doi /j. issn % % JOURNAL OF CHONGQING UNIVERSITY Social Science Edition Vol. 22 No.

Similar documents
Working Paper Series

Lecture 4: Bayesian VAR

BEAR 4.2. Introducing Stochastic Volatility, Time Varying Parameters and Time Varying Trends. A. Dieppe R. Legrand B. van Roye ECB.

VAR-based Granger-causality Test in the Presence of Instabilities

Large Vector Autoregressions with asymmetric priors and time varying volatilities

1. Shocks. This version: February 15, Nr. 1

Evaluating FAVAR with Time-Varying Parameters and Stochastic Volatility

Great Recession and Monetary Policy Transmission

Dynamic Factor Models and Factor Augmented Vector Autoregressions. Lawrence J. Christiano

Optimal Monetary Policy in a Data-Rich Environment

A Markov switching factor-augmented VAR model for analyzing US business cycles and monetary policy

Estimation of a forward-looking monetary policy rule: A time-varying parameter model using ex post data $

A Comparison of Business Cycle Regime Nowcasting Performance between Real-time and Revised Data. By Arabinda Basistha (West Virginia University)

Estimating and Accounting for the Output Gap with Large Bayesian Vector Autoregressions

LARGE TIME-VARYING PARAMETER VARS

Are recoveries all the same?

Macroeconomic Forecasting and Structural Change

The Dynamic Relationship between Permanent and Transitory Components of U.S. Business Cycles

Assessing Structural Convergence between Romanian Economy and Euro Area: A Bayesian Approach

Agosto de 2009 TAYLOR PRINCIPLE AND INFLATION STABILITY IN EMERGING MARKET COUNTRIES VLADIMIR KÜHL TELES MARTA ZAIDAN

What is different this time? Investigating the declines of the U.S. real GDP during the Great Recession

Methods for Computing Marginal Data Densities from the Gibbs Output

Oil price and macroeconomy in Russia. Abstract

Factor models. March 13, 2017

A quasi-bayesian nonparametric approach to time varying parameter VAR models Job Market Paper

Oil Price Forecastability and Economic Uncertainty

The Kalman filter, Nonlinear filtering, and Markov Chain Monte Carlo

Markov-Switching Models with Endogenous Explanatory Variables. Chang-Jin Kim 1

Federal Reserve Bank of New York Staff Reports

New Information Response Functions

Common Time Variation of Parameters in Reduced-Form Macroeconomic Models

Measuring interconnectedness between financial institutions with Bayesian time-varying VARs

VAR models with non-gaussian shocks

Research Division Federal Reserve Bank of St. Louis Working Paper Series

Large time varying parameter VARs for macroeconomic forecasting

Alina Barnett (1) Haroon Mumtaz (2) Konstantinos Theodoridis (3) Abstract

Are Policy Counterfactuals Based on Structural VARs Reliable?

Analyzing and Forecasting Output Gap and Inflation Using Bayesian Vector Auto Regression (BVAR) Method: A Case of Pakistan

On the Time Variations of US Monetary Policy: Who is right?

Gibbs Samplers for VARMA and Its Extensions

Staff Working Paper No. 622 Interpreting the latent dynamic factors by threshold FAVAR model Sinem Hacioglu Hoke and Kerem Tuzcuoglu

CENTRE FOR APPLIED MACROECONOMIC ANALYSIS

University of Kent Department of Economics Discussion Papers

Warwick Business School Forecasting System. Summary. Ana Galvao, Anthony Garratt and James Mitchell November, 2014

Lecture on State Dependent Government Spending Multipliers

Quarterly Journal of Economics and Modelling Shahid Beheshti University SVAR * SVAR * **

Computational Macroeconomics. Prof. Dr. Maik Wolters Friedrich Schiller University Jena

Factor models. May 11, 2012

Dynamic probabilities of restrictions in state space models: An application to the New Keynesian Phillips Curve

Regime-Switching Cointegration

Bayesian Compressed Vector Autoregressions

in Trend University Gary Koop # 590

Reducing Dimensions in a Large TVP-VAR

How reliable are Taylor rules? A view from asymmetry in the U.S. Fed funds rate. Abstract

The Natural Rate of Interest and its Usefulness for Monetary Policy

What You Match Does Matter: The Effects of Data on DSGE Estimation

Stochastic Trends & Economic Fluctuations

FEDERAL RESERVE BANK of ATLANTA

Structural changes in the US economy: is there a role for monetary policy?

