PMIs: Reliable indicators for exports?

Foreign economic activity is a major determinant of export developments. However, foreign GDP figures are published too late to be useful for short‐term forecasting. This paper presents a number of indicators based on the widely available PMI surveys that provide very early signals of foreign activity. Using MIDAS models we analyze the in‐ and out‐of‐sample performance of these and related indicators for two very trade‐exposed countries (Germany and Switzerland). We find that the monthly indicators based on foreign PMIs are strongly correlated with quarterly export growth. The forecast comparison shows that PMI‐based indicators perform very well relative to other benchmark models.     

Forecasting the aggregate stock market volatility in a data-rich world

ABSTRACT

In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing literature, we determine several candidate predictors that have 22 technical indicators and 14 macroeconomic and financial variables. Our out-of-sample results reveal several noteworthy findings. First, few macroeconomic and financial variables and most of technical indicators have superior performance relative to the benchmark model. Second, combination forecasts are able to significantly beat the benchmark and some signal predictors Third, the lasso and elastic models with all predictors can generate more accurate forecasts than the benchmark and some other predictors in both the statistical and economic sense. Fourth, the lasso and elastic models exhibit higher forecast accuracy during periods of expansions and recessions. Finally, our findings are robust to several tests, such as different forecasting windows, forecasting models, and forecasting evaluations.     

Forecasting GDP at the Regional Level with Many Predictors

In this study, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden‐Württemberg) and Eastern Germany. We overcome the problem of a ‘data‐poor environment’ at the sub‐national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single‐indicator, multi‐indicator, pooled and factor forecasts in a ‘pseudo‐real‐time’ setting. Our results show that we can significantly increase forecast accuracy compared with an autoregressive benchmark model, both for short‐ and long‐term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.     

Combination forecasts of tourism demand with machine learning models

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: support vector regression, Gaussian process regression and neural network models. We use an autoregressive moving average model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.     

Forecasting monthly industrial production in real-time: from single equations to factor-based models

The aim of this paper is to analyze the performance of alternative forecasting methods to predict the index of industrial production in Italy from 1 to 3 months ahead. We use twelve different models, from simple ARIMA to dynamic factor models exploiting the timely information of up to 110 short-term indicators, both qualitative and quantitative. This allows to assess the relevance for the forecasting practice of alternative combinations of types of data (real-time and latest available), estimation methods and periods. Out-of-sample predictive ability tests stress the relevance of more indicators in disaggregate models over sample periods covering a complete business cycle (about 7 years in Italy). Our findings downgrade the emphasis on both the estimation method and data revision issues. In line with the classical “average puzzle”, the use of simple averages of alternative forecasts often improves the predictive ability of their single components, mainly over short horizons. Finally, selected indicators and factor-based models always perform significantly better than ARIMA models, suggesting that the short-run indicator signal always dominates the noise component. On this regard, selected indicators models can further increase the amount of signal extracted to improve up to 30–40% the short-run predictive ability of factor-based models and to forecast-encompass them.     

Forecasting the Australian economy with DSGE and BVAR models

Reflecting the importance of commodities for the Australian economy, we construct a dynamic stochastic general equilibrium (DSGE) model of the Australian economy with a commodity sector. We assess whether its forecasts can be improved by using it as a prior for an empirical Bayesian vector autoregression (BVAR). We find that the forecasts from the BVAR tend to be more accurate than those from the DSGE model. Nevertheless, for output growth these forecasts do not outperform benchmark models, such as a small open economy BVAR estimated using the standard priors for forecasting. A Bayesian factor augmented vector autoregression produces the most accurate near-term inflation forecasts.     

Forecasting stock market volatility: The role of technical variables

Accurate volatility forecasts are required by both market participants and policy makers. In this paper, we forecast stock return volatility by using a wide range of technical indicators constructed based on the past behavior of stock price, volatility and trading volume. Our out-of-sample results indicate that the incorporation of technical variables in the autoregression benchmark can produce significantly more accurate volatility forecasts. The forecasting performance of the combination of technical indicators is further compared with that of the popular economic indicators. Technical variables perform better than economic variables when the economy is an expansion, while the economic variables generate more accurate forecasts when the economy belongs a recession. These two types of variables provide complementary information over the business cycle. We obtain more reliable forecasts by combining all economic and technical information together than by combining either type of information alone.     

