On economic evaluation of directional forecasts

It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of the upward or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework for assessing the economic forecast value when loss functions (or success measures) are properly formulated to account for the realized signs and realized magnitudes of directional movements. We discuss a general approach to (directional) forecast evaluation which is based on the loss function proposed by Granger, Pesaran and Skouras. It is simple to implement and provides an economically interpretable loss/success functional framework. We show that, in addition, this loss function is more robust to outlying forecasts than traditional loss functions. As such, the measure of the directional forecast value is a readily available complement to the commonly used squared error loss criterion.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.     

Evaluating the conditionality of judgmental forecasts

We propose a framework for evaluating the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are incorporated faithfully into the remainder of the forecast. We consider whether the Greenbook, Blue Chip survey and Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest rate projections in these forecasts appear to be incorporated efficiently into the forecasts of other economic variables.     

Testing the predictive accuracy of COVID-19 forecasts

We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results. First, at the short horizon (1 week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3 and 4 week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.     

Diagnostics for evaluating the value and rationality of economic forecasts

A number of studies have sought to determine whether economic forecasts had predictive value. These analyses used a single statistical methodology based on the independence of the actual and predicted changes. This paper questions whether the observed results are robust if alternative statistical methodologies are used to analyze this question. Procedures suggested by Cumby and Modest as well as rationality tests were applied to two data sets. Sometimes the conclusions differ depending on the procedures that are used. The results yield a guideline for the diagnostics that should be employed in testing for the value of economic forecasts.     

Optimal and robust combination of forecasts via constrained optimization and shrinkage

We introduce various methods that combine forecasts using constrained optimization with penalty. A non-negativity constraint is imposed on the weights, and several penalties are considered, taking the form of a divergence from a reference combination scheme. In contrast with most of the existing approaches, our framework performs forecast selection and combination in one step, allowing for potentially sparse combining schemes. Moreover, by exploiting the analogy between forecasts combination and portfolio optimization, we provide the analytical expression of the optimal penalty strength when penalizing with the L2-divergence from the equally-weighted scheme. An extensive simulation study and two empirical applications allow us to investigate the impact of the divergence function, the reference scheme, and the non-negativity constraint on the predictive performance. Our results suggest that the proposed models outperform those considered in previous studies.     

Realized volatility forecast with the Bayesian random compressed multivariate HAR model

We develop a Bayesian random compressed multivariate heterogeneous autoregressive (BRC-MHAR) model to forecast the realized covariance matrices of stock returns. The proposed model randomly compresses the predictors and reduces the number of parameters. We also construct several competing multivariate volatility models with the alternative shrinkage methods to compress the parameter’s dimensions. We compare the forecast performances of the proposed models with the competing models based on both statistical and economic evaluations. The results of statistical evaluation suggest that the BRC-MHAR models have the better forecast precision than the competing models for the short-term horizon. The results of economic evaluation suggest that the BRC-MHAR models are superior to the competing models in terms of the average return, the Shape ratio and the economic value.     

A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.     

Assessing the accuracy of electricity production forecasts in developing countries

This study assesses the accuracy of time series econometric methods for forecasting electricity production in developing countries. An analysis of the historical time series for 106 developing countries over the period 1960–2012 demonstrates that econometric forecasts are highly accurate for the majority of these countries. These forecasts have much smaller errors than the predictions of simple heuristic models, which assume that electricity production grows at an exogenous rate or is proportional to the real GDP growth. However, the quality of the forecasts diminishes for the countries and regions, where rapid economic and structural transformation makes it difficult to establish stable historical production trends.     

Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts

We construct a DSGE-VAR model for competing head to head with the long history of published forecasts of the Reserve Bank of New Zealand. We also construct a Bayesian VAR model with a Minnesota prior for forecast comparison. The DSGE-VAR model combines a structural DSGE model with a statistical VAR model based on the in-sample fit over the majority of New Zealand’s inflation-targeting period. We evaluate the real-time out-of-sample forecasting performance of the DSGE-VAR model, and show that the forecasts from the DSGE-VAR are competitive with the Reserve Bank of New Zealand’s published, judgmentally-adjusted forecasts. The Bayesian VAR model with a Minnesota prior also provides a competitive forecasting performance, and generally, with a few exceptions, out-performs both the DSGE-VAR and the Reserve Bank’s own forecasts.     

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

A new method to assess the degree of information rigidity using fixed-event forecasts

We propose a new method to explore the information content of fixed-event forecasts and estimate structural parameters that are keys to sticky and noisy information models. Estimation follows a regression-based framework in which estimated coefficients map one-to-one with parameters that measure the degree of information rigidity. The statistical characterization of regression errors explores the laws that govern expectation formation under sticky and noisy information, that is, they are coherent with the theory. This strategy is still unexplored in the literature and potentially enhances the reliability of inference results. The method also allows linking estimation results to the signal-to-noise ratio, an important parameter of noisy information models. This task cannot be accomplished if one adopts an “agnostic” characterization of regression errors. With regard to empirical results, they show a substantial degree of information rigidity in the countries studied. They also suggest that the theoretical characterization of regression errors yields a more conservative picture of the uncertainty surrounding parameter estimates.     

