Forecasting US employment growth using forecast combining methods

We examine different approaches to forecasting monthly US employment growth in the presence of many potentially relevant predictors. We first generate simulated out‐of‐sample forecasts of US employment growth at multiple horizons using individual autoregressive distributed lag (ARDL) models based on 30 potential predictors. We then consider different methods from the extant literature for combining the forecasts generated by the individual ARDL models. Using the mean square forecast error (MSFE) metric, we investigate the performance of the forecast combining methods over the last decade, as well as five periods centered on the last five US recessions. Overall, our results show that a number of combining methods outperform a benchmark autoregressive model. Combining methods based on principal components exhibit the best overall performance, while methods based on simple averaging, clusters, and discount MSFE also perform well. On a cautionary note, some combining methods, such as those based on ordinary least squares, often perform quite poorly. Copyright © 2008 John Wiley & Sons, Ltd.     

Short-term forecasting of the US unemployment rate

This paper aims to assess whether Google search data are useful when predicting the US unemployment rate among other more traditional predictor variables. A weekly Google index is derived from the keyword “unemployment” and is used in diffusion index variants along with the weekly number of initial claims and monthly estimated latent factors. The unemployment rate forecasts are generated using MIDAS regression models that take into account the actual frequencies of the predictor variables. The forecasts are made in real time, and the forecasts of the best forecasting models exceed, for the most part, the root mean squared forecast error of two benchmarks. However, as the forecasting horizon increases, the forecasting performance of the best diffusion index variants decreases over time, which suggests that the forecasting methods proposed in this paper are most useful in the short term.     

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.     

Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy

The aim of this study was to forecast the Singapore gross domestic product (GDP) growth rate by employing the mixed‐data sampling (MIDAS) approach using mixed and high‐frequency financial market data from Singapore, and to examine whether the high‐frequency financial variables could better predict the macroeconomic variables. We adopt different time‐aggregating methods to handle the high‐frequency data in order to match the sampling rate of lower‐frequency data in our regression models. Our results showed that MIDAS regression using high‐frequency stock return data produced a better forecast of GDP growth rate than the other models, and the best forecasting performance was achieved by using weekly stock returns. The forecasting result was further improved by performing intra‐period forecasting.     

Forecasting the Term Structure when Short‐Term Rates are Near Zero

This paper compares the experience of forecasting the UK government bond yield curve before and after the dramatic lowering of short‐term interest rates from October 2008. Out‐of‐sample forecasts for 1, 6 and 12 months are generated from each of a dynamic Nelson–Siegel model, autoregressive models for both yields and the principal components extracted from those yields, a slope regression and a random walk model. At short forecasting horizons, there is little difference in the performance of the models both prior to and after 2008. However, for medium‐ to longer‐term horizons, the slope regression provided the best forecasts prior to 2008, while the recent experience of near‐zero short interest rates coincides with a period of forecasting superiority for the autoregressive and dynamic Nelson–Siegel models. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.     

The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach

The difficulty in modelling inflation and the significance in discovering the underlying data‐generating process of inflation is expressed in an extensive literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting US inflation based on autoregressive and structural models of the term structure. We employ two nonlinear methodologies: the econometric least absolute shrinkage and selection operator (LASSO) and the machine‐learning support vector regression (SVR) method. The SVR has never been used before in inflation forecasting considering the term spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871:1–2015:3 that covers the entire history of inflation in the US economy. For comparison purposes we also use ordinary least squares regression models as a benchmark. In order to evaluate the contribution of the term spread in inflation forecasting in different time periods, we measure the out‐of‐sample forecasting performance of all models using rolling window regressions. Considering various forecasting horizons, the empirical evidence suggests that the structural models do not outperform the autoregressive ones, regardless of the model's method. Thus we conclude that the term spread models are not more accurate than autoregressive models in inflation forecasting. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimal Hedge Ratio Estimation and Effectiveness Using ARCD

This paper examines the importance of forecasting higher moments for optimal hedge ratio estimation. To this end, autoregressive conditional density (ARCD) models are employed which allow for time variation in variance, skewness and kurtosis. The performance of ARCD models is evaluated against that of GARCH and of other conventional hedge ratio estimation methodologies based on exponentially weighted moving averages, ordinary least squares and error correction, respectively. An empirical application using spot and futures data on the DJI, FTSE and DAX equity indices compares the in‐sample and out‐of‐sample hedging effectiveness of each approach in terms of risk minimization. The results show that the ARCD approach has the best performance, thus suggesting that forecasting higher moments is of practical importance for futures hedging. Copyright © 2012 John Wiley & Sons, Ltd.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Forecasting key US macroeconomic variables with a factor‐augmented Qual VAR

In this paper, we first extract factors from a monthly dataset of 130 macroeconomic and financial variables. These extracted factors are then used to construct a factor‐augmented qualitative vector autoregressive (FA‐Qual VAR) model to forecast industrial production growth, inflation, the Federal funds rate, and the term spread based on a pseudo out‐of‐sample recursive forecasting exercise over an out‐of‐sample period of 1980:1 to 2014:12, using an in‐sample period of 1960:1 to 1979:12. Short‐, medium‐, and long‐run horizons of 1, 6, 12, and 24 months ahead are considered. The forecast from the FA‐Qual VAR is compared with that of a standard VAR model, a Qual VAR model, and a factor‐augmented VAR (FAVAR). In general, we observe that the FA‐Qual VAR tends to perform significantly better than the VAR, Qual VAR and FAVAR (barring some exceptions relative to the latter). In addition, we find that the Qual VARs are also well equipped in forecasting probability of recessions when compared to probit models.     

