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GLOBAL COMMODITY CYCLES AND LINKAGES
, 1170
"... commodity cycles and linkages a FAVAR approach by Marco Lombardi, ..."
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commodity cycles and linkages a FAVAR approach by Marco Lombardi,
Panel Time-Series
, 2010
"... Traditionally economic panels had large number of cross-section units and relatively few time periods and econometric methods were developed for such ‘large N small T’ data. More recently panels with observations for a large numbers of time periods have become available on cross-section units like f ..."
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Traditionally economic panels had large number of cross-section units and relatively few time periods and econometric methods were developed for such ‘large N small T’ data. More recently panels with observations for a large numbers of time periods have become available on cross-section units like firms, industries, regions or countries. These notes explore the econometric methods developed for such ‘large N large T’ data. Such data allow more explicit treatment of (a) heterogeneity across units (b) dynamics, including the treatment of unit roots and cointegration and (c) cross-section dependence arising from spatial interactions or unobserved common factors.
Common Drivers in Emerging Market Spreads and Commodity Prices. Working Paper 2012/57. Buenos Aires: Banco Central de la Republica Argentina
, 2012
"... 1 Common Drivers in Emerging Market Spreads and Commodity Prices Diego Bastourre (BCRA, UNLP) Jorge Carrera (BCRA, UNLP) Javier Ibarlucia (BCRA, UNLP) Mariano Sardi (BCRA) Abstract This paper presents and evaluates the hypothesis that emerging countries specialized in commodity production are prone ..."
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1 Common Drivers in Emerging Market Spreads and Commodity Prices Diego Bastourre (BCRA, UNLP) Jorge Carrera (BCRA, UNLP) Javier Ibarlucia (BCRA, UNLP) Mariano Sardi (BCRA) Abstract This paper presents and evaluates the hypothesis that emerging countries specialized in commodity production are prone to experience non orthogonal commercial and financial shocks. Specifically, we investigate a set of global macroeconomic variables that, in principle, could simultaneously determine in opposite direction commodity prices and bonds spreads in commodity-exporting emerging economies. Employing common factors techniques and pairwise correlation analysis we find a strong negative correlation between commodity prices and emerging market spreads. Moreover, the empirical FAVAR (Factor Augmented VAR) model developed to test our main hypothesis confirms that this negative association pattern is not only explained by the fact that commodity prices are one of the most relevant fundamentals for commodity exporters bond spreads. In particular, we find that reductions in international interest rates and global risk appetite; rises in quantitative global liquidity measures and equity returns; and US dollar depreciations, tend to diminish spreads of emerging economies and strengthen commodity prices simultaneously. These results are relevant in order to improve our knowledge regarding the reasons behind some typical characteristics of emerging commodity producers, such as their tendency to experience high levels of macroeconomic volatility and procyclicality, or their propensity to be affected from exchange rate overshooting, external crisis and sudden stops. Concerning policy lessons, a key conclusion is the difficulty in disentangle challenges coming from financial openness and structural considerations in emerging economies, such as the lack of diversification of the productive structure or the difficulties of a growth strategy solely based on natural resources. It would be profitable to internalize the connection between these two key variables in formulating and conducting economic policy. JEL Classification: F32, F42, O13 2
Macroeconomic Forecasting Using Penalized Regression Methods Citation for published version (APA): Macroeconomic Forecasting Using Penalized Regression Methods *
"... Abstract We study the suitability of lasso-type penalized regression techniques when applied to macroeconomic forecasting with high-dimensional datasets. We consider performance of the lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumption underlying penalized ..."
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Abstract We study the suitability of lasso-type penalized regression techniques when applied to macroeconomic forecasting with high-dimensional datasets. We consider performance of the lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumption underlying penalized regression methods. We also investigate how the methods handle unit roots and cointegration in the data. In an extensive simulation study we find that penalized regression methods are more robust to mis-specification than factor models estimated by principal components, even if the underlying DGP is a factor model. Furthermore, the penalized regression methods are demonstrated to deliver forecast improvements over traditional approaches when applied to non-stationary data containing cointegrated variables, despite a deterioration of the selective capabilities. Finally, we also consider an empirical application to a large macroeconomic U.S. dataset and demonstrate that, in line with our simulations, penalized regression methods attain the best forecast accuracy most frequently.
Structural FECM: Cointegration in large-scale structural FAVAR models
"... Abstract Starting from the dynamic factor model for non-stationary data we derive the factor-augmented error correction model (FECM) ..."
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Abstract Starting from the dynamic factor model for non-stationary data we derive the factor-augmented error correction model (FECM)
Mining Big Data Using Parsimonious Factor and Shrinkage Methods
, 2014
"... A number of recent studies have focused on the usefulness of factor models in the context of prediction using big data (see e.g., Bai and Ng (2008), Dufour and Stevanovic (2010), Forni et al. (2000, 2005), Kim and Swanson (2014), Stock and Watson (2002b, 2006, 2012), and the references cited therei ..."
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A number of recent studies have focused on the usefulness of factor models in the context of prediction using big data (see e.g., Bai and Ng (2008), Dufour and Stevanovic (2010), Forni et al. (2000, 2005), Kim and Swanson (2014), Stock and Watson (2002b, 2006, 2012), and the references cited therein). We add to this literature by analyzing the predictive bene
ts associated with the use of independent component analysis (ICA) and sparse principal component analysis (SPCA), coupled with a variety of other factor estimation and data shrinkage methods, including bagging, boosting, and the elastic net, among others. We carry out a forecasting horse-race, involving the estimation of 28 di¤erent baseline model types, each constructed using a variety of speci
cation approaches, estimation approaches, and benchmark econometric models; and all used in the prediction of 11 key macroeconomic variables relevant for monetary policy assessment. In numerous instances we nd that a variety of benchmark autoregressive models and model averaging methods are mean square forecast error (MSFE) dominated by more complicated nonlinear methods. For example, simple averaging methods are MSFE best in only 9 of 33 key cases considered. However, in order to beatmodel averaging methods, we must combine new factor estimation methods with interesting new forms of shrinkage. For example, SPCA yields MSFE-best prediction models in many cases, particularly when coupled with shrinkage. In summary, we present empirical results that provide strong new evidence of the usefulness of sophisticated factor based forecasting methods, and as a corollary, of the use of big datain macroeconometric forecasting.