Testing the value of directional forecasts in the presence of serial correlation

Common approaches to testing the economic value of directional forecasts are based on the classical χ²${\chi }^2$-test for independence, Fisher’s exact test or the Pesaran and Timmermann test for market timing. These tests are asymptotically valid for serially independent observations, but in the presence of serial correlation they are markedly oversized, as has been confirmed in a simulation study. We therefore summarize robust test procedures for serial correlation and propose a bootstrap approach, the relative merits of which we illustrate by means of a Monte Carlo study. Our evaluations of directional predictions of stock returns and changes in Euribor rates demonstrate the importance of accounting for serial correlation in economic time series when making such predictions.     

Weekly economic activity: Measurement and informational content

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables; and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators.     

Simple averaging of direct and recursive forecasts via partial pooling using machine learning

This article introduces the winning method at the M5 Accuracy competition. The presented method takes a simple manner of averaging the results of multiple base forecasting models that have been constructed via partial pooling of multi-level data. All base forecasting models of adopting direct or recursive multi-step forecasting methods are trained by the machine learning technique, LightGBM, from three different levels of data pools. At the competition, the simple averaging of the multiple direct and recursive forecasting models, called DRFAM, obtained the complementary effects between direct and recursive multi-step forecasting of the multi-level product sales to improve the accuracy and the robustness.     

Further evidence on financial information and economic activity forecasts in the United States

Our study provides substantially robust evidence for the predictive power of financial variables in forecasting the business cycle at a further step. We select several interesting and representative financial variables and reveal that they can predict significant information regarding future equity premiums as well as future macroeconomic activity, which are proxied by comprehensive fresh macroeconomic variables. The predictive power remains stable in out-of-sample estimations and can generate profits in an active market-timing trading strategy in excess of the historical mean forecast strategy. Cochrane provides one of the core interpretations for such forecasts in the theoretical asset pricing framework.     

Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply, among other matters. The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts. This study fills this gap in forecasting economic growth and inflation in China, by using the rolling weighted least squares (WLS) with the practically feasible cross-validation (CV) procedure of Hong et al. (2018) to choose an optimal estimation window. We undertake an empirical analysis of monthly data on up to 30 candidate indicators (mainly asset prices) for a span of 17 years (2000–2017). It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows. The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases. One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms, policies, crises, and other factors. Furthermore, we find that, in most cases, asset prices are key variables for forecasting macroeconomic variables, especially output growth rate.     

Statistical efficiency in the linear combination of forecasts

In seeking an efficient combination of forecasts which minimises the forecast error variance, many methods have been suggested. Through analysis, simulation and case studies, this paper seeks to develop insights into the statistical circumstances which influence the relative accuracy of six of these methods. The six methods chosen have all been advocated in various publications and consist of ‘equal weighting’ (i.e., pooled average), ‘optimal’ (i.e., error variance minimising), ‘optimal with independence assumption’ (i.e., error variance minimising assuming zero correlation between individual forecast errors) and three variations on the formulation of a Bayesian combination based upon posterior probabilities. The statistical circumstances reflected varying conditions of relative forecast errors, error correlations and outliers.     

Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7

In this paper, we use survey data to analyze the accuracy, unbiasedness and efficiency of professional macroeconomic forecasts. We analyze a large panel of individual forecasts that has not previously been analyzed in the literature. We provide evidence on the properties of forecasts for all G7-countries and for four different macroeconomic variables. Our results show a high degree of dispersion of forecast accuracy across forecasters. We also find that there are large differences in the performances of forecasters, not only across countries but also across different macroeconomic variables. In general, the forecasts tend to be biased in situations where the forecasters have to learn about large structural shocks or gradual changes in the trend of a variable. Furthermore, while a sizable fraction of forecasters seem to smooth their GDP forecasts significantly, this does not apply to forecasts made for other macroeconomic variables.     

企业生态经济效率与可持续发展

认为可持续发展是当今社会发展的主题,生态经济是现代乃至未来经济发展的方向;随着经济发展由工业时代向环境时代、由经济效率向生态效率的转变,将生态环境因素纳入企业管理决策中显得愈发迫切,在业绩评价中融入生态经济效率概念,对于企业可持续发展有着重要的现实意义。从业绩评价的角度对生态经济效率的相关概念、内涵、特点及作用进行了诠释和解读,指出生态经济效率融合了环境业绩与经济业绩,要求企业以较少的生态环境影响实现较大的经济效益,从而实现可持续发展的战略目标及创造长期的价值。