Historical Decompositions for Nonlinear Vector Autoregression Models

Assessing Monetary Policy Models: Bayesian Inference for Heteroskedastic Structural VARs

Identifying Aggregate Liquidity Shocks with Monetary Policy Shocks: An Application using UK Data

Efficient Estimation of Bayesian VARMAs With Time-Varying Coefficients

Citation Working Paper Series, F-39:

General comments Linear vs Non-Linear Univariate vs Multivariate

Bayesian Compressed Vector Autoregressions

Nonlinear Relation of Inflation and Nominal Interest Rates A Local Nonparametric Investigation

Lecture 1: Information and Structural VARs

Business Cycle Comovements in Industrial Subsectors

Do BVAR Models Forecast Turkish GDP Better Than UVAR Models?

Using VARs and TVP-VARs with Many Macroeconomic Variables

Regime-Switching Cointegration

Nowcasting Norwegian GDP

Federal Reserve Bank of New York Staff Reports

growth in a time of debt evidence from the uk

The Price Puzzle: Mixing the Temporary and Permanent Monetary Policy Shocks.

Structural Evolution of Postwar U.S. Economy

Confidence and the Transmission of Macroeconomic Uncertainty in U.S. Recessions

Discussion of Bootstrap prediction intervals for linear, nonlinear, and nonparametric autoregressions, by Li Pan and Dimitris Politis

31 (3) ISSN

The Effects of Monetary Policy on Stock Market Bubbles: Some Evidence

TESTING FOR MULTIPLE STRUCTURAL BREAKS: AN APPLICATION OF BAI-PERRON TEST TO THE NOMINAL INTEREST RATES AND INFLATION IN TURKEY

Financial Stress Regimes and the Macroeconomy

Identifying the Monetary Policy Shock Christiano et al. (1999)

Identifying SVARs with Sign Restrictions and Heteroskedasticity

Estimating Macroeconomic Models: A Likelihood Approach

Materiali di discussione

NBER WORKING PAPER SERIES HAS THE BUSINESS CYCLE CHANGED AND WHY? James H. Stock Mark W. Watson. Working Paper

Dimitrios P. Louzis* Steady-state priors and Bayesian variable selection in VAR forecasting

Deviance Information Criterion for Comparing VAR Models

The Asymmetric Business Cycle

FEDERAL RESERVE BANK of ATLANTA

Bayesian model comparison for time-varying parameter VARs with stochastic volatility

Multivariate Markov Switching With Weighted Regime Determination: Giving France More Weight than Finland

Estimating Unobservable Inflation Expectations in the New Keynesian Phillips Curve

PRIORS FOR THE LONG RUN: ONLINE APPENDIX

Measuring Macro-Financial Conditions Using a Factor-Augmented Smooth-Transition Vector Autoregression

SHORT TERM FORECASTING SYSTEM OF PRIVATE DEMAND COMPONENTS IN ARMENIA

WERE THERE REGIME SWITCHES IN US MONETARY POLICY? I. INTRODUCTION

Relationships Among Prices of Rubber in ASEAN: Bayesian Structural VAR Model

Transcription:

doi1. 11835 /j. issn. 18-5831. 216. 1. 6 216 22 1 JOURNAL OF CHONGQING UNIVERSITYSocial Science EditionVol. 22 No. 1 216. J. 2161 5-57. Citation FormatJIN Chengxiao LU Yingchao. The decomposition of Chinese economic fluctuation and the dynamic response of monetary policy shocks J. Journal of Chongqing UniversitySocial Science Edition2161 5-57. 1 2 1. 13122. 15 SV - TVP - FAVAR 34. 12% 65. 88% 3 1961 1978 1996 SV - TVP - FAVAR F224. F82. 1 A 18-58312161-5-8 212 215-9 - 25 12JJD7915 1966-1987 -

