Pilar Poncela

Forecasting with nonstationary dynamic factor models

Abstract In this paper we analyze the structure and the forecasting performance of the dynamic factor model. It is shown that the forecasts obtained by the factor model imply shrinkage pooling terms, similar to the ones obtained from hierarchical Bayesian models that have been applied successfully in the econometric literature. Thus, the results obtained in this paper provide an additional justification for these and other types of pooling procedures. The expected decrease in MSE for using a factor model versus univariate ARIMA and shrinkage models are studied for the one factor model. Monte Carlo simulations are presented to illustrate this result. A factor model is also built to forecast GNP of European countries and it is shown that the factor model can provide a substantial improvement in forecasts with respect to both univariate and shrinkage univariate forecasts.

EXTRACTING NONLINEAR SIGNALS FROM SEVERAL ECONOMIC INDICATORS

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specification (one-step approach) with the “shortcut” of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.

Common dynamics of nonenergy commodity prices and their relation to uncertainty

The purpose of this article is to improve the empirical evidence on commodity prices in various dimensions. First, we attempt to identify the extent of comovements in 44 monthly nonenergy commodity price series in order to ascertain whether the increase in comovement is a recent term phenomenon. Second, we attempt to determine the role of uncertainty in determining comovements among nonenergy prices in the short run. We diagnose the overall comovement using a dynamic factor model estimated by principal components. A factor-augmented vector autoregressive approach is used to assess the relationship of fundamentals, financial and uncertainty variables with the comovement in commodity prices. We find a greater synchronization among raw materials since December 2003. Since that date, uncertainty has played an important role in determining short-run fluctuations in nonenergy raw material prices.

Dimension Reduction in Multivariate Time Series

This chapter compares models for dimension reduction in time series and tests of the dimension of the dynamic structure. We consider both stationary and nonstationary time series and discuss principal components, canonical analysis, scalar component models, reduced rank models, and factor models. The unifying view of canonical correlation analysis between the present and past values of the series is emphasized. Then, we review some of the tests based on canonical correlation analysis to find the dimension of the dynamic relationship among the time series. Finally, the procedures are compared through a real data example.

Data graduation based on statistical time series methods

On the basis of some suitable assumptions, we show that the best linear unbiased estimator of the true mortality rates has the form of Whittakers solution to the graduation problem. Some statistical tools are also proposed to help reducing subjectivity when graduating a dataset.

From Zero to Infinity: The Use of Impact Factors in the Evaluation of Economic Research in Spain

In the present study, we examine the use of short lists of journals in order to assess research performance in Spain - a country that features a rare combination of a thin and incomplete academic market along with an elite of eminent economists. Our analysis reveals that the implementation of bibliometric tools to produce short lists of journals for assessment purposes entail problems with the statistical significance of cutoff rates, neglect of the interdisciplinary nature of economics, and an inability to track progress in academic markets that move towards internationalization and publications in top-tier, premier outlets.

A two factor model to combine US inflation forecasts

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proved hard to surpass. This study considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedmans hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.

Some New Results on the Estimation of Structural Budget Balance for Spain

The recession that started in 2008 caused a sharp deterioration of the budget balance of Spain. This de-cline was not fully anticipated by the structural budget balance due to some methodology limitations. In this article, we calculate an alternative structural balance for Spain in the years prior to the subprime crisis that includes residential investment as an explanatory variable. This estimate shows that by 2004the Spanish fiscal situation was not as strong as presumed. This fragility was hidden by the extraordinary revenue from the real estate bubble and the construction boom.

Green shoots in the euro area. A real time measure

We show that an extension of the Markov-switching dynamic factor models that accounts for the specificities of the day to day monitoring of economic developments such as ragged edges, mixed frequencies and data revisions is a good tool to forecast the Euro area recessions in real time. We provide examples that show the nonlinear nature of the relations between data revisions, point forecasts and forecast uncertainty. According to our empirical results, we think that the real time probabilities of recession are an appropriate statistic to capture what the press call green shoots.

Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment

In the context of Dynamic Factor Models (DFM), we compare point and interval estimates of the underlying unobserved factors extracted using small and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the Maximum Likelihood Kalman filter and smoother (KFS) factor estimates are highly correlated with those obtained using hybrid Principal Components (PC) and KFS procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.