A residual-based multivariate constant correlation test

We propose a new multivariate constant correlation test based on residuals. This test takes into account the whole correlation matrix instead of the considering merely marginal correlations between bivariate data series. In financial markets, it is unrealistic to assume that the marginal variances are constant. This motivates us to develop a constant correlation test which allows for non-constant marginal variances in multivariate time series. However, when the assumption of constant marginal variances is relaxed, it can be shown that the residual effect leads to nonstandard limit distributions of the test statistics based on residual terms. The critical values of the test statistics are not directly available and we use a bootstrap approximation to obtain the corresponding critical values for the test. We also derive the limit distribution of the test statistics based on residuals under the null hypothesis. Monte Carlo simulations show that the test has appealing size and power properties in finite samples. We also apply our test to the stock returns in Euro Stoxx 50 and integrate the test into a binary segmentation algorithm to detect multiple break points.     

Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning.     

Interpoint Distance Test of Homogeneity for Multivariate Mixture Models

Finite mixtures offer a rich class of distributions for modelling of a variety of random phenomena in numerous fields of study. Using the sample interpoint distances (IPDs), we propose the IPD‐test statistic for testing the hypothesis of homogeneity of mixture of multivariate power series distribution or multivariate normal distribution. We derive the distribution of the IPDs that are drawn from a finite mixture of the multivariate power series distribution and multivariate normal distribution. Based on the empirical distribution of the IPDs, we construct a bootstrap test of homogeneity for other multivariate finite mixture models. The IPD test is applied to mixture models for matrix‐valued distributions and a test of homogeneity for Wishart mixture is presented. Numerical comparisons show that IPD test has accurate type I errors and is more powerful in most multivariate cases than the expectation–maximization (EM) test and modified likelihood ratio test.     

The non-normality of some macroeconomic forecast errors

This paper investigates the distributional properties of individual and consensus time series macroeconomic forecast errors, using data from the Survey of Professional Forecasters. The degree of autocorrelation and the presence of ARCH in the consensus errors is also determined. We find strong evidence of leptokurtic forecast errors as well as evidence of skewness, suggesting that an assumption of error normality is inappropriate; many of the forecast error series are found to have non-zero mean, and we find widespread evidence of consensus error ARCH. Properties of the distribution of cross-sectional forecast errors are also examined.     

Research and Development,Uncertainty, and Analysts’ Forecasts: The Case of IAS 38

This study analyzes the consequences of the capitalization of development expenditures under IAS 38 on analysts’ earnings forecasts. We use unique hand‐collected data in a sample of highly research and development (R&D)‐intensive German‐listed firms over the period 2000–2007. We find that the capitalization of development costs is significantly associated with both higher individual analysts’ forecast errors and forecast dispersion. This suggests that the increasing complexity surrounding the capitalization of development costs negatively impacts forecast accuracy. However, for firms with high underlying environmental uncertainty, forecast errors are negatively associated with capitalized development expenditures. This indicates that the negative impact of increased complexity on forecast accuracy can be outweighed by the information contained in the signals from capitalized development costs when the underlying environmental uncertainty is high. The findings contribute to the ongoing controversial debate on the accounting for self‐generated intangible assets. Our results provide useful insights on the link between capitalization of development costs, environmental uncertainty, and analysts’ forecasts for accounting academics and practitioners alike.     

Does the Capitalization of Development Costs Improve Analyst Forecast Accuracy? Evidence from the UK

It has been documented that investments in Research and Development (R&D) are associated with increased errors and inaccuracy in earnings forecasts made by financial analysts. These deficiencies have been generally attributed to information complexity and the uncertainty of the future benefits of R&D. This paper examines whether the capitalization of development costs can reduce analyst uncertainty about the future economic outcome of R&D investments, provide outsiders with a better matching of future R&D‐related revenues and costs, and therefore promote accuracy in analyst forecasts. UK data is used, because accounting rules in the United Kingdom permitted firms to conditionally capitalize development costs even before the introduction of the International Financial Reporting Standards. The choice to expense R&D rather than conditionally capitalize development costs is found to relate positively to signed analyst forecast errors. This finding is robust to controlling for the influence of other factors that may affect errors, as well as for the influence of R&D investments on forecast errors. The decision to capitalize versus expense is not observed to have a significant influence on analyst forecast revisions. The findings are interpreted as evidence that the choice to capitalize as opposed to expense may help to reduce deficiencies in analyst forecasts; hence, is informative for users of financial statements. Increased informativeness is expected to have repercussions for the effectiveness with which analysts produce earnings forecasts, and, as a result, market efficiency.     