51 SV - TVP - FAVAR 1 2 Chen 3 VAR Fleischman Roberts 4 Morley Nelson Zivot 5 GDP GDP Bernanke 6 Sims 7 FAVAR 8 FAVAR CPI CPI FAVAR FAVAR Boivin Giannoni 9 Cogley Sargent 1-11 Clarida 12 2 8 13 Primiceri 14 Sims Zha 15 Koop Korobilis 16 VAR VAR 17 Boivin Ng 18 Giannone Reichlin Small 19 Boivin Giannoni 9 Bernanke 6 Korobilis 2 FAVAR SV - TVP - FAVAR Korobilis 2 SV - TVP - FAVAR 1 2 3 4 SV - TVP - FAVAR Sim 7 VAR Korobilis 2 SV - TVP - FAVAR Y t = α t + Σ P p = 1 β t p Y t-p + υ t 1 Y t = F t R t ' ν 1 R t K + ν 1 υ t Ω t K + ν K + ν 1

52 216 22 1 X t N FAVAR K + v N X t F t R t X t = Λ f F t + Λ r R t + e t 2 Λ f N K Λ r N ν e t N 1 Ψ = diagexpψ 1 t expψ n t 2 F t R t X t 1 2 SV - TVP - FAVAR Ω t = A -1 t H t A -1 t ' A t H t H t h 1 t h 2 t h K+ν t A t 1 a 21 t 1 a K+ν1 t 1 a K+νk t Primiceri 14 Koop Korobilis 16 Giordini Kohn 21 3 Λ t = Λ t-1 + J λ i tη λ t ψ i t = ψ i t-1 + J ψ i tη ψ t t = t-1 + J i tη t a t = a t-1 + J a i tη a t lnh i t = lnh i t-1 + J h i tη h t = α t β t p 5 η λ t η ψ t η t η a t η h t ~ N Q Q = Q η λ t Q η ψ t Q η t Q η α t Q η h t 4 5 5 2 X t Λ f Λ r R t M2 GDP 22-23 Bai Ng 24 Bernanke Boivin Eliase 6 1 2 F t 2 F t K F t F t Korobilis 2 Gibbs MCMC 1 Gibbs 2 Gibbs

53 1953-211 59 31 X t 1 2 3 4 5 5 3 CPI = CPI - 1 = - / M2 M2 M2 1953 21 25 M2 = M1+ = M+ + 21 X t 6 59 31 1 31 59 2 3 2 3 59 SV - TVP - FAVAR X t 1 X t Eviews matlab Bernanke Boivin Eliase 6 2 1 1 1 1

54 216 22 1 1 2 x it = λ f if t + λ r ir t + e it 6 8 R 2 R 2 i = 2 varλ 'C i t Σ varλ f if t + λ r ir t = t λ 'C i t 7 Σ t λ f if t + λ r ir t 2 var λ 'C i t GDP 1 M2 1 3 13. 323 34. 12% 1 25. 72 65. 88% 34. 12% 65. 88% 1 Chow Jushan Bap Pierre Perron 26 SUPF T k UDmax WDmax 2 M2 Sequential BIC 3 LWZ 1 3 WDmax M2 1961 1978 1996 1978

55 25 1978 1996 1996 1996 2 2 Sup F T 1 Sup F T 2 Sup F T 3 Sup F T 4 Sup F T 5 7. 93 2 13. 39 5 * 17. 467 8 * 14. 564 6 * 8. 48 6 * UDmax WDmax SupF2 1 SupF3 2 SupF4 3 17. 467 8 * 17. 467 8 * 11. 98 7 * 11. 98 7 *. 578 3 Sequential 3 BIC 3 LWZ 1 T^ 1961 1 T^ 2 * 5% 1978 T^ 3 1996 2 3 1961 1978 1996 1 1 2 3 3 2 4 4

