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.     

Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation

The growing affluence of the East and Southeast Asian economies has come about through a substantial increase in their economic links with the rest of the world, the OECD economies in particular. Econometric studies that try to quantify these links face a severe shortage of high‐frequency time series data for China and the group of ASEAN4 (Indonesia, Malaysia, Philippines and Thailand). In this paper we provide quarterly real GDP estimates for these countries derived by applying the Chow–Lin related series technique to annual real GDP series. The quality of the disaggregated series is evaluated through a number of indirect methods. Some potential problems of using readily available univariate disaggregation techniques are also highlighted. Copyright © 2004 John Wiley & Sons, Ltd.     

Integrating Quarterly Data into a Dynamic Factor Model of US Monthly GDP

This paper develops and estimates a dynamic factor model in which estimates for unobserved monthly US Gross Domestic Product (GDP) are consistent with observed quarterly data. In contrast to existing approaches, the quarterly averages of our monthly estimates are exactly equal to the Bureau of Economic Analysis (BEA) quarterly estimates. The relationship between our monthly estimates and the quarterly data is therefore the same as the relationship between quarterly and annual data. The study makes use of Bayesian Markov chain Monte Carlo and data augmentation techniques to simulate values for the logarithms on monthly US GDP. The imposition of the exact linear quarterly constraint produces a non‐standard distribution, necessitating the implementation of a Metropolis simulation step in the estimation. Our methodology can be easily generalized to cases where the variable of interest is monthly GDP and in such a way that the final results incorporate the statistical uncertainty associated with the monthly GDP estimates. We provide an example by incorporating our monthly estimates into a Markov switching model of the US business cycle. Copyright © 2016 John Wiley & Sons, Ltd.     

Monthly data and short‐term forecasting: an assessment of monthly data in a VAR model

Use of monthly data for economic forecasting purposes is typically constrained by the absence of monthly estimates of GDP. Such data can be interpolated but are then prone to measurement error. However, the variance matrix of the measurement errors is typically known. We present a technique for estimating a VAR on monthly data, making use of interpolated estimates of GDP and correcting for the impact of measurement error. We then address the question how to establish whether the model estimated from the interpolated monthly data contains information absent from the analogous quarterly VAR. The techniques are illustrated using a bivariate VAR modelling GDP growth and inflation. It is found that, using inflation data adjusted to remove seasonal effects and the impacts of changes to indirect taxes, the monthly model has little to add to a quarterly model when projecting one quarter ahead. However, the monthly model has an important role to play in building up a picture of the current quarter once one or two months' hard data becomes available. Copyright © 1999 John Wiley & Sons, Ltd.     

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.     

On estimating contemporaneous quarterly regional GDP

Subnational regional jurisdictions rarely have at their disposal a reasonable array of timely statistics to monitor their economic condition. In light of this, we develop a procedure that simultaneously estimates a quarterly time series for all regions of a country based upon quarterly national and annual regional data. While other such techniques exist, we suggest a temporal error structure that eliminates possible spurious jumps. Using our approach, regional analysts should now be able to distribute national growth among regions as soon as quarterly national figures are released. In a Spanish application, we detail some practicalities of the process and show that our proposal produces better estimates than the uniregional methods often used. Copyright © 2007 John Wiley & Sons. Ltd.     

Forecasting the Italian industrial production index in real time

Daily electricity consumption data, available almost in real time, can be used in Italy to estimate the level of industrial production in any given month before the month is over. We present a number of procedures that do this using electricity consumption in the first 14 days of the month. (This is an extension of a previous model that used monthly electricity data.) We show that, with a number of adjustments, a model using half-monthly electricity data generates acceptable estimates of the monthly production index. More precisely, these estimates are more accurate than univariate forecasts but less accurate than estimates based on monthly electricity data. A further improvement can be obtained by combining ‘half-monthly’ electricity-based estimates with univariate forecasts. We also present quarterly estimates and discuss confidence intervals for various types of forecasts.     

Monthly Employment Indicators of the Euro Area and Larger Member States: Real‐Time Analysis of Indirect Estimates

The paper presents a comparative real‐time analysis of alternative indirect estimates relative to monthly euro area employment. In the experiment quarterly employment is temporally disaggregated using monthly unemployment as related series. The strategies under comparison make use of the contribution of sectoral data of the euro area and its six larger member states. The comparison is carried out among univariate temporal disaggregations of the Chow and Lin type and multivariate structural time series models of small and medium size. Specifications in logarithms are also systematically assessed. All multivariate set‐ups, up to 49 series modelled simultaneously, are estimated via the EM algorithm. Main conclusions are that mean revision errors of disaggregated estimates are overall small, a gain is obtained when the model strategy takes into account the information by both sector and member state and that larger multivariate set‐ups perform very well, with several advantages with respect to simpler models.Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Short‐term forecasting of GDP using large datasets: a pseudo real‐time forecast evaluation exercise

