Narrow Results

This paper employs the local Bayesian likelihood methodology to estimate a medium-scale dynamic stochastic general equilibrium (DSGE) model on different frequencies and uses frequency-domain tools to evaluate the time-varying parameter model and the fixed-parameter model. These techniques yield fresh insights into theoretical and empirical implications conveyed by alternative models beyond what conventional time-domain approaches can offer. We show that parameter estimates are sensitive to frequencies, and goodness-of-fit varies substantially with the frequency bands. Overall, the estimated time-varying parameter model captures the properties of the U.S. data better in the business cycle frequency band, and beyond this band, the fixed-parameter model performs better. Additionally, our study also reveals the importance of structural shocks in improving the fit between models and data. Finally, we utilize the spectral representation of generalized forecast error variance decomposition to investigate the frequency dynamics of volatility connectedness. We find shocks to economic activity have an impact on variables at different frequencies with different strengths, and markets become more connected during crisis periods. Furthermore, this study provides insights into a question policymakers are much concerned with: which shocks are major sources of economic volatilities and which sectors serve as major recipients of shocks?

Final results of a detailed analysis of p+p elastic scattering data are presented, utilizing the quark–diquark model of protons in a form proposed by Bialas and Bzdak. The differential cross-section of elastic proton–proton collisions is analyzed in a detailed and systematic manner at small momentum transfers, starting from the energy range of CERN ISR at , including also recent TOTEM data at the present LHC energies at . These studies confirm the picture that the size of proton increases systematically with increasing energies, while the size of the constituent quarks and diquarks remains approximately independent of (or only increases slightly with) the colliding energy. The detailed analysis indicates correlations between model parameters and also indicates an increasing role of shadowing at LHC energies. Within the investigated class of models, a simple and model-independent phenomenological relation was discovered that connects the total p+p scattering cross-section to the effective quark, diquark size and their average separation. Our best fits indicate that the relative error of this phenomenological relation is 10–15% in the considered energy range.

We present a parsimonious method of improving forecasts and show that fit, the discrepancy between model forecasts and realized values, is persistent for individual stocks. Conditioning on fit profoundly affects the forecast error for future and out-of-sample returns. Forecasts of stock price direction with the best (worst) decile of historical fit are correct 63.6% (49.2%) of the time and are significantly different from the unconditioned model’s 56% accuracy. We find that superior factor forecasts are essential to profit from model conditioning and conclude that analysts who possess superior factor estimates can dramatically improve their forecasts through the technique we present.