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.     

Some theoretical results on forecast combinations

This paper proposes a framework for the analysis of the theoretical properties of forecast combination, with the forecast performance being measured in terms of mean squared forecast errors (MSFE). Such a framework is useful for deriving all existing results with ease. In addition, it also provides insights into two forecast combination puzzles. Specifically, it investigates why a simple average of forecasts often outperforms forecasts from single models in terms of MSFEs, and why a more complicated weighting scheme does not always perform better than a simple average. In addition, this paper presents two new findings that are particularly relevant in practice. First, the MSFE of a forecast combination decreases as the number of models increases. Second, the conventional approach to the selection of optimal models, based on a simple comparison of MSFEs without further statistical testing, leads to a biased selection.     

Evaluating a three-dimensional panel of point forecasts: The Bank of England Survey of External Forecasters

This article provides a first analysis of the forecasts of inflation and GDP growth obtained from the Bank of England's Survey of External Forecasters, considering both the survey average forecasts published in the quarterly Inflation Report, and the individual survey responses, recently made available by the Bank. These comprise a conventional incomplete panel dataset, with an additional dimension arising from the collection of forecasts at several horizons; both point forecasts and density forecasts are collected. The inflation forecasts show good performance in tests of unbiasedness and efficiency, albeit over a relatively calm period for the UK economy, and there is considerable individual heterogeneity. For GDP growth, inaccurate real-time data and their subsequent revisions are seen to cause serious difficulties for forecast construction and evaluation, although the forecasts are again unbiased. There is evidence that some forecasters have asymmetric loss functions.     

Using forecast evaluation to improve the accuracy of the Greenbook forecast

Recently, Patton and Timmermann (2012) proposed a more powerful kind of forecast efficiency regression at multiple horizons, and showed that it provides evidence against the efficiency of the Fed’s Greenbook forecasts. I use their forecast efficiency evaluation to propose a method for adjusting the Greenbook forecasts. Using this method in a real-time out-of-sample forecasting exercise, I find that it provides modest improvements in the accuracies of the forecasts for the GDP deflator and CPI, but not for other variables. The improvements are statistically significant in some cases, with magnitudes of up to 18% in root mean square prediction error.     

The uncertainty in extreme risk forecasts from covariate-augmented volatility models

For a GARCH-type volatility model with covariates, we derive asymptotically valid forecast intervals for risk measures, such as the Value-at-Risk or Expected Shortfall. To forecast these, we use estimators from extreme value theory. In the volatility model, we allow for leverage effects and the inclusion of exogenous variables, e.g., volatility indices or high-frequency volatility measures. In simulations, we find coverage of the forecast intervals to be adequate for sufficiently extreme risk levels and sufficiently large samples, which is consistent with theory. Finally, we investigate if covariate information from volatility indices or high-frequency data improves risk forecasts for major US stock indices. While—in our framework—volatility indices appear to be helpful in this regard, intra-day data are not.     

Does age structure forecast economic growth?

Increases in the proportion of the working age population can yield a “demographic dividend” that enhances the rate of economic growth. We estimate the parameters of an economic growth model using a cross section of countries over the period 1960 to 1980, and investigate whether the inclusion of age structure improves the model's forecasts for the period 1980 to 2000. We find that including the age structure improves the forecast, although there is evidence of parameter instability between periods with an unexplained growth slowdown in the second period. We use the model to generate growth forecasts for the period 2000 to 2020.     

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.     

Predicting economic contractions and expansions with the aid of professional forecasts

Traditional econometric models of economic contractions typically perform poorly in forecasting exercises. This criticism is also frequently levelled at professional forecast probabilities of contractions. This paper addresses the problem of incorporating the entire distribution of professional forecasts into an econometric model for forecasting contractions and expansions. A new augmented probit approach is proposed, involving the transformation of the distribution of professional forecasts into a ‘professional forecast’ prior for the economic data underlying the probit model. Since the object of interest is the relationship between the distribution of professional forecasts and the probit model’s economic-data dependent parameters, the solution avoids criticisms levelled at the accuracy of professional forecast based point estimates of contractions. An application to US real GDP data shows that the model yields significant forecast improvements relative to alternative approaches.     

