Short‐term forecasts of economic activity: Are fortnightly factors useful?

A short‐term mixed‐frequency model is proposed to estimate and forecast Italian economic activity fortnightly. We introduce a dynamic one‐factor model with three frequencies (quarterly, monthly, and fortnightly) by selecting indicators that show significant coincident and leading properties and are representative of both demand and supply. We conduct an out‐of‐sample forecasting exercise and compare the prediction errors of our model with those of alternative models that do not include fortnightly indicators. We find that high‐frequency indicators significantly improve the real‐time forecasts of Italian gross domestic product (GDP); this result suggests that models exploiting the information available at different lags and frequencies provide forecasting gains beyond those based on monthly variables alone. Moreover, the model provides a new fortnightly indicator of GDP, consistent with the official quarterly series.     

The Effects of Disaggregation on Forecasting Nonstationary Time Series

This paper focuses on the effects of disaggregation on forecast accuracy for nonstationary time series using dynamic factor models. We compare the forecasts obtained directly from the aggregated series based on its univariate model with the aggregation of the forecasts obtained for each component of the aggregate. Within this framework (first obtain the forecasts for the component series and then aggregate the forecasts), we try two different approaches: (i) generate forecasts from the multivariate dynamic factor model and (ii) generate the forecasts from univariate models for each component of the aggregate. In this regard, we provide analytical conditions for the equality of forecasts. The results are applied to quarterly gross domestic product (GDP) data of several European countries of the euro area and to their aggregated GDP. This will be compared to the prediction obtained directly from modeling and forecasting the aggregate GDP of these European countries. In particular, we would like to check whether long‐run relationships between the levels of the components are useful for improving the forecasting accuracy of the aggregate growth rate. We will make forecasts at the country level and then pool them to obtain the forecast of the aggregate. The empirical analysis suggests that forecasts built by aggregating the country‐specific models are more accurate than forecasts constructed using the aggregated data. Copyright © 2014 John Wiley & Sons, Ltd.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

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.     

Parameter Space Restrictions in State Space Models

The state space model is widely used to handle time series data driven by related latent processes in many fields. In this article, we suggest a framework to examine the relationship between state space models and autoregressive integrated moving average (ARIMA) models by examining the existence and positive‐definiteness conditions implied by auto‐covariance structures. This study covers broad types of state space models frequently used in previous studies. We also suggest a simple statistical test to check whether a certain state space model is appropriate for the specific data. For illustration, we apply the suggested procedure in the analysis of the United States real gross domestic product data. Copyright © 2011 John Wiley & Sons, Ltd.     

Short‐term forecasts of euro area real GDP growth: an assessment of real‐time performance based on vintage data

Forecasters commonly predict real gross domestic product growth from monthly indicators such as industrial production, retail sales and surveys, and therefore require an assessment of the reliability of such tools. While forecast errors related to model specification and unavailability of data in real time have been assessed, the impact of data revisions on forecast accuracy has seldom been evaluated, especially for the euro area. This paper proposes to evaluate the contributions of these three sources of forecast error using a set of data vintages for the euro area. The results show that gains in accuracy of forecasts achieved by using monthly data on actual activity rather than surveys or financial indicators are offset by the fact that the former set of monthly data is harder to forecast and less timely than the latter set. These results provide a benchmark which future research may build on as more vintage datasets become available. Copyright © 2008 John Wiley & Sons, Ltd.     

Predicting Recessions with Factor Linear Dynamic Harmonic Regressions

We propose a new framework for building composite leading indicators for the Spanish economy using monthly targeted predictors and small‐scale dynamic factor models. Our leading indicator index, based on the low‐frequency components of four monthly economic variables, is able to predict the onset of the Spanish recessions as well as the gross domestic product (GDP) growth cycles and classical industrial production cycles, both historically and in real time. Also, our leading indicator provides substantial aid in forecasting annual and quarterly GDP growth rates. Using only real data available at the beginning of each forecast period, our indicator one‐step‐ahead forecasts shows substantial improvements over other alternatives. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model

In this paper a multivariate time series model using the seemingly unrelated time series equation (SUTSE) framework is proposed to forecast longevity gains. The proposed model is represented in state space form and uses Kalman filtering to estimate the unobservable components and fixed parameters. We apply the model both to male mortality rates in Portugal and the USA. Our results compare favorably, in terms of mean absolute percentage error, in‐sample and out‐of‐sample, to those obtained by the Lee–Carter method and some of its extensions. Copyright © 2015 John Wiley & Sons, Ltd.     