Model specification and forecasting foreign exchange rates with vector autoregressions

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven‐variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non‐stationary, stationary and error‐correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non‐stationary specification outperformed those of the stationary and error‐correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error‐correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak. Copyright © 2001 John Wiley & Sons, Ltd.     

Forecasting inflation using univariate continuous-time stochastic models

In this paper we investigate the applicability of several continuous-time stochastic models to forecasting inflation rates with horizons out to 20 years. While the models are well known, new methods of parameter estimation and forecasts are supplied, leading to rigorous testing of out-of-sample inflation forecasting at short and long time horizons. Using US consumer price index data we find that over longer forecasting horizons—that is, those beyond 5 years—the log-normal index model having Ornstein–Uhlenbeck drift rate provides the best forecasts.     

Predictive likelihood for coherent forecasting of count time series

A new forecasting method based on the concept of the profile predictive likelihood function is proposed for discrete‐valued processes. In particular, generalized autoregressive moving average (GARMA) models for Poisson distributed data are explored in detail. Highest density regions are used to construct forecasting regions. The proposed forecast estimates and regions are coherent. Large‐sample results are derived for the forecasting distribution. Numerical studies using simulations and two real data sets are used to establish the performance of the proposed forecasting method. Robustness of the proposed method to possible misspecifications in the model is also studied.     

Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

This paper compares the in‐sample fitting and the out‐of‐sample forecasting performances of four distinct Nelson–Siegel class models: Nelson–Siegel, Bliss, Svensson, and a five‐factor model we propose in order to enhance the fitting flexibility. The introduction of the fifth factor resulted in superior adjustment to the data. For the forecasting exercise the paper contrasts the performances of the term structure models in association with the following econometric methods: quantile autoregression evaluated at the median, VAR, AR, and a random walk. As a pattern, the quantile procedure delivered the best results for longer forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.     

Quantile Double AR Time Series Models for Financial Returns

We develop a novel quantile double autoregressive model for modelling financial time series. This is done by specifying a generalized lambda distribution to the quantile function of the location‐scale double autoregressive model developed by Ling (2004, 2007). Parameter estimation uses Markov chain Monte Carlo Bayesian methods. A simulation technique is introduced for forecasting the conditional distribution of financial returns m periods ahead, and hence any for predictive quantities of interest. The application to forecasting value‐at‐risk at different time horizons and coverage probabilities for Dow Jones Industrial Average shows that our method works very well in practice. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting Private Consumption by Consumer Surveys

Survey‐based indicators are widely seen as leading indicators for economic activity. As such, consumer confidence might be informative for the future path of private consumption. Although the indicators receive high attention in the media, their forecasting power often appears to be very limited. This paper takes a fresh look at the data that serve as a basis for the consumer confidence indicator (CCI) reported by the EU Commission for the euro area. Different pooling methods are applied to exploit the survey information. Forecasts are based on mixed data sampling (MIDAS) and bridge equations. While the CCI does not outperform the autoregressive benchmark, the new indicators are able to raise forecasting performance. The best performing indicator should be built upon pre‐selection methods. Data‐driven aggregation methods should be preferred to determine the weights of the individual ingredients. Copyright © 2011 John Wiley & Sons, Ltd.     

Nelson–Siegel,Affine and Quadratic Yield Curve Specifications: Which One is Better at Forecasting?

In this paper we compare the in‐sample fit and out‐of‐sample forecasting performance of no‐arbitrage quadratic, essentially affine and dynamic Nelson–Siegel term structure models. In total, 11 model variants are evaluated, comprising five quadratic, four affine and two Nelson–Siegel models. Recursive re‐estimation and out‐of‐sample 1‐, 6‐ and 12‐month‐ahead forecasts are generated and evaluated using monthly US data for yields observed at maturities of 1, 6, 12, 24, 60 and 120 months. Our results indicate that quadratic models provide the best in‐sample fit, while the best out‐of‐sample performance is generated by three‐factor affine models and the dynamic Nelson–Siegel model variants. Statistical tests fail to identify one single best forecasting model class. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecast Evaluation of Nonlinear Models: The Case of Long‐Span Real Exchange Rates

This paper deals with the nonlinear modeling and forecasting of the dollar–sterling and franc–sterling real exchange rates using long spans of data. Our contribution is threefold. First, we provide significant evidence of smooth transition dynamics in the series by employing a battery of recently developed in‐sample statistical tests. Second, we investigate the small‐sample properties of several evaluation measures for comparing recursive forecasts when one of the competing models is nonlinear. Finally, we run a forecasting race for the post‐Bretton Woods era between the nonlinear real exchange rate model, the random walk, and the linear autoregressive model. The nonlinear model outperforms all rival models in the dollar–sterling case but cannot beat the linear autoregressive in the franc–sterling. Copyright © 2011 John Wiley & Sons, Ltd.     

How helpful are spatial effects in forecasting the growth of Chinese provinces?

In this paper, we make multi‐step forecasts of the annual growth rates of the real gross regional product (GRP) for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed for. We find that both pooling and accounting for spatial effects help substantially to improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It is also shown that the effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at the 1‐year horizon and exceeds 25% at the 13‐ and 14‐year horizons). Copyright © 2010 John Wiley & Sons, Ltd.     

Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non‐linear models

This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non‐linear framework. We reject linearity for the US and UK swap spreads in favour of a regime‐switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non‐linear nearest‐neighbours model as well as that of linear AR and VAR models. We find some evidence that the non‐linear models predict better than the linear ones. At short horizons, the nearest‐neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models. Copyright © 2007 John Wiley & Sons, Ltd.