Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model

The purpose of this paper is to compare the forecasting power of DFM and LBVAR models as they are used to forecast house price growth rates for 42 metropolitan areas in the United States. The forecasting performances of these two large-scale models are compared based on the Theil U-statistic.Housing market, DFM, LBVAR, dynamic PCA, Demand and Price Analysis,

Forecasting German GDP using alternative factor models based on large datasets

This paper discusses the forecasting performance of alternative factor models based on a large panel of quarterly time series for the german economy. One model extracts factors by static principals components analysis, the other is based on dynamic principal components obtained using frequency domain methods. The third model is based on subspace algorithm for state space models. Out-of-sample forecasts show that the prediction errors of the factor models are generally smaller than the errors of simple autoregressive benchmark models. Among the factors models, either the dynamic principal component model or the subspace factor model rank highest in terms of forecast accuracy in most cases. However, neither of the dynamic factor models can provide better forecasts than the static model over all forecast horizons and different specifications of the simulation design. Therefore, the application of the dynamic factor models seems to provide only small forecasting improvements over the static factor model for forecasting German GDP. --Factor models,static and dynamic factors,principal components,forecasting accuracy

Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing

We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.Comment: 27 pages, 13 figures, link to external web site for supplementary animated figure

Measuring Business Cycle Turning Points in Japan with a Dynamic Markov Switching Factor Model

In the dynamic factor model, a single unobserved factor common to some macroeconomic variables is defined as a composite index to measure business cycles. This model has recently been developed by combining with the regime switching model so that the mean growth of the index may shift depending on whether the economy is in a boom regime or in a recession regime. An advantage of this dynamic Markov switching factor model is that estimating the model by a Bayesian method produces the posterior probabilities that the economy is in the recession regime, which can be used to date the business cycle turning points. This article estimates the dynamic Markov switching factor model using some macroeconomic variables in Japan. The model comparison using the Bayes factor does not provide strong evidence that the mean growth of the index shifts, but the dynamic Markov switching factor model is found to produce the estimates of turning points close to the reference dates of the Economic and Social Research Institute in the Cabinet Office, unless only weakly correlated variables are used.

Viscoelastic model for the dynamic structure of binary systems

This paper presents the viscoelastic model for the Ashcroft-Langreth dynamic structure factors of liquid binary mixtures. We also provide expressions for the Bhatia-Thornton dynamic structure factors and, within these expressions, show how the model reproduces both the dynamic and the self-dynamic structure factors corresponding to a one-component system in the appropriate limits (pseudobinary system or zero concentration of one component). In particular we analyze the behavior of the concentration-concentration dynamic structure factor and longitudinal current, and their corresponding counterparts in the one-component limit, namely, the self dynamic structure factor and self longitudinal current. The results for several lithium alloys with different ordering tendencies are compared with computer simulations data, leading to a good qualitative agreement, and showing the natural appearance in the model of the fast sound phenomenon.Comment: 20 pages, 19 figures, submitted to PR

Forecasting in dynamic factor models using Bayesian model averaging

This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. For both GDP and inflation, we find that the models which contain factors do out-forecast an AR(p), but only by a relatively small amount and only at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of dependent variable seem to contain most of the information relevant for forecasting. Relative to the small forecasting gains provided by including factors, the gains provided by using Bayesian model averaging over forecasting methods based on a single model are appreciable

Nowcasting an Economic Aggregate with Disaggregate Dynamic Factors: An Application to Portuguese GDP

This paper consists of an empirical study comparing a dynamic factor model approach to estimate the current quarter aggregate GDP with the alternative approach of aggregating the forecasts obtained from specific dynamic factor models for each major expenditure disaggregate. The out-of-sample forecasting performance results suggest that there is no advantage in aggregating the disaggregate forecasts.Forecasting; Dynamic Factor Model; Temporal Disaggregation

A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle

We build a small-scale factor model for the GDP of one of the hardest hit economies during the latest recession to study the exact dynamic versus static factor model performance along a business cycle, with an emphasis placing on nowcasting performance during a pronounced switch of business cycle phases due to the latest recession. We compare the factor models' nowcasting performance to a random walk, autoregressive and the best-performing nowcasting models at our hands, which are vector autoregressive (VAR) models. It is shown that a small-scale static factor-augmented VAR (FAVAR) model tends to improve upon the nowcasting performance of the VAR models when the model span and the nowcasting period stretch beyond a single business cycle phase, while exact dynamic factor models tend to fail to detect the timing and depth of the recession regardless of ARMA specifications. As regards the case when the model span and the nowcasting period are contained within a single business cycle phase, static and dynamic factor models appear to show similar performance with potentially slight superiority of dynamic factor models if the factor-forming set of variables and factor dynamics are carefully selected.nowcasting; business cycle; static versus dynamic factors; small-scale FAVAR; VAR; GDP

Measuring bank capital requirements through Dynamic Factor analysis

In this paper, using industry sector stock returns as proxies of firm asset values, we obtain bank capital requirements (through the cycle). This is achieved by Montecarlo simulation of a bank loan portfolio loss density. We depart from the Basel 2 analytical formula developed by Gordy (2003) for the computation of the economic capital by, first, allowing dynamic heterogeneity in the factor loadings, and, also, by accounting for stochastic dependent recoveries. Dynamic heterogeneity in the factor loadings is introduced by using dynamic forecast of a Dynamic Factor model fitted to a large dataset of macroeconomic credit drivers. The empirical findings show that there is a decrease in the degree of Portfolio Credit Risk, once we move from the Basel 2 analytic formula to the Dynamic Factor model specification.Dynamic Factor Model, Forecasting, Stochastic Simulation, Risk Management, Banking