Bayesian analysis of concurrent time series with application to regional IBM revenue data

Business data frequently arise in the form of concurrent time series. We present a general framework for simultaneous modeling and fitting of such series using the class of Box—Jenkins models. This framework is an exchangeable hierarchical Bayesian model incorporating dependence among the series. Our motivating data set consists of regional IBM revenue available monthly for several geographic regions. Stationary seasonal autoregressive models are simultaneously fit to the regional data series using various error covariance specifications for the strong interregional dependence. A modified Gibbs sampling algorithm is used to carry out the fitting and to enable all subsequent inference. Graphical techniques using predictive distributions are employed to assess model adequacy and to select among models. Outlier estimation and prediction under the chosen model are used for planning and to measure the effect of special promotional events.     

A note on the predictive power of survey data in nowcasting euro area GDP

This paper investigates the trade‐off between timeliness and quality in nowcasting practices. This trade‐off arises when the frequency of the variable to be nowcast, such as gross domestic product (GDP), is quarterly, while that of the underlying panel data is monthly; and the latter contains both survey and macroeconomic data. These two categories of data have different properties regarding timeliness and quality: the survey data are timely available (but might possess less predictive power), while the macroeconomic data possess more predictive power (but are not timely available because of their publication lags). In our empirical analysis, we use a modified dynamic factor model which takes three refinements for the standard dynamic factor model of Stock and Watson (Journal of Business and Economic Statistics, 2002, 20, 147–162) into account, namely mixed frequency, preselections and cointegration among the economic variables. Our main finding from a historical nowcasting simulation based on euro area GDP is that the predictive power of the survey data depends on the economic circumstances; namely, that survey data are more useful in tranquil times, and less so in times of turmoil.     

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality‐forecasting models be associated with real‐world trends in health‐related variables? Does inclusion of health‐related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle‐related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models. Copyright © 2014 John Wiley & Sons, Ltd.     

Comparing the DSGE model with the factor model: an out‐of‐sample forecasting experiment

In this paper, we put dynamic stochastic general equilibrium DSGE forecasts in competition with factor forecasts. We focus on these two models since they represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic background; the factor model on the other hand is mainly data‐driven. We show that incorporating a large information set using factor analysis can indeed improve the short‐horizon predictive ability, as claimed by many researchers. The micro‐founded DSGE model can provide reasonable forecasts for US inflation, especially with growing forecast horizons. To a certain extent, our results are consistent with the prevailing view that simple time series models should be used in short‐horizon forecasting and structural models should be used in long‐horizon forecasting. Our paper compares both state‐of‐the‐art data‐driven and theory‐based modelling in a rigorous manner. Copyright © 2008 John Wiley & Sons, Ltd.     

Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?

Recently developed structural models of the global crude oil market imply that the surge in the real price of oil between mid 2003 and mid 2008 was driven by repeated positive shocks to the demand for all industrial commodities, reflecting unexpectedly high growth mainly in emerging Asia. We evaluate this proposition using an alternative data source and a different econometric methodology. Rather than inferring demand shocks from an econometric model, we utilize a direct measure of global demand shocks based on revisions of professional real gross domestic product (GDP) growth forecasts. We show that forecast surprises during 2003–2008 were associated primarily with unexpected growth in emerging economies (in conjunction with much smaller positive GDP‐weighted forecast surprises in the major industrialized economies), that markets were repeatedly surprised by the strength of this growth, that these surprises were associated with a hump‐shaped response of the real price of oil that reaches its peak after 12–16 months, and that news about global growth predict much of the surge in the real price of oil from mid 2003 until mid 2008 and much of its subsequent decline. Copyright © 2012 John Wiley & Sons, Ltd.     

