Structural FECM: Cointegration in Large-Scale Structural FAVAR Models

Starting from the dynamic factor model for non-stationary data we derive the factor-augmented error correction model (FECM) and, by generalizing the Granger representation theorem, its moving-average representation. The latter is used for the identification of structural shocks and their propagation mechanism. Besides discussing contemporaneous restrictions along the lines of Bernanke et al. (2005), we show how to implement classical identification schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified real shock.

[1]  Christian Gengenbach,et al.  Error Correction Testing in Panels with Common Stochastic Trends , 2016 .

[2]  Matteo Barigozzi,et al.  Non-Stationary Dynamic Factor Models for Large Datasets , 2016, 1602.02398.

[3]  Marco Lippi,et al.  Dynamic Factor Models, Cointegration, and Error Correction Mechanisms , 2015, 1510.02399.

[4]  Serena Ng,et al.  Working Paper Series , 2019 .

[5]  Jing Cynthia Wu,et al.  WORKING PAPER SERIES FEDERAL RESERVE BANK of ATLANTA Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound , 2014 .

[6]  Helmut Lütkepohl,et al.  Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey , 2014 .

[7]  In Choi,et al.  EFFICIENT ESTIMATION OF FACTOR MODELS , 2011, Econometric Theory.

[8]  Luca Gambetti,et al.  No News in Business Cycles , 2014 .

[9]  Massimiliano Marcellino,et al.  Forecasting with Factor-Augmented Error Correction Models , 2010 .

[10]  J. Westerlund,et al.  A NOTE ON THE POOLING OF INDIVIDUAL PANIC UNIT ROOT TESTS , 2009, Econometric Theory.

[11]  Marco Lippi,et al.  OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS , 2009, Econometric Theory.

[12]  Silvana Tenreyro,et al.  Hot and Cold Seasons in the Housing Market ∗ , 2009 .

[13]  Mario Forni,et al.  The Dynamic Effects of Monetary Policy: A Structural Factor Model Approach , 2008 .

[14]  Serena Ng,et al.  Panel cointegration with global stochastic trends , 2008, 0805.1768.

[15]  Massimiliano Marcellino,et al.  Factor-Augmented Error Correction Models , 2008 .

[16]  F. Dias,et al.  Determining the number of factors in approximate factor models with global and group-specific factors , 2008 .

[17]  Christian Gengenbach,et al.  Panel Error Correction Testing with Global Stochastic Trends , 2008 .

[18]  Helmut Ltkepohl,et al.  New Introduction to Multiple Time Series Analysis , 2007 .

[19]  Sandra Eickmeier.  Comovements and Heterogeneity in the Euro Area Analyzed in a Non-Stationary Dynamic Factor Model , 2007 .

[20]  F. Smets,et al.  Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach , 2007 .

[21]  Mark W. Watson,et al.  Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel , 2007 .

[22]  Serena Ng,et al.  Determining the Number of Primitive Shocks in Factor Models , 2007 .

[23]  Mattias Villani,et al.  Bayesian Estimation of an Open Economy DSGE Model with Incomplete Pass-Through , 2006 .

[24]  J. Bai,et al.  Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions , 2006 .

[25]  Ricardo Mestre,et al.  An area-wide model for the euro area , 2005 .

[26]  Peter J. G. Vlaar.  ON THE ASYMPTOTIC DISTRIBUTION OF IMPULSE RESPONSE FUNCTIONS WITH LONG-RUN RESTRICTIONS , 2004, Econometric Theory.

[27]  Jushan Bai,et al.  Estimating cross-section common stochastic trends in nonstationary panel data , 2004 .

[28]  Serena Ng,et al.  Confidence Intervals for Diffusion Index Forecasts with a Large Number of Predictor , 2004 .

[29]  Jean Boivin,et al.  Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach , 2003 .

[30]  J. Stock,et al.  Forecasting Using Principal Components From a Large Number of Predictors , 2002 .

[31]  J. Stock,et al.  Macroeconomic Forecasting Using Diffusion Indexes , 2002 .

[32]  Eric S. Rosengren,et al.  Board of Governors of the Federal Reserve System , 2002 .

[33]  J. Bai,et al.  A Panic Attack on Unit Roots and Cointegration , 2001 .

[34]  M. Hallin,et al.  The Generalized Dynamic-Factor Model: Identification and Estimation , 2000, Review of Economics and Statistics.

[35]  L. Kilian.  Small-sample Confidence Intervals for Impulse Response Functions , 1998, Review of Economics and Statistics.

[36]  E. Mammen.  The Bootstrap and Edgeworth Expansion , 1997 .

[37]  S. Johansen.  Likelihood-Based Inference in Cointegrated Vector Autoregressive Models , 1996 .

[38]  A. Warne.  A Common Trends Model : Identification, Estimation and Inference , 1993 .

[39]  Olivier J. Blanchard,et al.  The Dynamic Effects of Aggregate Demand and Supply Disturbances , 1988 .

[40]  Charles I. Plosser,et al.  Stochastic Trends and Economic Fluctuations , 1987 .

[41]  T. W. Anderson.  Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions , 1951 .