Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting

This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge regression and forecast combinations, which are widely used in macroeconomic forecasting, and compares these with a lesser known alternative method: partial least squares regression. Under the latter, linear, orthogonal combinations of a large number of predictor variables are constructed such that these linear combinations maximize the covariance between the target variable and each of the common components constructed from the predictor variables. We provide a theorem that shows that when the data comply with a factor structure, principal components and partial least squares regressions provide asymptotically similar results. We also argue that forecast combinations can be interpreted as a restricted form of partial least squares regression. Monte Carlo experiments confirm our theoretical result that principal components and partial least squares regressions are asymptotically similar when the data has a factor structure. These experiments also indicate that when there is no factor structure in the data, partial least squares regression outperforms both principal components and Bayesian ridge regressions. Finally, we apply partial least squares, principal components and Bayesian ridge regressions on a large panel of monthly U.S. macroeconomic and financial data to forecast, for the United States, CPI inflation, core CPI inflation, industrial production, unemployment and the federal funds rate across different sub-periods. The results indicate that partial least squares regression usually has the best out-of-sample performance relative to the two other data-rich prediction methods.Macroeconomic forecasting, Factor models, Forecast combination, Principal components, Partial least squares, (Bayesian) ridge regression

Commodity prices, commodity currencies, and global economic developments

In this paper we seek to produce forecasts of commodity price movements that can systematically improve on naive statistical benchmarks, and revisit the forecasting performance of changes in commodity currencies as efficient predictors of commodity prices, a view emphasized in the recent literature. In addition, we consider different types of factor-augmented models that use information from a large data set containing a variety of indicators of supply and demand conditions across major developed and developing countries. These factor-augmented models use either standard principal components or partial least squares (PLS) regression to extract dynamic factors from the data set. Our forecasting analysis considers ten alternative indices and sub-indices of spot prices for three different commodity classes across different periods. We .find that the exchange rate-based model and especially the PLS factor-augmented model are more prone to outperform the naive statistical benchmarks. However, across our range of commodity price indices we are not able to generate out-of-sample forecasts that, on average, are systematically more accurate than predictions based on a random walk or autoregressive specifications.

Multivariate Methods for Monitoring Structural Change

Detection of structural change is a critical empirical activity, but continuous 'monitoring' of series, for structural changes in real time, raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case-by-case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co-breaking series. This is robust to a cross-sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices.Monitoring, Structural change, Panel, CUSUM, Fluctuation test

Pixel masks for screen-door transparency

Rendering objects transparently gives additional insight in complex and overlapping structures. However, traditional techniques for the rendering of transparent objects such as alpha blending are not very well suited for the rendering of multiple transparent objects in dynamic scenes. Screen-door transparency is a technique to render transparent objects in a simple and efficient way: No sorting is required and intersecting polygons can be handled without further preprocessing. With this technique, polygons are rendered through a mask: Only where the mask is present, pixels are set. However, artifacts such as incorrect opacities and distracting patterns can easily occur if the masks are not carefully designed. In this paper, first the requirements on the masks are considered. Next, three algorithms are presented for the generation of pixel masks. One algorithm is designed for the creation of small (e.g. 4 $times$ 4) masks. The other two algorithms can be used for the creation of larger masks (e.g. 32 $times$ 32). For each of these algorithms results are presented and discussed

Organizing NPD networks for high innovation performance:the case of Dutch medical devices SMEs

This research examines which combination of network characteristics (the network configuration) leads to high innovation performance for small and medium sized companies (SMEs). Even though research has paid significant attention to the relation between the external network and the innovation performance of SMEs, research has not yet clearly demonstrated which configurations most affect innovation in particular contexts. The context of the research is the Dutch medical devices sector. This sector is selected because collaboration with external partners for new product development means becomes increasingly important due to the complexity of the products and the fragmentation of the market. In addition the sector is characterized by very strict regulations. These regulations are the cause of the time and cost consuming product development process. In triangulation with quantitative survey data (N=60), qualitative data was gathered through semi-structured interviews in these same companies (N=50), which resulted in a response rate of 61,9%. The systems approach was used to construct the successful network configuration that is related to high innovation performance. By using this approach we are able to simultaneously address multiple network characteristics. Correlation statistics between the Innovation Performance and the Euclidean Distance showed that the more a companies’ network configuration differed from the successful network configuration, the lower the Innovation Performance of that company. Contrary to what we hypothesized from literature, the results of the social systems approach indicate that the network configuration that is related to high innovation performance includes high levels of resource complementarity and goal alignment, and low levels of trust and network position strength. Instead of the social way of networking, both our quantitative and qualitative findings show that a “businesslike” approach which is focused and consistent is related to high innovation performance

Emotion Recognition in Patients with Low-Grade Glioma before and after Surgery

Research on patients with low-grade gliomas (LGGs) showed neurocognitive impairments in various domains. However, social cognition has barely been investigated. Facial emotion recognition is a vital aspect of social cognition, but whether emotion recognition is affected in LGG patients is unclear. Therefore, we aimed to investigate the effect of LGG and resection by examining emotion recognition pre- and postoperatively. Additionally, the relationships among emotion recognition and general cognition and tumor location were investigated. Thirty patients with LGG who underwent resective surgery were included and matched with 63 healthy control participants (HCs). Emotion recognition was measured with the Facial Expressions of Emotion–Stimuli and Tests (FEEST) and general cognition with neuropsychological tests. Correlations and within-group and between-group comparisons were calculated. Before surgery, patients performed significantly worse than the HCs on FEEST-Total and FEEST-Anger. Paired comparisons showed no significant differences between FEEST scores before and post-surgery. No significant correlations with general cognition and tumor location were found. To conclude, the results of this study indicate that the tumor itself contributes significantly to social cognitive dysfunction and that surgery causes no additional deficit. Impairments were not related to general cognitive deficits or tumor location. Consequently, incorporating tests for emotion recognition into the neuropsychological assessment of patients with LGG is important

Correlative 3D cryo X-ray imaging reveals intracellular location and effect of designed antifibrotic protein-nanomaterial hybrids

Revealing the intracellular location of novel therapeutic agents is paramount for the understanding of their effect at the cell ultrastructure level. Here, we apply a novel correlative cryo 3D imaging approach to determine the intracellular fate of a designed protein-nanomaterial hybrid with antifibrotic properties that shows great promise in mitigating myocardial fibrosis. Cryo 3D structured illumination microscopy (cryo-3D-SIM) pinpoints the location and cryo soft X-ray tomography (cryo-SXT) reveals the ultrastructural environment and subcellular localization of this nanomaterial with spatial correlation accuracy down to 70 nm in whole cells. This novel high resolution 3D cryo correlative approach unambiguously locates the nanomaterial after overnight treatment within multivesicular bodies which have been associated with endosomal trafficking events by confocal microscopy. Moreover, this approach allows assessing the cellular response towards the treatment by evaluating the morphological changes induced. This is especially relevant for the future usage of nanoformulations in clinical practices. This correlative super-resolution and X-ray imaging strategy joins high specificity, by the use of fluorescence, with high spatial resolution at 30 nm (half pitch) provided by cryo-SXT in whole cells, without the need of staining or fixation, and can be of particular benefit to locate specific molecules in the native cellular environment in bio-nanomedicine