Asymmetry in unemployment rate forecast errors

Asymmetries in unemployment dynamics have been observed in the time series of a number of countries, including the United States. This paper studies asymmetries in unemployment rate forecast errors. We consider conditions under which optimal forecasts will display asymmetrically-distributed errors and how the degree of asymmetry might vary with the forecast horizon. Using data from the U.S. Survey of Professional Forecasters and the Federal Reserve Greenbook, we find substantial evidence of forecast error asymmetry, which tends to increase with the forecast horizon; we also find noteworthy differences in forecasts from these two sources. The results give insight into the abilities of professional forecasters to adapt their forecasts to asymmetry in underlying processes.     

Semiparametric quantile averaging in the presence of high-dimensional predictors

The paper proposes a method for forecasting conditional quantiles. In practice, one often does not know the “true” structure of the underlying conditional quantile function, and in addition, we may have a large number of predictors. Focusing on such cases, we introduce a flexible and practical framework based on penalized high-dimensional quantile averaging. In addition to prediction, we show that the proposed method can also serve as a predictor selector. We conduct extensive simulation experiments to asses its prediction and variable selection performances for nonlinear and linear time series model designs. In terms of predictor selection, the approach tends to select the true set of predictors with minimal false positives. With respect to prediction accuracy, the method competes well even with the benchmark/oracle methods that know one or more aspects of the underlying quantile regression model. We further illustrate the merit of the proposed method by providing an application to the out-of-sample forecasting of U.S. core inflation using a large set of monthly macroeconomic variables based on FRED-MD database. The application offers several empirical findings.     

The tourism forecasting competition

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.     

Testing for unit roots in the possible presence of multiple trend breaks using minimum Dickey–Fuller statistics

Trend breaks appear to be prevalent in macroeconomic time series, and unit root tests therefore need to make allowance for these if they are to avoid the serious effects that unmodelled trend breaks have on power. Carrion-i-Silvestre et al. (2009) propose a pre-test-based approach which delivers near asymptotically efficient unit root inference both when breaks do not occur and where multiple breaks occur, provided the break magnitudes are fixed. Unfortunately, however, the fixed magnitude trend break asymptotic theory does not predict well the finite sample power functions of these tests, and power can be very low for the magnitudes of trend breaks typically observed in practice. In response to this problem we propose a unit root test that allows for multiple breaks in trend, obtained by taking the infimum of the sequence (across all candidate break points in a trimmed range) of local GLS detrended augmented Dickey–Fuller-type statistics. We show that this procedure has power that is robust to the magnitude of any trend breaks, thereby retaining good finite sample power in the presence of plausibly-sized breaks. We also demonstrate that, unlike the OLS detrended infimum tests of Zivot and Andrews (1992), these tests display no tendency to spuriously reject in the limit when fixed magnitude trend breaks occur under the unit root null.     

Smooth-transition error-correction in exchange rates

Recent research has increasingly suggested that exchange rates may be characterized by non-linear behavior. This paper examines whether such non-linear behavior is evident, not in rates themselves, but in the adjustment of rates back to fundamental equilibrium. Thus, we examine whether a series of four spot and forward exchange rates exhibit smooth transition non-linear error-correction dynamic behavior. Our results are supportive of this model, particularly in-sample, and suggest some salient differences in the mean-reverting behavior of spot and forward rates, which may be of use to policy authorities and model builders. However, out-of-sample forecast errors between the two models appear insignificantly different from each other.     

Commodity futures prices: More evidence on forecast power,risk premia and the theory of storage

In this paper, we examine the temporal stability of the evidence for two commodity futures pricing theories. We investigate whether the forecast power of commodity futures can be attributed to the extent to which they exhibit seasonality and we also consider whether there are time varying parameters or structural breaks in these pricing relationships. Compared to previous studies, we find stronger evidence of seasonality in the basis, which supports the theory of storage. The power of the basis to forecast subsequent price changes is also strengthened, while results on the presence of a risk premium are inconclusive. In addition, we show that the forecasting power of commodity futures cannot be attributed to the extent to which they exhibit seasonality. We find that in most cases where structural breaks occur, only changes in the intercepts and not the slopes are detected, illustrating that the forecast power of the basis is stable over different economic environments.     

Fiscal Surprises at the FOMC

We examine a new set of U.S. fiscal forecasts from the FOMC briefing books. These forecasts are precisely those that were presented to monetary policymakers, and include frequently-updated estimates covering six complete business cycles and several fiscal-policy regimes. We detail the performances of forecast federal expenditures, revenues, surpluses, and structural surpluses in terms of their accuracy, bias, and efficiency. We find that forecast errors can be large economically, even at relatively short forecast horizons. While economic activity became less volatile after 1990, fiscal policy became harder to forecast. Finally, cyclically-adjusted deficit forecasts appear to be over-optimistic around both peaks and troughs of the business cycle, suggesting that fiscal policy is counter-cyclical in downturns and pro-cyclical in the early stages of recoveries.     

