Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama–Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.     

Fitting dynamic factor models to non-stationary time series

Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infinite-length filtering of the factors via dynamic loadings. In this paper, motivated from economic data observed over long time periods which show smooth transitions over time in their covariance structure, we allow the dynamic structure of the factor model to be non-stationary over time by proposing a deterministic time variation of its loadings. In this respect we generalize the existing recent work on static factor models with time-varying loadings as well as the classical, i.e. stationary, dynamic approximate factor model. Motivated from the stationary case, we estimate the common components of our dynamic factor model by the eigenvectors of a consistent estimator of the now time-varying spectral density matrix of the underlying data-generating process. This can be seen as a time-varying principal components approach in the frequency domain. We derive consistency of this estimator in a “double-asymptotic” framework of both cross-section and time dimension tending to infinity. The performance of the estimators is illustrated by a simulation study and an application to a macroeconomic data set.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models

In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information from a large number of predictors. So far, the dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities for forecasting twenty important macroeconomic variables. These alternative models can handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show, both analytically and empirically, that combing forecasts from LASSO-based models with those from dynamic factor models can reduce the mean square forecast error (MSFE) further. Our three main findings can be summarized as follows. First, for most of the variables under investigation, all of the LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at economically meaningful block levels, the new methods greatly enhance the interpretability of the models. Third, once forecasts from a LASSO-based approach are combined with those from a dynamic factor model by forecast combination techniques, the combined forecasts are significantly better than either dynamic factor model forecasts or the naïve random walk benchmark.     

Nonlinear relationship between economic growth and nuances of globalisation with income stratification: Roles of financial development and governance

We study the effect of overall globalisation on economic growth in a neoclassical macroeconomic growth model. We further assess our model by considering the decomposed measures of globalisation including economic, political, and social globalisation components. To this end, we estimate panel data models by applying the cross-sectional dependency-autoregressive distributed lags (CS-ARDL) approach to a sample of 116 countries during the available period, 1980–2015. We classified our sample into upper middle-, lower middle-, and high-income groups to minimise country-specific heterogeneity. Our results affirm the presence of a quadratic (nonlinear) U-shaped relationship between the overall globalisation (including the economic, political, social components) and economic growth for the lower middle- and upper middle-income group. However, they provide evidence of a positive linear relationship between globalisation and economic growth for the high-income countries. Given the arguments that the impact of globalisation on growth is conditional on local financial development (FD) and quality of governance (QoG), we incorporate their role. We provide fresh evidence that the impacts of globalisation on economic growth are more profound in the countries with a higher QoG and a higher deepening of FD. We further check the robustness of our analysis applying the U test and dynamic generalised methods of moment approach. We also provide policy implications.     

Non‐Fundamentalness in Structural Econometric Models: A Review

Current economic theory typically assumes that all the macroeconomic variables belonging to a given economy are driven by a small number of structural shocks. As recently argued, apart from negligible cases, the structural shocks can be recovered if the information set contains current and past values of a large, potentially infinite, set of macroeconomic variables. However, the usual practice of estimating small size causal Vector AutoRegressions can be extremely misleading as in many cases such models could fully recover the structural shocks only if future values of the few variables considered were observable. In other words, the structural shocks may be non‐fundamental with respect to the small dimensional vector used in current macroeconomic practice. By reviewing a recent strand of econometric literature, we show that, as a solution, econometricians should enlarge the space of observations, and thus consider models able to handle very large panels of related time series. Among several alternatives, we review dynamic factor models together with their economic interpretation, and we show how non‐fundamentalness is non‐generic in this framework. Finally, using a factor model, we provide new empirical evidence on the effect of technology shocks on labour productivity and hours worked.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Modeling frailty-correlated defaults using many macroeconomic covariates

We propose a novel time series panel data framework for estimating and forecasting time-varying corporate default rates subject to observed and unobserved risk factors. In an empirical application for a U.S. dataset, we find a large and significant role for a dynamic frailty component even after controlling for more than 80% of the variation in more than 100 macro-financial covariates and other standard risk factors. We emphasize the need for a latent component to prevent a downward bias in estimated default rate volatility and in estimated probabilities of extreme default losses on portfolios of U.S. debt. The latent factor does not substitute for a single omitted macroeconomic variable. We argue that it captures different omitted effects at different times. We also provide empirical evidence that default and business cycle conditions partly depend on different processes. In an out-of-sample forecasting study for point-in-time default probabilities, we obtain mean absolute error reductions of more than forty percent when compared to models with observed risk factors only. The forecasts are relatively more accurate when default conditions diverge from aggregate macroeconomic conditions.     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

Robustness and the general dynamic factor model with infinite-dimensional space: Identification,estimation, and forecasting

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. As second-order models, however, they are sensitive to the presence of outliers—an issue that has not been analyzed so far in the general case of dynamic factors with possibly infinite-dimensional factor spaces (Forni et al. 2000, 2015, 2017). In this paper, we consider this robustness issue and study the impact of additive outliers on the identification, estimation, and forecasting performance of general dynamic factor models. Based on our findings, we propose robust versions of identification, estimation, and forecasting procedures. The finite-sample performance of our methods is evaluated via Monte Carlo experiments and successfully applied to a classical data set of 115 US macroeconomic and financial time series.     

