Volume 18, Issue 4

We extend the “bottom up” approach for forecasting economic aggregates with disaggregates to probability forecasting. Our methodology utilises a linear opinion pool to combine the forecast densities from many disaggregate forecasting specifications, using weights based on the continuous ranked probability score. We also adopt a post-processing step prior to forecast combination. These methods are adapted from the meteorology literature. In our application, we use our approach to forecast US Personal Consumption Expenditure inflation from 1990q1 to 2009q4. Our ensemble combining the evidence from 16 disaggregate PCE series outperforms an integrated moving average specification for aggregate inflation in terms of density forecasting.

We study different implementations of the sparse portfolio construction and rebalancing method introduced by Brodie et al. (Brodie, J., I. Daubechies, C. De Mol, D. Giannone, and I. Loris. 2009. “Sparse and Stable Markowitz Portfolios.” PNAS 106 (30): 12267–12272). This technique is based on the use of a l 1 -norm (sum of the absolute values) type penalization on the portfolio weights vector that regularizes the Markowitz portfolio selection problem by automatically eliminating the dynamical redundancies present in the time evolution of asset prices. We make specific recommendations as to the different estimation techniques for the parameters needed in the use of the method and we prove its good performance in realistic situations involving different rebalancing frequencies and transaction costs. Our empirical findings show that the beneficial effects of the use of sparsity constraints are robust with respect to the choice of trend and covariance estimation methods used in its implementation.

This paper extends Kim’s (Kim, C.-J. 2004. “Markov-Switching Models with Endogenous Explanatory Variables.” Journal of Econometrics 122: 127–136; Kim, C.-J. 2009. “Markov-Switching Models with Endogenous Explanatory Variables II: A Two-Step MLE Procedure.” Journal of Econometrics 148: 46–55.) studies on Markov switching models with endogenous regressors by considering the time-varying relationship between endogenous regressors and instrumental variables. To deal with the endogenous problem, we introduce three estimation methods, e.g., a joint estimation procedure, a two-step estimation procedure and a MCMC estimation procedure. Although the joint estimation procedure provides us with a direct estimator via Kim’s (Kim, C.-J. 1994. “Dynamic Linear Models with Markov-Switching.” Journal of Econometrics 60: 1–22.) approximation, it is not always feasible due to the “curse of dimensionality” problem. In this case, we consider the two-step estimation procedure and the MCMC estimation procedure. Our Monte Carlo experiments show that three estimation procedures are in general feasible and robust. Although the two-step estimation procedure is not as efficient as the MCMC estimation procedure, they both perform better than the joint estimation procedure. In an application to the Campbell and Mankiw’s (1989) consumption model, we document the robustness of our three estimation procedures and verify the significant regime switching behavior of this model.

This paper analyzes the relationship between commodity prices and consumer food prices in the euro area and in its largest countries (Germany, France and Italy) and tests whether the latter respond asymmetrically to shocks to the former. The issue is of particular interest for those monetary authorities that target headline consumer price inflation, which has been heavily influenced by pronounced swings in international commodity prices in the past decade. The empirical analysis is based on two distinct but complementary approaches. We first use a structural model, identify a shock to commodity prices and check through formal econometric tests whether the Impulse Response Functions of food consumer prices is invariant to the sign of the commodity price shock. Next, we employ predictive regressions and examine the relative forecasting ability of linear models with respect to that of models that allow for sign-dependent nonlinearities. Overall, the empirical analysis uncovers very little evidence of asymmetries.

We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this scaling factor. Consequently, we find that the discretized process is also long memory with the same Hurst exponent as the original process. We consider the properties of two estimators of the Hurst exponent, namely the local Whittle (LW) estimator and the detrended fluctuation analysis (DFA). By using analytical considerations and numerical simulations we show that, in presence of round-off error, both estimators are severely negatively biased in finite samples. Under regularity conditions we prove that the LW estimator applied to discretized processes is consistent and asymptotically normal. Moreover, we compute the asymptotic properties of the DFA for a generic (i.e., non-Gaussian) long memory process and we apply the result to discretized processes.