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DSGE Model-Based Forecasting of Non-Modelled Variables

Schorfheide, Frank - 2010

This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to...

Persistent link: https://www.econbiz.de/10012757579

Identifying Long-Run Risks : A Bayesian Mixed-Frequency Approach

Schorfheide, Frank - 2014

multiple stochastic volatility processes. The estimation is based on annual consumption data from 1929 to 1959, monthly … Bayesian estimation provides strong evidence for a small predictable component in consumption growth (even if asset return data … are omitted from the estimation). Three independent volatility processes capture different frequency dynamics; our …

Persistent link: https://www.econbiz.de/10013050301

Answering the Critics : Yes, Arch Models Do Provide Good Volatility Forecasts

Andersen, Torben G. - 2008

Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this, a voluminous literature has emerged for modeling the...

Persistent link: https://www.econbiz.de/10012774886

Comparing Predictive Accuracy

Diebold, Francis X.; Mariano, Roberto S. - 2021

Using research designs patterned after randomized experiments, many recent economic studies examine outcome measures for treatment groups and comparison groups that are not randomly assigned. By using variation in explanatory variables generated by changes in state laws, government draft...

Persistent link: https://www.econbiz.de/10013223006

Are Macroeconomic Forecasts Informative? Cointegration Evidence from the Asa-Nber Surveys

Cheung, Yin-Wong; Chinn, Menzie David - 2021

We examine the properties of the ASA-NBER forecasts for several US macroeconomic variables, specifically: (i) are the actual and forecast series integrated of the same order; (ii) are they cointegrated, and; (iii) is the cointegrating vector consistent with long run unitary elasticity of...

Persistent link: https://www.econbiz.de/10013224861

The Predictive Ability of Several Models of Exchange Rate Volatility

West, Kenneth D.; Cho, Dongchul - 2021

We compare the out-of-sample forecasting performance of univariate homoskedastic, GARCH, autoregressive and nonparametric models for conditional variances, using five bilateral weekly exchange rates for the dollar, 1973-1989. For a one week horizon, GARCH models tend to make slightly more...

Persistent link: https://www.econbiz.de/10013225431

Bayesian Variable Selection for Nowcasting Economic Time Series

Scott, Steven L. - 2014

We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine...

Persistent link: https://www.econbiz.de/10013062413

Evidence on Structural Instability in Macroeconomic Time Series Relations

Stock, James H.; Watson, Mark W. - 2021

An experiment is performed to assess the prevalence of instability in univariate and bivariate macroeconomic time series relations and to ascertain whether various adaptive forecasting techniques successfully handle any such instability. Formal tests for instability and out-of-sample forecasts...

Persistent link: https://www.econbiz.de/10013311213

Tempered Particle Filtering

Herbst, Edward - 2017

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While...

Persistent link: https://www.econbiz.de/10012955446

Conditional Projection by Means of Kalman Filtering

Clarida, Richard; Coyle, Diane - 2021

We establish that the recursive, state-space methods of Kalman filtering and smoothing can be used to implement the Doan, Litterman, and Sims (1983) approach to econometric forecast and policy evaluation. Compared with the methods outlined in Doan, Litterman, and Sims, the Kalman algorithms are...

Persistent link: https://www.econbiz.de/10013248279

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