A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation

We use a boosting algorithm to forecast the returns of gold and silver prices. We then study the implications of using different information criteria to terminate the boosting algorithm in terms of the statistical and economic performance of a forecasting model. Our findings demonstrate that information criteria that select parsimonious forecasting models perform better in statistical terms than information criteria that select relatively complex forecasting models, but this good performance does not necessarily survive an economic performance evaluation.     

Think national,forecast local: a case study of 71 German urban housing markets

In this article, we examine whether the local indicators are able to predict the city-level housing prices and rents better than national indicators. For this purpose, we assess the forecasting ability of 126 indicators and 21 types of forecast combinations using a sample of 71 large German cities. There are several predictors that are especially useful, namely price-to-rent ratios, national-level business confidence, and consumer surveys. We also find that combinations of individual forecasts are among the top forecasting models. On average, the forecast improvements attain about 20%, measured by a reduction in root mean square error, compared to the naive models.     

Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey

A widespread method for forecasting economic macro level parameters such as GDP growth rates is survey-based indicators that contain early information in contrast to official data. But surveys are commonly affected by nonresponding units, which can cause biased results. Many papers have examined the effect of nonresponse in individual or household surveys, but less is known in the case of business surveys. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications at the macro level. Finally, we show that the differences between the original and imputed indicators do not lead to substantial changes in the interpretation and the forecasting performance of the indicators.     

Moments of cross-sectional stock market returns and the German business cycle

Based on monthly data covering the period from 1987 to 2021, we analyse whether cross-sectional moments of stock market returns may provide information about the future position of the German business cycle. We apply in-sample forecasting regressions with and without leading indicators as control variables, pseudo-out-of-sample exercises, autoregressive distributed lag models, and impulse-response functions estimated by local projections. We find in-sample predictive power of the first and third cross-section moments for the future growth of industrial production, even if one controls for well-established leading indicators for the German business cycle. Out-of-sample tests show that these variables reduce the relative mean squared error compared with benchmark models. We do not find a long-run relation between the moment series and industrial production. The dynamic response of industrial production to a shock on the cross-section moments is in line with the other results.     

The impact of trading volume,number of trades and overnight returns on forecasting the daily realized range

Intraday data of 26 German stocks are used to investigate whether the information contained in trading volume and number of trades as well as in various specifications of overnight returns can improve one-step-ahead volatility forecasts. For this purpose, a HAR model of the realized range adjusted for discrete trading is augmented by each of these variables and compared with the model's default form. The results show that the considered liquidity measures lead to very modest improvements in forecasting performance. The overnight returns exhibit some in-sample forecasting power. However, the accuracy improvement of out-of-sample forecasts is unequivocally non-significant.     

Modeling Expectation Formation Involving Several Sources of Information

Abstract. Experimental studies of expectation formation of subjects are predominantly limited to the prediction of one single time series despite the practical relevance of expectations in situations with multiple sources of information. In this paper, we report on an experiment in which subjects are given time series (indicators) as additional information for the judgemental forecast of a stationary time series. The quality and the number of these indicators are varied in three versions of a forecasting experiment. We explore the effects on forecasting accuracy and we test the average forecasts of the subjects for consistency with the rational expectations hypothesis. A simple heuristic is presented that explains the average forecasting behavior better than the rational expectations if indicators are presented to the subjects. It is demonstrated by a simulation study that this result is representative for the considered stationary stochastic processes.     

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.     

Consumption,wealth and business cycles in Germany

This paper studies the long-run relationship between consumption, asset wealth and income—the consumption–wealth ratio—based on German data from 1980 to 2003. We find that departures from this long-run relationship mainly predict adjustments in income. The German consumption–wealth ratio also contains considerable forecasting power for a range of business cycle indicators, including the unemployment rate. This finding is in contrast to earlier studies for some of the Anglo-Saxon economies that have shown that the consumption–wealth ratio reverts to its long-run mean mainly through subsequent adjustments in asset prices. While the German consumption wealth ratio contains little information about future changes in German asset prices, we report that the U.S. consumption–wealth ratio has considerable forecasting power for the German stock market. One explanation of these findings is that in Germany—due to structural differences in the financial and pension systems—the share of publicly traded equity in aggregate household wealth is much smaller than in the Anglo-Saxon countries. We discuss the implications of our results for the measurement of a potential wealth effect on consumption. The views expressed in this paper are those of the authors and do not reflect the position of the Deutsche Bundesbank. We gratefully acknowledge comments and suggestions from an anonymous referee as well as from Heinz Herrmann, Helmut Lütkepohl, the editor, Baldev Raj, Burkhard Raunig, Monika Schnitzer, Harald Uhlig and Christian Upper. We also benefitted from comments by seminar participants at the ECB, the Deutsche Bundesbank, the CESifo Macro, Money and International Finance Area Conference 2005, the EEA 2005 annual congress and at the 2005 IAEA Meetings. Last but not least, we would like to thank Mark Weth for very useful information concerning the construction of the financial wealth data. Hoffmann’s work on this paper is also part of the project The International Allocation of Risk funded by Deutsche Forschungsgemeinschaft in the framework of SFB 475. Responsibility for any remaining errors and shortcomings is entirely our own.     

