This paper examines how and to what extent parameter estimates can be biased in a dynamic stochastic general equilibrium (DSGE) model that omits the zero lower bound constraint on the nominal interest rate. Our experiments show that most of the parameter estimates in a standard sticky-price DSGE...

Macroeconometric and fi?nancial researchers often use secondary or constructed binary random variables that differ in terms of their sta- tistical properties from the primary random variables used in micro- econometric studies. One important difference between primary and secondary binary...

To match the NBER business cycle features it is necessary to employ Gen- eralised dynamic categorical (GDC) models that impose certain phase re- strictions and permit multiple indexes. Theory suggests additional shape re- strictions in the form of monotonicity and boundedness of certain...

A Hidden Markov Model (HMM) is used to classify an out of sample <p> observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. <p> Instead o maximizing a likelihood, the model is estimated...</p></p>

We propose a methodology to gauge the uncertainty in output gap nowcasts across a large number of commonly-deployed vector autoregressions in US inflation and various measures of the output gap. Our approach constructs ensemble nowcast densities using a linear opinion pool. This yields...

We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents...

We highlight how detrending within Structural Vector Autoregressions (SVAR) is directly linked to the shock identification. Consequences of trend misspecification are investigated using a prototypical Real Business Cycle model as the Data Generating Process. Decomposing the different sources of...

We examine the effectiveness of recursive-weight and equal-weight combination strategies for forecasting using many time-varying models of the relationship be- tween inflation and the output gap. The forecast densities for inflation reflect the uncertainty across models using many statistical...

A bivariate second-order VAR model of money growth and inflation is specified and estimatedby means of least squares. The bias of the parameter estimates is approximated in three ways and new, bias-reduced estimates are computed using the approximations. The effects of bias reduction on...

The paper discusses a simple univariate nonlinear parametric time-series model for unemployment rates, focusing on the asymmetry observed in many OECD unemployment rate series. The model is based on a standard logistic smooth transition autoregressive (LSTAR) model for the first difference of...