Forecasting GDP all over the world using leading indicators based on comprehensive survey data

Comprehensive and international comparable leading indicators across countries and continents are rare. In this paper, we use a free and instantaneous available source of leading indicators, the ifo World Economic Survey (WES), to forecast growth of Gross Domestic Product (GDP) in 44 countries and three country aggregates separately. We come up with three major results. First, for more than three-fourths of the countries or country-aggregates in our sample, a model containing one of the major WES indicators produces on average lower forecast errors compared to a benchmark model. Second, the most important WES indicators are either the economic climate or the expectations on future economic development for the next six months. And third, adding the WES indicators of the main trading partners leads to a further increase in forecast accuracy in more than 50% of the countries. It seems therefore reasonable to incorporate economic signals from the domestic economy’s main trading partners.     

Forecasting with large datasets: compressing information before,during or after the estimation?

We study the forecasting performance of three alternative large data forecasting approaches. These three approaches handle the dimensionality problem evoked by a large dataset by compressing its informational content, yet at different stages of the forecasting process. We consider different factor models, a large Bayesian vector autoregression and model averaging techniques, where the data compression takes place before, during and after the estimation of the respective forecasting models. We use a quarterly dataset for Germany that consists of 123 variables and find that overall the large Bayesian vector autoregression and the Bayesian factor augmented vector autoregression provide the most precise forecasts for a set of 11 core macroeconomic variables. Further, we find that the performance of these two models is very robust to the exact specification of the forecasting model.     

Do equity index industry groups improve forecasts of inflation and production? A US analysis

This study develops a new financial market indicator, which may be a useful addition to analysing real activity in the US. By taking the ratio of the price return of equity industry groups of the S&P 500 over a benchmark industry group, in this case taken to be the Utilities industry group, an indicator is created which represents the price return performance specific to each individual industry. We then perform recursive pseudo out-of-sample bivariate forecasts of future changes in the Industrial Production Index (IPI) and the Consumer Price Index (CPI) at 3-month, 6-month and 12-month horizons using each of the indicators and compare results against an AR forecast. The results of the bivariate forecasts using a number of the indicators produce better forecasts of changes in the IPI and are also significant for causality, both for the full sample period and when tested recursively. Bivariate forecasts of changes to the CPI, however, do not improve upon the AR forecasts.     

Short-term inflation forecasting models for Turkey and a forecast combination analysis

In this paper, we produce short term forecasts for the inflation in Turkey, using a large number of econometric models. In particular, we employ univariate models, decomposition based approaches (both in frequency and time domain), a Phillips curve motivated time varying parameter model, a suite of VAR and Bayesian VAR models and dynamic factor models. Our findings suggest that the models which incorporate more economic information outperform the benchmark random walk, and the relative performance of forecasts are on average 30% better for the first two quarters ahead. We further combine our forecasts by means of several weighting schemes. Results reveal that, the forecast combination leads to a reduction in forecast error compared to most of the models, although some of the individual models perform alike in certain horizons.     

Monetary Policy and Forecasts for Real GDP Growth: An Empirical Investigation for the Federal Republic of Germany

Using quarterly data for the Federal Republic of Germany, we generate four-quarter-ahead forecasts for real GDP growth. Throughout the 1970s and 1980s, other monetary indicators like real M1 or short-run interest rates clearly outperform forecasts which are based on interest rate spreads. This holds for within as well as for ex-post predictions. The same holds for the development after 1992. Moreover, it is shown that simple forecasts based on M1 or on short-run interest rates outperform the common biannual GNP forecasts of the group of German economic research institutes.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