Financial information and macroeconomic forecasts

We study the forecasting power of financial variables for macroeconomic variables in 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices, and house prices have considerable predictive power for macroeconomic variables at the one- to four-quarter horizons. A forecasting model that includes financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85% of our sample countries at the four-quarter horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.     

fETSmcs: Feature-based ETS model component selection

The well-developed ETS (ExponenTial Smoothing, or Error, Trend, Seasonality) method incorporates a family of exponential smoothing models in state space representation and is widely used for automatic forecasting. The existing ETS method uses information criteria for model selection by choosing an optimal model with the smallest information criterion among all models fitted to a given time series. The ETS method under such a model selection scheme suffers from computational complexity when applied to large-scale time series data. To tackle this issue, we propose an efficient approach to ETS model selection by training classifiers on simulated data to predict appropriate model component forms for a given time series. We provide a simulation study to show the model selection ability of the proposed approach on simulated data. We evaluate our approach on the widely used M4 forecasting competition dataset in terms of both point forecasts and prediction intervals. To demonstrate the practical value of our method, we showcase the performance improvements from our approach on a monthly hospital dataset.     

Estimating the output gap in real time: A factor model approach

By using a dynamic factor model, we can substantially improve the reliability of real-time output gap estimates for the U.S. economy. First, we use a factor model to extract a series for the common component in GDP from a large panel of monthly real-time macroeconomic variables. This series is immune to revisions to the extent that revisions are due to unbiased measurement errors or idiosyncratic news. Second, our model is able to handle the unbalanced arrival of the data. This yields favorable nowcasting properties and thus starting conditions for the filtering of data into a trend and deviations from a trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic importance for real-time policy decisions and improves real-time inflation forecasts.     

Impact of Islamic banking development and major macroeconomic variables on economic growth for Islamic countries: Evidence from panel smooth transition models

This study examines the nonlinear relationship between Islamic banking development, major macroeconomic variables and economic growth in Islamic countries. Using the panel smooth transition model, the results show a positive nonlinear relationship between Islamic banking development and economic growth. Moreover, the relationship between the macroeconomic variables and economic growth is asymmetric and regime-dependent. Further, by using the dynamic panel quantile model, we show that for many cases the Islamic banking variables lead economic growth across the quantiles. More specifically, foreign direct investment, oil production and inflation have a positive impact on economic growth during the normal financial development state, while government consumption, one-lag economic growth, terms of trade and financial development have a negative impact on economic growth for this state. The human capital index, education and the rule of law have an insignificant impact, regardless of the prevailing regime. The results for the separated oil-importing and oil-exporting economies are generally consistent with the combined sample regarding the Islamic banking development variables. As for the macro variables, they have a positive and significant (insignificant) effect on EG for the oil-importing (oil-exporting) economies for almost all models.     

Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting

Factor models have been applied extensively for forecasting when high‐dimensional datasets are available. In this case, the number of variables can be very large. For instance, usual dynamic factor models in central banks handle over 100 variables. However, there is a growing body of literature indicating that more variables do not necessarily lead to estimated factors with lower uncertainty or better forecasting results. This paper investigates the usefulness of partial least squares techniques that take into account the variable to be forecast when reducing the dimension of the problem from a large number of variables to a smaller number of factors. We propose different approaches of dynamic sparse partial least squares as a means of improving forecast efficiency by simultaneously taking into account the variable forecast while forming an informative subset of predictors, instead of using all the available ones to extract the factors. We use the well‐known Stock and Watson database to check the forecasting performance of our approach. The proposed dynamic sparse models show good performance in improving efficiency compared to widely used factor methods in macroeconomic forecasting. Copyright © 2014 John Wiley & Sons, Ltd.     

Macroeconomic Forecasting using Low‐frequency Filters

We consider univariate low‐frequency filters applicable in real‐time as a macroeconomic forecasting method. This amounts to targeting only low frequency fluctuations of the time series of interest. We show through simulations that such approach is warranted and, using US data, we confirm empirically that consistent gains in forecast accuracy can be obtained in comparison with a variety of other methods. There is an inherent arbitrariness in the choice of the cut‐off defining low and high frequencies, which calls for a careful characterization of the implied optimal (for forecasting) degree of smoothing of the key macroeconomic indicators we analyse. We document interesting patterns that emerge: for most variables the optimal choice amounts to disregarding fluctuations well below the standard business cycle cut‐off of 32 quarters while generally increasing with the forecast horizon; for inflation and variables related to housing this cut‐off lies around 32 quarters for all horizons, which is below the optimal level for federal government spending.     

Nowcasting GDP using machine-learning algorithms: A real-time assessment

Can machine-learning algorithms help central banks understand the current state of the economy? Our results say yes! We contribute to the emerging literature on forecasting macroeconomic variables using machine-learning algorithms by testing the nowcast performance of common algorithms in a full ‘real-time’ setting—that is, with real-time vintages of New Zealand GDP growth (our target variable) and real-time vintages of around 600 predictors. Our results show that machine-learning algorithms are able to significantly improve over a simple autoregressive benchmark and a dynamic factor model. We also show that machine-learning algorithms have the potential to add value to, and in one case improve on, the official forecasts of the Reserve Bank of New Zealand.     

Dynamics of financial returns densities: A functional approach applied to the Bovespa intraday index

We model the stochastic evolution of the probability density functions (PDFs) of Ibovespa intraday returns over business days, in a functional time series framework. We find evidence that the dynamic structure of the PDFs reduces to a vector process lying in a two-dimensional space. Our main contributions are as follows. First, we provide further insights into the finite-dimensional decomposition of the curve process: it is shown that its evolution can be interpreted as a dynamic dispersion-symmetry shift. Second, we provide an application to realized volatility forecasting, with a forecasting ability that is comparable to those of HAR realized volatility models in the model confidence set framework.     

Nowcasting China’s GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated. Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises. In this study, we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data. We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors. We demonstrate the validity of our approach through simulation studies, and explore the applicability of our approach through an empirical study in nowcasting the China’s GDP using 117 monthly data series of several categories in the Chinese market. The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China’s GDP.