Structural changes of economics in poland and New German Countries

The process of transition of the Polish economy from a centrally planned to a market economy started in the 1980s by a series of attempts to introduce economic reforms. By 1990–95, more important changes took place. The political and economic changes in the German Democratic Republic began near the end of 1989. The first step was the monetary union of both German states in June 1990. Although German unification (in October 1990) took place relatively later than the Polish reforms, the East German changes were greater. This paper discusses various macroeconomic indicators in both regions for the period 1990–95. These indicators are the dynamics and structure of gross domestic product, population number and employment, unemployment rate, and consumption structure.     

Accounting for Sustainable Development over the Long‐Run: Lessons from Germany

We construct long‐run sustainability indicators based on changes in Comprehensive Wealth – which we refer to as Genuine Savings (GS) – for Germany over the period 1850–2000. We find that German sustainability indicators are positive for the most part, although they are negative during and after the two World Wars and also the Great Depression. We also test the relationship between these wealth changes and a number of measures of well‐being over the long‐run: changes in consumption as well as changes in average height and infant mortality rates. We find a positive relationship between GS and our well‐being indicators over different time horizons, however, the relationship breaks down during WWII. We also test if the GS/Comprehensive Wealth framework is able to cope with massive disinvestment at the end of the Second World War due to war‐related destructions and dismantlement. We find that negative rates of GS were by and large avoided due to the accumulation of technology and growth‐friendly institutions. We demonstrate the importance of broader measures of capital, including measures of technological progress, and its role in the process of economic development; and the limits of conventional measures of investment to understand why future German consumption did not collapse.     

Measures of underlying inflation in the euro area: assessment and role for informing monetary policy

The paper evaluates the 24-month-ahead inflation forecasting performance of various indicators of underlying inflation and structural models. Measures derived using the generalized dynamic factor model (GDFM) overperform other measures over the monetary policy horizon and are leading indicators of headline inflation. Trimmed means, although weaker than GDFM indicators, have good forecasting performance, while indicators by permanent exclusion underperform but provide useful information about short-term dynamics. The forecasting performance of theoretically-founded models that relate monetary aggregates, the output gap, and inflation improves with the time horizon but generally falls short of that of the GDFM. A composite measure of underlying inflation, derived by averaging the statistical indicators and the model-based estimates, improves forecast accuracy by eliminating bias and offers valuable insight about the distribution of risks.     

Assessing the predictive power of financial spreads in the euro area: does parameters instability matter?

In the empirical literature there is wide consensus that financial spreads cannot constitute a broadly based assessment on future output growth and inflation because the bivariate estimated regressions are not stable over time and lead to relatively poor out-of-sample forecasting performance (e.g. J Econ Liter 41:788–829, 2003). This conclusion arised for the USA, as well as for several European countries. In this paper we check whether the marginal predictive content of some financial spreads (the slope of the yield curve, the reverse yield gap and the credit spread) for macroeconomic forecasting in the euro area can be recovered using techniques taking into account potential parameters instability. We set up a quarterly Bayesian vector autoregression model with time-varying coefficients, comprising both target variables, as well as other monetary policy indicators, to serve as a benchmark. Then, the properties of the spreads as leading indicators are assessed by augmenting this benchmark BVAR with the spreads, one at a time. We find time variation of the coefficients to be a relevant issue in our model, especially for forecasting output growth, but financial spreads continue to have no or negligible marginal predictive content for both output growth and inflation. Overall, our results confirm that there is no ready-to-use financial spread that can replace an encompassing multivariate model for the prediction of target variables in the euro area.      

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.     

Trade and the impact of innovations and standards: the case of Germany and the UK

The effects of patents as indicators for innovations and standards on German trade performance are analysed in general and German–British trade in detail. The latter analysis goes a step further than the approach of Swann et al. () and is based on a broader and more detailed database. The results show that Germany's export performance can primarily be explained by its innovative capacity and only to a small extent by its strength in standards. Furthermore, the results underscore the common view of the trade-fostering effect of international standards, while ‘idiosyncratic' standards have ambiguous effects on exports.     

Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data

In this paper, we develop a methodology for forecasting key macroeconomic indicators, based on business survey data. We estimate a large set of models, using an autoregressive specification, with regressors selected from business and household survey data. Our methodology is based on the Bayesian averaging of classical estimates method. Additionally, we examine the impact of deterministic and stochastic seasonality of the business survey time series on the outcome of the forecasting process. We propose an intuitive procedure for incorporating both types of seasonality into the forecasting process. After estimating the specified models, we check the accuracy of the forecasts.     

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.     

The information content of the divisia monetary aggregates in forecasting inflation in the euro area

The paper investigates the performance of a set of monetary indicators, based on the Divisia money constructed for the euro area, on forecasting euro area inflation. The paper first briefly discusses on the relative information contents of the Divisia aggregates and the simple sum aggregates. The forecasting performance of the former is then examined by means of simulated out-of-sample forecasting. In addition to examining the information contents of the Divisia aggregate constructed for M3 money, the study also examines the performance of the Divisia M1 money to gain evidence on the relative performance between the broad and narrow Divisia monetary aggregates. According to the results, only some of the monetary indicators considered can significantly improve the univariate inflation forecasts. The Divisia M3 money based monetary indicators turned out to perform better than their Divisia M1 based counterparts. The result contradicts some previous evidence on the optimal level on monetary aggregation in the context of broad versus narrow money.      

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 house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.