Measuring the output gap in Switzerland with linear opinion pools

We use the recently proposed linear opinion pool methodology of Garratt et al. (2014) to construct real-time output gap estimates for Switzerland over the out-of-sample period from 2003:Q1 to 2015:Q4. The model space consists of a large number of bivariate VAR specifications for the output gap and inflation, with each VAR specification using a different estimate of the output gap, lag order, and structural break information. We find that the linear opinion pool performs rather poorly. Real-time estimates of the output gap are no more accurate than those from some simple benchmark models, no more robust to ex post revisions than the real-time estimates of the individual univariate output gaps, and do not produce more accurate forecasts of inflation. The key driver of ‘good’ forecast performance is structural break information. Once the same structural break information is conditioned upon in all prediction models, the gain from averaging over many different pools of models that utilize various output gap estimates or lag structures in the VAR specification is of negligible magnitude.     

Realized volatility models and alternative Value-at-Risk prediction strategies

We assess the Value-at-Risk (VaR) forecasting performance of recently proposed realized volatility (RV) models combined with alternative parametric and semi-parametric quantile estimation methods. A benchmark inter-daily GJR-GARCH model is also employed. Based on four asset classes, i.e. equity, FOREX, fixed income and commodity, and a turbulent six year out-of-sample period (2007–2013), we find that statistical accuracy and regulatory compliance is essentially improved when we use quantile methods which account for the fat tails and the asymmetry of the innovations distribution. In particular, empirical analysis gives evidence in favor of the skewed student distribution and the Extreme Value Theory (EVT) method. Nonetheless, efficiency of VaR estimates, as defined by the minimization of Basel II capital requirements and its opportunity costs, is reassured only with the use of realized volatility models. Overall, empirical evidence support the use of an asymmetric HAR realized volatility model coupled with the EVT method since it produces statistically accurate VaR forecasts which comply with Basel II accuracy mandates and allows for more efficient capital allocations.     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.     

Do corporate executives have accurate predictions for the economy? A directional analysis

Although many studies on the directional accuracy of forecasts by international organizations and professional forecasters have been scrutinized, little attention has been paid to forecasts by business leaders. In order to address this gap, we use directional tests to investigate whether forecasts of Gross Domestic Product by corporate executives are valuable to their users. Our findings indicate that all the forecasts with forecast horizons from 1 to 14 months are valuable, whereas established literature indicates that longer-term forecasts tend not to be valuable. This suggests that corporate executives are concerned with and focus on longer-term economic environments and can therefore serve as an important resource for policymakers. However, some of the useful forecasts with real-time data, in particular those in the Tankan survey, are not useful with historical data.     

‘Ex-ante’ Taylor rules – Newly discovered evidence from the G7 countries

This paper addresses the question of whether financial market participants apply the framework of Taylor-type rules in their forecasts for the G7 countries. To this end, we use the Consensus Economic Forecast poll providing us a unique data set of inflation rate, interest rate and growth rate forecasts for the time period 1989-2008. We provide empirical evidence that financial market participants incorporate Taylor-type rules in their forecasts. Thus, the paper uses ex-ante data for the estimation of Taylor rules. This is a new approach, because so far only ex-post (revised) or real-time data have been applied.     

Environmental performance,corruption and economic growth: global evidence using a new data set

This study investigates the relationship between environmental performance, corruption and economic growth using panel data of 87 countries covering the period from 2002 to 2012. The Environmental Performance Index is used for the first time to evaluate the environmental quality on economic growth. By employing both ‘static’ and ‘dynamic’ panel models, we find that environmental performance is positively related to economic growth and is more significant in non-Organization for Economic Cooperation Development (OECD) countries. Moreover, when corruption is incorporated, the empirical estimation results indicate that although lower corruption helps economic growth in non-OECD countries, the negative coefficients of the three interactive terms show that the positive effect of environment performance on economic growth will drop, while greater environmental performance combined with natural resource abundance inevitably leads to inefficient bureaucracies and hence disadvantageous economic growth. As a result, policymakers in non-OECD countries should carefully ensure better government quality when they exhibit strong environmental performance so as to avoid any disadvantageous impact upon economic growth.     

For how long do IMF forecasts of world economic growth stay up-to-date?

This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.