Unraveling the Genetic Component of Multifactorial Diseases: Dream or Reality

The etiology of many human diseases is complex and very likely involves a combination of genetic and environmental risk factors. A popular strategy to detect genetic risk factors is to perform a systematic screening of the genome searching for linkage. The power of such and approach depends very much on the unknown characteristics of the genetic factors and the main difficulty is to establish a good trade-off between false positives and false negatives. Besides, a precise localisation of the risk factor will generally not be obtained. The set up of a candidate gene stratery is necessary to go further in genetic factor identification. It is likely that for multicfactorioal diseases the only genetic risk factors that can be detected are those with fairly strong effect. Even in that case, it is important to design strategies which increase the power of detection and provide for a better evaluation of the associated risks.     

Sensitivity and specificity of the Mini-Mental State Examination in the Mexican population

The Mini-Mental State Examination (MMSE) is a rapid, easy-to-administer test for the assessment of cognition functions. It is widely used in clinical practice and in applied research. In this study, we aimed to establish a standard for the Mexican population similar to the ones produced for other relevant populations. We also analysed the effects of demographic variables which regularly induce bias in responses on performance tests, and then, on the basis of the results, implemented a series of corrections to the MMSE to compensate for the usual effects of age and years of formal education. We thus generated a new scale, the adjusted MMSE (AMMSE). We established the maximum sensitivity point to discriminate between the normal population and subjects diagnosed with dementia (vascular and Alzheimer’s). The study provides sensitivity and specificity estimates of this subject-standardized tool in order to reduce the probability of false positives and negatives in the Mexican population.     

Physics-informed Gaussian process regression for states estimation and forecasting in power grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method.We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power.For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.     

Unit root testing under a local break in trend

Recent approaches to testing for a unit root when uncertainty exists over the presence and timing of a trend break employ break detection methods, so that a with-break unit root test is used only if a break is detected by some auxiliary statistic. While these methods achieve near asymptotic efficiency in both fixed trend break and no trend break environments, in finite samples pronounced “valleys” in the power functions of the tests (when mapped as functions of the break magnitude) are observed, with power initially high for very small breaks, then decreasing as the break magnitude increases, before increasing again. In response to this problem, we propose two practical solutions, based either on the use of a with-break unit root test but with adaptive critical values, or on a union of rejections principle taken across with-break and without-break unit root tests. These new procedures are shown to offer improved reliability in terms of finite sample power. We also develop local limiting distribution theory for both the extant and the newly proposed unit root statistics, treating the trend break magnitude as local-to-zero. We show that this framework allows the asymptotic analysis to closely approximate the finite sample power valley phenomenon, thereby providing useful analytical insights.     

FFORMPP: Feature-based forecast model performance prediction

This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from historical time series with an efficient Bayesian multivariate surface regression approach. The minimum predicted forecast error is then used to identify an individual model or a combination of models to produce the final forecasts. It is well known that the performance of most meta-learning models depends on the representativeness of the reference dataset used for training. In such circumstances, we augment the reference dataset with a feature-based time series simulation approach, namely GRATIS, to generate a rich and representative time series collection. The proposed framework is tested using the M4 competition data and is compared against commonly used forecasting approaches. Our approach provides comparable performance to other model selection and combination approaches but at a lower computational cost and a higher degree of interpretability, which is important for supporting decisions. We also provide useful insights regarding which forecasting models are expected to work better for particular types of time series, the intrinsic mechanisms of the meta-learners, and how the forecasting performance is affected by various factors.     

On the comprehensive method of testing non-nested regression models

The paper derives the specific form of the exponentially combined likelihood function of two competing multivariate non-linear regression models and shows that the application of the comprehensive approach to testing non-nested regression models will, in general, be indeterminate. It establishes that in the univariate case there exists a large number of tests of non-nested regression models which are consistent in addition to having the same asymptotic distribution under the null hypothesis. The paper then derives a set of conditions under which all these consistent tests are asymptotically equivalent not only under the null hypothesis but also under local alternatives. As an application of this latter result the paper establishes the asymptotic equivalence of the tests recently proposed by Davidson and MacKinnon, and Fisher and McAleer under local alternatives, and shows that within the class of tests considered in the paper these proposed tests possess maximum local power. The latter test has this property only when the number of explanatory variables of the ‘true’ model is not more than that of the ‘false’ model.     

Anticipation,learning and welfare: the case of distortionary taxation

We study the impact of anticipated fiscal policy changes in a Ramsey economy where agents form long-horizon expectations using adaptive learning. We extend the existing framework by introducing distortionary taxes as well as elastic labor supply, which makes agents’ decisions non-predetermined but more realistic. We detect that the dynamic responses to anticipated tax changes under learning have oscillatory behavior that can be interpreted as self-fulfilling waves of optimism and pessimism emerging from systematic forecast errors. Moreover, we demonstrate that these waves can have important implications for the welfare consequences of fiscal reforms.