Forecasting the Distribution of Economic Variables in a Data-Rich Environment

Sebastiano Manzan

68 citations0/0 files storedJournal of Business and Economic Statistics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forecasting Techniques and Applications Monetary Policy and Economic Impact Financial Risk and Volatility Modeling

Abstract

In this paper we investigate the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable. The baseline time series model is a semi-parametric specification based on the Quantile Auto-Regressive (QAR) model that assumes that the quantiles depend on the lagged values of the variable. We then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset of variables selected by LASSO. We forecast the distribution of the h-month growth rate for four economic variables from 1975 to 2011 and evaluate the forecast accuracy with score functions tailored to evaluate quantile, areas of the distribution, and intervals. The results for the output and employment measures indicate that the multivariate models outperform the time series forecasts, in particular at short horizons and low quantiles, while for the inflation variables the improved performance occurs mostly at the one-year horizon. We also consider an application of the distribution forecasts to predict the probability of a future decline in output and employment and illustrate their practical use at three dates during the last recession.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 68 citations exist upstream.

References

2007Strictly Proper Scoring Rules, Prediction, and Estimation 2002Forecasting Using Principal Components From a Large Number of Predictors 2002Macroeconomic Forecasting Using Diffusion Indexes 1991The Term Structure as a Predictor of Real Economic Activity 2007Why Has U.S. Inflation Become Harder to Forecast?2000The Generalized Dynamic-Factor Model: Identification and Estimation 2006Tests of Conditional Predictive Ability 1998Evaluating Density Forecasts with Applications to Financial Risk Management

Cited by

2021Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them 2020Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions 2022TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES 2016Out-of-Sample Return Predictability: A Quantile Combination Approach 2015Quantile Forecast Combinations in Realised Volatility Prediction 2022

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.