preferences & Nonlinear Phillips Curve . 135 3.8 Data Appendix . 137 3.8.1 Seasonal Adjustment . 144 Bibliography ................................. 162 List of Tables 1.1 Calibrated (Quarterly) Parameters . 44 1.2 Data . 45 1.3 Grid Search Results . 45 1.4 Cost of Adjusting Prices in % of Good Output . 46 1.5 Data and Model Moments . 46 2.1 Volatilities for OECD and Emerging Market Targeters . 82 2.2 Countries Included in the Sample . 82 2.3 Priors for Parameters . 83 2.4 Maximum Likelihood & Bayesian Estimates: Industrial Targeters . 84 2.5 Maximum Likelihood and Bayesian Estimates: Industrial Targeters . 85 2.6 Maximum Likelihood & Bayesian Estimates: Industrial Targeters . 86 2.7 Maximum Likelihood & Bayesian Estimates: Industrial Targeters . 87 2.8 Maximum Likelihood & Bayesian Estimates: Industrial Targeters . 88 2.9 Maximum Likelihood & Bayesian Estimates: Emerging Market Targeters 89 2.10 Maximum Likelihood & Bayesian Estimates: Emerging Market Targeters 90 2.11 Maximum Likelihood & Bayesian Estimates: Emerging Market Targeters 91 2.12 Maximum Likelihood & Bayesian Estimates: Emerging Market Targeters 92 2.13 Maximum Likelihood & Bayesian Estimates: Non-Targeters . 93 2.14 Estimated Duration of Regimes: Maximum Likelihood Estimates . 94 2.15 Estimated Duration of Regimes: Bayesian Estimates . 95 2.16 Parameter Means: ML & Bayesian Estimates . 96 3.1 Inflation-Targeting Countries . 145 3.2 Location of Actual Inflation in Inflation Band-Targeting Countries (Frequency in % of sample period) . 146 3.3 Ordered Logit Regressions: Odds-Ratios . 147 3.4 Ordered Logit Regressions: Marginal Effects (All Countries) . 148 3.5 Ordered Logit Regressions: Marginal Effects (Industrial Countries) . 149 3.6 Ordered Logit Regressions: Marginal Effects (Emerging Market Coun- tries) . 150 3.7 2SCML Results: Odds-Ratios . 151 3.8 2SCML Results: Marginal Effects (All Countries) . 152 3.9 2SCML Results: Marginal Effects (Industrial Countries) . 153 3.10 2SCML Results: Marginal Effects (Emerging Market Countries) . 154 3.11 Linear & Rank Correlations of Predicted and Actual Band Positions . 155 List of Figures 1.1 Housing Price Growth and the Federal Funds Rate . 47 1.2 Pricing Cost of Adjustment Functions . 48 1.3 Simulated Housing Good Inflation Histograms . 49 1.4 Data Series Used (1984:Q1 - 2007:Q4) . 50 1.5 Housing and Consumption Pricing Cost of Adjustment Functions: 1984:Q1- 2007:Q4 . 51 1.6 IRFs: Monetary Policy Shock . 52 1.7 IRFs: Consumption Technology Shock . 53 1.8 IRFs: Housing Technology Shock . 54 2.1 Posterior Distribution of Parameters: United States . 97 2.2 Recursive Means of Parameters: United States . 98 2.3 Filtered and Smoothed Probabilities of Regimes: Industrial Targeters 99 2.3 Filtered and Smoothed Probabilities of Regimes: Industrial Targeters 100 2.3 Filtered and Smoothed Probabilities of Regimes: Industrial Targeters 101 2.4 Filtered and Smoothed Probabilities of Regimes: Emerging Market Targeters . 102 2.4 Filtered and Smoothed Probabilities of Regimes: Emerging Market Targeters . 103 2.5 Filtered and Smoothed Probabilities of Regimes: Non-Targeters . 104 3.1 US Inflation (CPI and Core CPI) . 156 3.2 (a.) OECD Countries' Inflation with Actual Bands . 157 3.2 (b.) Emerging Market Countries' Inflation with Actual Bands . 158 3.3 Quadratic and Zone-Quadratic Loss Functions . 159 3.4 Optimal Policy in the Linear Quadratic and Linear Zone-Quadratic Models with and without Uncertainty . 159 3.5 US Inflation (Core CPI and Total CPI) with Constructed Bands . 160 3.6 Predicted US Inflation (CPI and Core CPI) . 161 Part I My House is Worth What! :The Asymmetric Effects of Nominal Loss Aversion in the Housing Market 1 Abstract Utilizing microeconomic evidence that nominal loss aversion plays a role in the pricing of housing services, this study explores the extent to which this phenomenon in the housing sector affects the macroeconomy as a whole. A.