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

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

Comparative interactomics analysis of different ALS-associated proteins identifies converging molecular pathways

Amyotrophic lateral sclerosis (ALS) is a devastating neurological disease with no effective treatment available. An increasing number of genetic causes of ALS are being identified, but how these genetic defects lead to motor neuron degeneration and to which extent they affect common cellular pathways remains incompletely understood. To address these questions, we performed an interactomic analysis to identify binding partners of wild-type (WT) and ALS-associated mutant versions of ATXN2, C9orf72, FUS, OPTN, TDP-43 and UBQLN2 in neuronal cells. This analysis identified several known but also many novel binding partners of these proteins. Interactomes of WT and mutant ALS proteins were very similar except for OPTN and UBQLN2, in which mutations caused loss or gain of protein interactions. Several of the identified interactomes showed a high degree of overlap: shared binding partners of ATXN2, FUS and TDP-43 had roles in RNA metabolism; OPTN- and UBQLN2-interacting proteins were related to protein degradation and protein transport, and C9orf72 interactors function in mitochondria. To conf

The Monetary Exchange Rate Model as a Long-Run Phenomenon

Pure time series-based tests fail to find empirical support formonetary exchange rate models. In this paper we apply pooled timeseries estimation on a forward-looking monetary model, resulting inparameter estimates which are in compliance with the underlyingtheory. Based on a panel version of the Engle and Granger (1987) two-stepprocedure we find that the residuals of our pooled estimated modelare stationary. This indicates that on a pooled time series levelthere is cointegration between the exchange rate and themacroeconomic fundamentals of this monetary model.

Likelihood-Based Cointegration Analysis in Panels of Vector Error Correction Models

We propose in this paper a likelihood-based framework forcointegration analysis in panels of a fixed number of vector errorcorrection models. Maximum likelihood estimators of thecointegrating vectors are constructed using iterated GeneralizedMethod of Moments estimators. Using these estimators we constructlikelihood ratio statistics to test for a common cointegrationrank across the individual vector error correction models, bothwith heterogeneous and homogeneous cointegrating vectors. Thecorresponding limiting distributions are a summation of thelimiting behavior of Johansen (1991) trace statistics. We alsoincorporate both unrestricted and restricted deterministiccomponents which are either homogeneous or heterogeneous. Theproposed framework is applied on a data set of exchange rates andappropriate monetary fundamentals. The test results show strongevidence for the validity of the monetary exchange rate modelwithin a panel of vector error correction models for three majorEuropean countries, whereas the results based on individual vectorerror correction models for each of these countries separately areless supportive.

Financial amplification of foreign exchange risk premia

Theories of financial frictions in international capital markets suggest that financial intermediaries' balance sheet constraints amplify fundamental shocks. We present empirical evidence for such theories by decomposing the U.S. dollar risk premium into components associated with macroeconomic fundamentals, and a component associated with financial intermediary balance sheets. Relative to the benchmark model with only macroeconomic state variables, balance sheets amplify the U.S. dollar risk premium. We discuss applications to financial stability monitoring.Foreign exchange risk premium Financial stability monitoring Financial intermediaries Asset pricing

A real time evaluation of Bank of England forecasts of inflation and growth

We compare the Bank of England's Inflation Report quarterly forecasts for growth and inflation to real-time benchmark forecasts. The results reveal the well-known difficulty of forecasting in a stable macroeconomic environment, and the Inflation Report forecasts of GDP growth are generally inferior to forecasts from linear and non-linear univariate models. However, for the inflation forecast the Inflation Report is clearly dominant.Real-time data Forecast performance Inflation Growth