Takeover prediction using forecast combinations

The ability to identify likely takeover targets at an early stage should provide investors with valuable information, enabling them to profit by investing in potential target firms. In this paper we contribute to the takeover forecasting literature by suggesting the combination of probability forecasts as an alternative method of improving the forecast accuracy in takeover prediction and realizing improved economic returns from portfolios made up of predicted targets. Forecasts from several non-linear forecasting models, such as logistic and neural network models and a combination of them, are used to determine the methodology that best reduces the out-of-sample misclassification error. We draw two general conclusions from our results. First, the forecast combination method outperforms the single models, and should therefore be used to improve the accuracy of takeover target predictions. Second, we demonstrate that an investment in a portfolio of the combined predicted targets results in significant abnormal returns being made by an investor, in the order of up to double the market benchmark return when using a portfolio of manageable size.     

Investigating the inefficiency of the CBO’s budgetary projections

This paper evaluates the efficiency of budgetary projections issued by the Congressional Budget Office (CBO) from 1984 to 2016 and investigates the cause of their inefficiency. The efficiency of both its budgetary and macroeconomic projections is rejected in many cases, especially for revenue projections. A comparison of forecast evaluations suggests that the inefficiency of revenue projections is likely due to the inefficiency of the underlying macroeconomic projections. By adjusting budgetary projections by the CBO in real time using macroeconomic forecasts by the Survey of Professional Forecasters, the accuracy of the former projections can be significantly improved in some cases, by up to 26% in terms of the root-mean-square prediction error.     

Scoring rules and survey density forecasts

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.     

MSE-improvement of the least squares estimator by dropping variables

It is well known that dropping variables in regression analysis decreases the variance of the least squares (LS) estimator of the remaining parameters. However, after elimination estimates of these parameters are biased, if the full model is correct. In his recent paper, Boscher (1991) showed that the LS-estimator in the special case of a mean shift model (cf. Cook and Weisberg, 1982) which assumes no “outliers” can be considered in the framework of a linear regression model where some variables are deleted. He derived conditions under which this estimator outperforms the LS-estimator of the full model in terms of the mean squared error (MSE)-matrix criterion. We demonstrate that this approach can be extended to the general set-up of dropping variables. Necessary and sufficient conditions for the MSE-matrix superiority of the LS-estimator in the reduced model over that in the full model are derived. We also provide a uniformly most powerful F-statistic for testing the MSE-improvement.     

The accuracy of IMF crises nowcasts

The International Monetary Fund (IMF) provides loans to countries in economic crises as a lender of last resort. IMF loan approvals are tied to policy reforms and quantitative targets that reflect the IMF’s crisis assessment. An extensive literature scrutinizes the efficacy of IMF loan programs, instead, we examine the accuracy of the IMF’s crisis assessments (nowcasts) that predicate program designs. Analyzing an unprecedented 602 IMF loan programs from 1992 to 2019, we contradict previous findings that IMF nowcasts are generally optimistic. Disentangling the structure of the IMF’s nowcast bias, we find the IMF systematically overestimates high-growth recoveries GDPs, while low-growth recoveries for low-income countries (LICs) are underestimated. In contrast, non-LICs’ nowcasts exhibit no statistically significant optimistic and pessimistic bias. Interestingly, shorter nowcast horizons do not improve accuracy, and GDP growth nowcasts improved substantially since 2013, while inflation nowcasts remain inefficient. We also isolate the sources of IMF nowcast inefficiencies according to ((i) program objectives, ((ii) program conditionality type, ((iii) geographic regions, ((iv) global crises, and ((v) geopolitics (elections, conflicts, and disasters).     