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 multivariate time series with linear restrictions using constrained structural state‐space models

This paper presents a methodology for modelling and forecasting multivariate time series with linear restrictions using the constrained structural state‐space framework. The model has natural applications to forecasting time series of macroeconomic/financial identities and accounts. The explicit modelling of the constraints ensures that model parameters dynamically satisfy the restrictions among items of the series, leading to more accurate and internally consistent forecasts. It is shown that the constrained model offers superior forecasting efficiency. A testable identification condition for state space models is also obtained and applied to establish the identifiability of the constrained model. The proposed methods are illustrated on Germany's quarterly monetary accounts data. Results show significant improvement in the predictive efficiency of forecast estimators for the monetary account with an overall efficiency gain of 25% over unconstrained modelling. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasting US GNP growth: The role of uncertainty

A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.     

Creating high‐frequency national accounts with state‐space modelling: a Monte Carlo experiment

This paper assesses a new technique for producing high‐frequency data from lower frequency measurements subject to the full set of identities within the data all holding. The technique is assessed through a set of Monte Carlo experiments. The example used here is gross domestic product (GDP) which is observed at quarterly intervals in the United States and it is a flow economic variable rather than a stock. The problem of constructing an unobserved monthly GDP variable can be handled using state space modelling. The solution of the problem lies in finding a suitable state space representation. A Monte Carlo experiment is conducted to illustrate this concept and to identify which variant of the model gives the best monthly estimates. The results demonstrate that the more simple models do almost as well as more complex ones and hence there may be little gain in return for the extra work of using a complex model. Copyright © 2001 John Wiley & Sons, Ltd.     

Bayesian Analysis of Vector ARMA Models using Gibbs Sampling

We present a methodology for estimation, prediction, and model assessment of vector autoregressive moving-average (VARMA) models in the Bayesian framework using Markov chain Monte Carlo algorithms. The sampling-based Bayesian framework for inference allows for the incorporation of parameter restrictions, such as stationarity restrictions or zero constraints, through appropriate prior specifications. It also facilitates extensive posterior and predictive analyses through the use of numerical summary statistics and graphical displays, such as box plots and density plots for estimated parameters. We present a method for computationally feasible evaluation of the joint posterior density of the model parameters using the exact likelihood function, and discuss the use of backcasting to approximate the exact likelihood function in certain cases. We also show how to incorporate indicator variables as additional parameters for use in coefficient selection. The sampling is facilitated through a Metropolis–Hastings algorithm. Graphical techniques based on predictive distributions are used for informal model assessment. The methods are illustrated using two data sets from business and economics. The first example consists of quarterly fixed investment, disposable income, and consumption rates for West Germany, which are known to have correlation and feedback relationships between series. The second example consists of monthly revenue data from seven different geographic areas of IBM. The revenue data exhibit seasonality, strong inter-regional dependence, and feedback relationships between certain regions.© 1997 John Wiley & Sons, Ltd.     

Nowcasting quarterly GDP growth in a monthly coincident indicator model

This paper presents an extension of the Stock and Watson coincident indicator model that allows one to include variables available at different frequencies while taking care of missing observations at any time period. The proposed procedure provides estimates of the unobserved common coincident component, of the unobserved monthly series underlying any included quarterly indicator, and of any missing values in the series. An application to a coincident indicator model for the Portuguese economy is presented. We use monthly indicators from business surveys whose results are published with a very short delay. By using the available data for the monthly indicators and for quarterly real GDP, it becomes possible to produce simultaneously a monthly composite index of coincident indicators and an estimate of the latest quarter real GDP growth well ahead of the release of the first official figures. Copyright © 2005 John Wiley & Son, Ltd.     

Model uncertainty,thick modelling and the predictability of stock returns

Recent financial research has provided evidence on the predictability of asset returns. In this paper we consider the results contained in Pesaran and Timmerman (1995), which provided evidence on predictability of excess returns in the US stock market over the sample 1959–1992. We show that the extension of the sample to the nineties weakens considerably the statistical and economic significance of the predictability of stock returns based on earlier data. We propose an extension of their framework, based on the explicit consideration of model uncertainty under rich parameterizations for the predictive models. We propose a novel methodology to deal with model uncertainty based on ‘thick’ modelling, i.e. on considering a multiplicity of predictive models rather than a single predictive model. We show that portfolio allocations based on a thick modelling strategy systematically outperform thin modelling. Copyright © 2005 John Wiley & Sons, Ltd.