This paper performs a large‐scale forecast evaluation exercise to assess the performance of different models for the short‐term forecasting of GDP, resorting to large datasets from ten European countries. Several versions of factor models are considered and cross‐country evidence is provided. The forecasting exercise is performed in a simulated real‐time context, which takes account of publication lags in the individual series. In general, we find that factor models perform best and models that exploit monthly information outperform models that use purely quarterly data. However, the improvement over the simpler, quarterly models remains contained. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Ex-post and ex-ante prediction of unobserved economic time series: a case study

In several countries, some macro-economic variables are not observed frequently (e.g. quarterly) and economic authorities need estimates of these high-frequency figures to make econometric analyses or to follow closely the country's economic growth. Two problems are involved in this context. The first is to make these estimates after observing low-frequency values and some related indicators, and the second is to obtain predictions using just the observed indicators, i.e. before observing a new low-frequency figure. This paper gives a new optimal solution to the first problem, and solves the second using a recursive optimal approach. In the second situation, additionally, statistical tests are developed for detecting structural changes at current periods in the macro-economic variable involved. © 1998 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 real‐time data allowing for data revisions

A modeling approach to real‐time forecasting that allows for data revisions is shown. In this approach, an observed time series is decomposed into stochastic trend, data revision, and observation noise in real time. It is assumed that the stochastic trend is defined such that its first difference is specified as an AR model, and that the data revision, obtained only for the latest part of the time series, is also specified as an AR model. The proposed method is applicable to the data set with one vintage. Empirical applications to real‐time forecasting of quarterly time series of US real GDP and its eight components are shown to illustrate the usefulness of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

Disaggregation of annual time-series data to quarterly figures: A comparative study

In this paper we consider some of the prominent methods that are available in the literature for the problem of disaggregating annual time-series data to quarterly figures. The procedures are briefly described and illustrated through a real data set. The performances of the methods are compared in a Monte Carlo study. The results indicate that the complicated model-based procedure is usually superior to other non-model-based alternatives in the large sample situations. Based on the simulation results, we make some recommendations regarding the use of these methods.     

Periodically integrated subset autoregressions for dutch industrial production and money stock

The univariate quarterly Dutch series of industrial production and money stock are both modelled with a periodically integrated subset autoregression (PISA). This model for a non-stationary series allows the lag orders, the values of the parameters and the cyclical patterns to vary over the seasons. The PISA models are found by applying a general-to-simple specification strategy, which deals with non-stationarity and periodicity simultaneously. It is found that the two series show a common asymmetric cyclical behaviour. This paper further proposes a test for periodicity in the errors, with which it is argued that a non-periodic model for the industrial production and money stock is misspecified and that seasonal adjustment does not remove periodicity in the autocorrelation function.     

The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries

The delayed release of the National Account data for GDP is an impediment to the early understanding of the economic situation. In the short run, this information gap may be at least partially eliminated by bridge models (BM) which exploit the information content of timely updated monthly indicators. In this paper we examine the forecasting ability of BM for GDP growth in the G7 countries and compare their performance to that of univariate and multivariate statistical benchmark models. We run four alternative one‐quarter‐ahead forecasting experiments to assess BM performance in situations as close as possible to the actual forecasting activity. BM are estimated for GDP both for single countries (USA, Japan, Germany, France, UK, Italy and Canada), and area‐wide (G7, European Union, and Euro area). BM forecasting ability is always superior to that of benchmark models, provided that at least some monthly indicator data are available over the forecasting horizon. Copyright © 2007 John Wiley & Sons, Ltd.     

Monetary Aggregates to Improve Early Output Gap Estimates in the Euro Area: An Empirical Assessment

Output gap estimates at the current edge are subject to severe revisions. This study analyzes whether monetary aggregates can be used to improve the reliability of early output gap estimates as proposed by several theoretical models. A real‐time experiment shows that real M1 can improve output gap estimates for euro area data. For many periods the cyclical component of real M1 shows good results, while a forecasting strategy based on projecting GDP series seems to be more robust and provides superior results during the Great Recession. Broader monetary aggregates provide no superior information for output gap estimates. Copyright © 2015 John Wiley & Sons, Ltd.     

Municipal budget forecasting with multivariate ARMA models

In this paper multivariate ARMA models are applied to the problem of forecasting city budget variables. Unlike univariate time-series methods, multivariate models can use relationships among budget variables as well as relationships with economic and demographic indicators. Although available budget series are shorter than what is usually believed necessary for multivariate ARMA modelling, the forecasts seem to be of higher quality than those from univariate models.