Projecting the Medium-Term: Outcomes and Errors for GDP Growth

The focus of this paper is the evaluation of a very popular method for potential output estimation and medium-term forecasting? the production function approach?in terms of predictive performance. For this purpose, a forecast evaluation for the three to five years ahead predictions of GDP growth for the individual G7 countries is conducted. To carry out the forecast performance check a particular testing framework is derived that allows the computation of robust test statistics given the specific nature of the generated out-of sample forecasts. In addition, medium-term GDP projections from national and international institutions are examined and it is assessed whether these projections convey a reliable view about future economic developments and whether there is scope for improving their predictive content. --Potential output,projections,forecast evaluation

Panel Tests for Unit Roots in Hours Worked

Hours worked is a time series of interest in many empirical investigations of the macroeconomy. Estimates of macro elasticities of labour supply, for example, build on this variable. Other empirical applications investigate the response of hours worked to a shock to technology on the basis of the real business cycle model. Irrespective of the problem being addressed, robust inference of empirical outcomes strongly hinges on the adequately modelling of the time series of hours worked. The aim of the present paper is to provide cross country evidence of the non- stationarity of hours worked for OECD countries. For these purposes, panel unit root tests are employed to improve power against univariate counterparts. Since cross section correlation is a distinct feature of the underlying panel data, results are based on various second generation panel unit root tests which account for cross section dependence among units. If an unobserved common factor model is assumed for generating the observations, there is indication for both a common factor and idiosyncratic components driving the non-stationarity of hours worked. In addition, taking these results together, there is no indication of cointegration among the individual time series of hours worked. --Hours worked,panel unit root,cross section dependence,unobserved common factor,cointegration

Monte Carlo Study of Pure-Phase Cumulants of 2D q-State Potts Models

We performed Monte Carlo simulations of the two-dimensional q-state Potts model with q=10, 15, and 20 to study the energy and magnetization cumulants in the ordered and disordered phase at the first-order transition point $\beta_t$. By using very large systems of size 300 x 300, 120 x 120, and 80 x 80 for q=10, 15, and 20, respectively, our numerical estimates provide practically (up to unavoidable, but very small statistical errors) exact results which can serve as a useful test of recent resummed large-q expansions for the energy cumulants by Bhattacharya `et al.' [J. Phys. I (France) 7 (1997) 81]. Up to the third order cumulant and down to q=10 we obtain very good agreement, and also the higher-order estimates are found to be compatible.Comment: 18 pages, LaTeX + 2 postscript figures. To appear in J. Phys. I (France), May 1997 See also http://www.cond-mat.physik.uni-mainz.de/~janke/doc/home_janke.htm

The role of structural common and country-specific shocks in the business cycle dynamics of the G7 countries

The study analyses the business cycles of the G7 countries in a structural vector autoregression(SVAR) framework comprising output, nominal interest rate and inflation. Common and country-specific supply, demand and nominal shocks of each G7 country are identified, and the corresponding shock propagation channels are computed. We establish the statistical properties of the cyclical fluctuations and investigate the role of each structural common and country-specific shock in the cyclical fluctuations of the variables of interest as well as the business cycle co-movement in the G7 group of countries. --International Business Cycles,Common and Country-Specific Structural Shocks,Structural Vector Autoregression Models

Online Learning of a Memory for Learning Rates

The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks. Here, we introduce a computationally efficient online meta-learning algorithm that builds and optimizes a memory model of the optimal learning rate landscape from previously observed gradient behaviors. While performing task specific optimization, this memory of learning rates predicts how to scale currently observed gradients. After applying the gradient scaling our meta-learner updates its internal memory based on the observed effect its prediction had. Our meta-learner can be combined with any gradient-based optimizer, learns on the fly and can be transferred to new optimization tasks. In our evaluations we show that our meta-learning algorithm speeds up learning of MNIST classification and a variety of learning control tasks, either in batch or online learning settings.Comment: accepted to ICRA 2018, code available: https://github.com/fmeier/online-meta-learning ; video pitch available: https://youtu.be/9PzQ25FPPO