56 216 22 1 3 SV - TVP - FAVAR 34. 12% 65. 88% SUPF T k UDmax WDmax 1961 1978 1996 3 1. J. 25 241 52-57. 2. J. 261 49-52. 3CHEN J Z KANNAN P LOUNGANI P et al. New evidence on cyclical and structural sources of unemploymentr. IMF working paper 211. 4FLEISCHMAN CA ROBERTS J M. From many seriesone cycleimproved estimates of the business cycle from a multivariate unobserved components modelr. Federal Reserve Board Finance and Economics Discussion Series Working paper 211. 5MORLEY J C NELSON C R ZIVOT E. Why are Beveridge - Nelson and unobserved-component decompositions of GDP so different J. The Review of Economics and Statistics 23 852 235-243. 6BERNANKE B S BOIVIN J ELIASZ P. Measuring the effects of monetary policya factor-augmented vector autoregressive FAVAR approach J. Quarterly journal of economics 25 121 387-422.

57 7 SIMS C A. Macroeconomics and reality J. Econometrica Society 198 481 1-48. 8. CPI J. 21212 29-42. 9BOIVIN J GIANNONI M. Has monetary policy become more effective J. Review of Economics and Statistics 26 88445-462. 1COGLEY T SARGENT T. Evolving post-world War II inflation dynamicsr. NBER Macroeconomic Annual 21 16331-373. 11COGLEY T SARGENT T. Drifts and volatilitiesmonetary policies and outcomes in the post WWII U. S J. Review of Economic Dynamics 25 8262-32. 12CLARIDA R GALI J GERTLER M. Monetary policy rule and macroeconomic stabilityevidence and some theory J. Quarterly Journal of Economics 2 115147-18. 13. J. 291 61-73. 14PRIMICERI G. Time varying structural vector autoregressions and monetary policy J. Review of Economic Studies 25 72821-852. 15 SIMS C ZHA T. Were there regime switches in macroeconomic policy J. American Economic Review 26 9654-81. 16KOOP G KOROBILIS D. Bayesian multivariate time series methods for empirical macroeconomics J. Foundations and Trends in Econometrics 213 267-358. 17BERNANKE B S BOIVIN J. Monetary policy in a data-rich environment J. Journal of Monetary Economics 23 5525-546. 18BOIVIN J NG S. Understanding and comparing factor-based forecasts J. International Journal of Central Banking 253 111-151. 19GIANNONE D REICHLIN L SMALL D. NowcastingThe real-time informational content of macroeconomic data J. Journal of Monetary Economics 28 55665-676. 2KOROBILIS D. Assessing the transmission of monetary policy shocks using dynamic factor modelsr. mimeo29. 21GIORDANI P KOHN R. Efficient Bayesian inference for multiple change point and mixture innovation modelsj. Journal of Business and Economic Statistics 28 2666-77. 22. J. 261 51-63. 23. J. 285 25-34. 24BAI J S NG S. Determining the number of factors in approximate factor models J. Econometrica Econometric Society 227 1 191-221. 25. D. 22. 26BAI J S PERRON P. Computation and analysis of multiple structural change models J. Journal of Applied Econometrics 23 181 1-22. The decomposition of Chinese economic fluctuation and the dynamic response of monetary policy shocks JIN Chengxiao 1 LU Yingchao 2 1. Center for Quantitative EconomicsJilin UniversityChangchun 1312P. R. China 2. Post-doctorate Research StationHuaxia BankBeijing 15P. R. China Abstract: In this paper, we construct a factor-augmented vector auto-regressive model with time-varying coefficients and stochastic volatility, and use it to decompose the economic fluctuation. Based on this, we give the dynamic response of the mainly economic variables to the monetary policy shock. The results show that: 1 ) Structural factors explained 34. 12% of economic fluctuation, cyclical factors explained 65. 88%, the two factors have significant influence on the economy; 2) During the sample period, there are three break points. They are 1961, 1987 and 1996; 3) Different variables have different response to the shock of monetary policy; 4) In different period, the response of the economic variables to the monetary policy shock has significant difference. Key words: economic fluctuations decomposition; monetary policy shocks; SV - TVP - FAVAR