A comparison of financial duration models via density forecasts

Using density forecast evaluation techniques, we compare the predictive performance of econometric specifications that have been developed for modeling duration processes in intra-day financial markets. The model portfolio encompasses various variants of the Autoregressive Conditional Duration (ACD) model and recently proposed dynamic factor models. The evaluation is conducted on time series of trade, price and volume durations computed from transaction data of NYSE listed stocks. The results show that simpler approaches perform at least as well as more complex methods. With respect to modeling trade duration processes, standard ACD models successfully account for duration dynamics while none of the models provides an acceptable specification for the conditional duration distribution. We find that the Logarithmic ACD, if based on a flexible innovation distribution, provides a quite robust and useful framework for the modeling of price and volume duration processes.     

Boosting nonlinear predictability of macroeconomic time series

We apply the boosting estimation method in order to investigate to what extent and at what horizons macroeconomic time series have nonlinear predictability that comes from their own history. Our results indicate that the U.S. macroeconomic time series have more exploitable nonlinear predictability than previous studies have found. On average, the most favorable out-of-sample performance is obtained via a two-stage procedure, where a conventional linear prediction model is fitted first and the boosting technique is applied to build a nonlinear model for its residuals.     

A new marked point process model for the federal funds rate target: Methodology and forecast evaluation

Forecasts of key interest rates set by central banks are of paramount concern for investors and policy makers. Recently it has been shown that forecasts of the federal funds rate target, the most anticipated indicator of the Federal Reserve Bank's monetary policy stance, can be improved considerably when its evolution is modeled as a marked point process (MPP). This is due to the fact that target changes occur in discrete time with discrete increments, have an autoregressive nature and are usually in the same direction. We propose a model which is able to account for these dynamic features of the data. In particular, we combine Hamilton and Jordà's [2002. A model for the federal funds rate target. Journal of Political Economy 110(5), 1135–1167] autoregressive conditional hazard (ACH) and Russell and Engle's [2005. A discrete-state continuous-time model of financial transactions prices and times: the autoregressive conditional multinomial-autoregressive conditional duration model. Journal of Business and Economic Statistics 23(2), 166 – 180] autoregressive conditional multinomial (ACM) model. The paper also puts forth a methodology to evaluate probability function forecasts of MPP models. By improving goodness of fit and point forecasts of the target, the ACH–ACM qualifies as a sensible modeling framework. Furthermore, our results show that MPP models deliver useful probability function forecasts at short and medium term horizons.     

On the path-dependence of economic growth

The cumulated growth of an economy during a period of time depends on the whole economical history during this period and not only of the situation at the beginning and at the end of the interval. However, if some constraint is made on the set of possible economies, this phenomenon of history-dependence may disappear. We give a simple necessary and sufficient condition on the constraint for this to happen, generalizing results of Samuelson–Swamy and Hulten. Our approach is different, based on differential and symplectic geometry. We also solve the same question for a subsector of the economy.     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

On Estimators of the Nearest Neighbour Distance Distribution Function for Stationary Point Processes

There are three approaches for the estimation of the distribution function D(r) of distance to the nearest neighbour of a stationary point process: the border method, the Hanisch method and the Kaplan-Meier approach. The corresponding estimators and some modifications are compared with respect to bias and mean squared error (mse). Simulations for Poisson, cluster and hard-core processes show that the classical border estimator has good properties; still better is the Hanisch estimator. Typically, mse depends on r, having small values for small and large r and a maximum in between. The mse is not reduced if the exact intensity λ (if known) or intensity estimators from larger windows are built in the estimators of D(r); in contrast, the intensity estimator should have the same precision as that of λ D(r). In the case of replicated estimation from more than one window the best way of pooling the subwindow estimates is averaging by weights which are proportional to squared point numbers.     

水电并购项目初步经济评价分析

清洁能源是我国未来能源发展的趋势,水电能源作为清洁能源的重要组成部分,在市场经济条件下,水电并购项目日趋增多,项目初步经济评价对项目决策起着至关重要的作用。文章对水电项目经济评价的基本流程、基准收益率、基本参数和测算模型进行了探讨和分析,提出建议以供参考。