Multi-horizon inflation forecasts using disaggregated data

In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.     

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.     

Does a bank's business model affect its capital and profitability?

We use a data-driven classification of systemically important European banks into business models based on confidential granular supervisory data and investigate whether banks following different models differ with respect to their capitalisation and profitability. Our aim is to locate the banks' business model in a risk-return space. Using an instrumental variables approach, our econometric methodology addresses potential endogeneity issues. Overall, we find that wholesale funded and securities holding banks are positioned on a relatively high risk-return trade-off plane compared with commercial banks. On the other hand, traditional commercial banks earn lower returns with moderate risk.     

Forecasting business and consumer surveys indicators–a time-series models competition

The objective of this article is to compare different time-series methods for the short-run forecasting of Business and Consumer Survey Indicators. We consider all available data taken from the Business and Consumer Survey Indicators for the Euro area between 1985 and 2002. The main results of the forecast competition are offered not only for raw data but we also consider the effects of seasonality and removing outliers on forecast accuracy. In most cases, the univariate autoregressions were not outperformed by the other methods. As for the effect of seasonal adjustment methods and the use of data from which outliers have been removed, we obtain that the use of raw data has little effect on forecast accuracy. The forecasting performance of qualitative indicators is important since enlarging the observed time series of these indicators with forecast intervals may help in interpreting and assessing the implications of the current situation and can be used as an input in quantitative forecast 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.     

An Investigation of the Effects of Data Transformation on Nonlinearity

This paper investigates the effects of data transformation on nonlinearity by means of a simulation analysis based on empirical threshold models for the unemployment rate. Unemployment rate series are particularly suitable because they exhibit a number of interesting features: business cycle asymmetries, persistence, long memory and seasonality. The main finding is that evidence of nonlinearity is not independent of the form in which data are analysed and that most data transformations result in a loss of nonlinearity. This is particularly the case for seasonal adjustment transformations, which remove not only seasonality but also nonlinear features, as shown for the commonly applied Census X12 method.     

Bayesian forecasting of federal funds target rate decisions

In this paper we examine which macroeconomic and financial variables have most predictive ability for the federal funds target rate decisions made by the Federal Open Market Committee (FOMC). We conduct the analysis for the 157 FOMC decisions during the period January 1990–June 2008, using dynamic ordered probit models with a Bayesian endogenous variable selection methodology and real-time data for a set of 33 candidate predictor variables. We find that indicators of economic activity and forward-looking term structure variables, as well as survey measures are most informative from a forecasting perspective. For the full sample period, in-sample probability forecasts achieve a hit rate of 90%. Based on out-of-sample forecasts for the period January 2001–June 2008, 82% of the FOMC decisions are predicted correctly.     

Business cycle amplification with heterogeneous expectations

This paper studies the implications for business cycle dynamics of heterogeneous expectations in a stochastic growth model. The assumption of homogeneous, rational expectations is replaced with a heterogeneous expectations model where a fraction of agents hold rational expectations and the remaining fraction adopt parsimonious forecasting models that are, in equilibrium, optimal within a restricted class. Our approach nests the literature on rational expectations in business cycle models with a recent approach based on adaptive learning. We demonstrate that (i.) heterogeneous expectations can lead to substantial improvement in the internal propagation of equilibrium business cycle models and (ii.) the internal propagation depends on the degree of heterogeneity. A calibrated model with heterogeneity provides a closer fit to business cycle data than its representative agent, rational expectations counterpart.     

Was the deflation of the depression anticipated? An inference using real-time data

Theories that explain the behavior of the economy during the Depression are based on assumptions about agents’ expectations about future price trends. This paper uses an alternative methodological approach which utilizes real-time information from the Depression period to infer whether deflation was anticipated. The information includes the forecasting methodology of that time as well as projections about anticipated output that were obtained from the textual analysis of business statements, converting qualitative to quantitative data. We infer that deflation was not anticipated because agents did not expect economic output to consistently decrease.     

Forecasting macro variables with a Qual VAR business cycle turning point index

One criticism of Vector Autoregression (VAR) forecasting is that macroeconomic variables tend not to behave as linear functions of their own past around business cycle turning points. A large amount of literature therefore focuses on nonlinear forecasting models, such as Markov switching models, which only indirectly capture the relation with turning points. This article investigates a direct approach to using information on turning points from the National Bureau of Economic Research (NBER) chronology to model and forecast macroeconomic data. Our Qual VAR model includes a truncated normal latent business cycle index that is negative during NBER recessions and positive during expansions. We motivate our forecasting exercise by demonstrating that if starting from a linear specification, a truncated normal variable is an omitted variable, then forecasts of the remaining variables will become nonlinear functions of their own past. We apply the Qual VAR model to recursive out-of-sample forecasting and find that the Qual VAR improves on out-of-sample forecasts from a standard VAR.     

Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.     

Forecasting industrial production in the Euro area

The creation of the Euro area has increased the importance of obtaining timely information about short-term changes in the area's real activity. In this paper we propose a number of alternative short term forecasting models, ranging from simple ARIMA models to more complex cointegrated VAR and conditional models, to forecast the index of industrial production in the euro area. A conditional error-correction model in which the aggregate index of industrial production for the area is explained by the US industrial production index and the business confidence index from the European Commission harmonised survey on manufacturing firms achieves the best score in terms of forecasting capacity. First version received: Jan. 2000/Final version received: March 2000     

How do business schools compete in Latin America? Stability and best predictors of success for the AmericaEconomia MBA Ranking

ABSTRACT

The main aim of this paper is to present a longitudinal analysis of the AmericaEconomia MBA Ranking for the period 2005–2014. AmericaEconomia was the first international ranking specifically devoted to Latin American business schools, and with data gathered from this publication, we build a panel to study its stability and the main determinants of a school‘s position in such ranking. We examine the reliability of the ranking, that is whether changes in the relative positions are not just due to white noise, and compare its stability with those of the US and other global rankings. We also empirically determine which are the key quality variables this ranking is promoting for Latin America Business Schools and the evolution of these business schools during the period under study. Unlike previous literature that usually considers dynamic Tobit models for ranking analysis, we put forwards an alternative methodology based on a system GMM estimator with first-differenced instruments. We argue that dynamic Tobit models are appropriate only if you have truncated data about the ranking variable but full data on Business Schools variables.     

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.     

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.     

Modelling industrial new orders

This article models industrial new orders across the European Union (EU) countries for various breakdowns. A common modelling framework exploits soft (business opinion surveys) as well as hard data (industrial turnover). The estimates show for about 200 cases that the model determinants significantly help in explaining new orders' monthly growth rates. An alternative estimation method, different model specifications and out-of-sample and real-time forecasting all show that the model results are robust. We present real-time outcomes of a European Central Bank (ECB) indicator on industrial new orders at an aggregated euro area level. This indicator is largely based on national new orders data and on estimates yielded by the model for those countries that no longer report new orders at the national level. Finally, we demonstrate the leading content of the ECB indicator on euro area new orders for industrial production.     

Box-Jenkins Methoden — Alternative Verfahren zur Prognose ökonomischer Zeitreihen

Summary In this paper we try to clarify whether the use ofBox-Jenkins methods would have improved the forecasting performance in Austria during the recession of 1975. For this purpose we estimate ARIMA models for gross national product, private consumption, investment in plant and equipment, and inventory investment. We then compare the forecasts derived from these models with the results of more convential forecasting techniques. It can not be expected that Box-Jenkins methods predict a business cycle turning point. But, as soon as the recession was under way Box-Jenkins methods were faster in adapting to the new situation than conventional forecasting techniques. We found that the accuracy of Box-Jenkins predictions depends to a large extent on the length of the forecasting horizon. Our results suggest that the forecasting horizon should not exceed one year. All in all, Box-Jenkins methods applied together with the forecasting techniques already in use could further improve the forecasting performance.     

Real-time forecasting US GDP from small-scale factor models

We show that the single-index dynamic factor model developed by Aruoba and Diebold (Am Econ Rev, 100:20–24, 2010) to construct an index of the US business cycle conditions is also very useful to forecast US GDP growth in real time. In addition, we adapt the model to include survey data and financial indicators. We find that our extension is unequivocally the preferred alternative to compute backcasts. In nowcasting and forecasting, our model is able to forecast growth as well as AD and better than several baseline alternatives. Finally, we show that our extension could also be used to infer the US business cycles very precisely.     

Economic forecasting with evolved confidence indicators

We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables – both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.     

Innovation forecasting

Technological forecasting is premised on a certain orderliness of the innovation process. Myriad studies of technological substitution, diffusion, and transfer processes have yielded conceptual models of what matters for successful innovation, but most technological forecasts key on limited empirical measures quite divorced from those innovation process models. We glean a number of concepts from various innovation models, then present an array of bibliometric measures that offer the promise of operationalizing these concepts. Judicious combination of such bibliometrics with other forms of evidence offers an enriched form of technological forecasting we call “innovation forecasting.” This provides a good means to combine technological trends, mapping of technological interdependencies, and competitive intelligence to produce a viable forecast. We illustrate by assessing prospects for ceramic engine technologies.