VAR Forecasting Using Bayesian Variable Selection

This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and nonlinear models, as well as models of large dimensions. The performance of the proposed variable selection method is assessed in forecasting three major macroeconomic time series of the UK economy. Data-based restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators.Forecasting; variable selection; time-varying parameters; Bayesian vector autoregression

Identifying Sources of Business Cycle Fluctuations in Germany 1975–1998

In this paper, we estimate a small New Keynesian dynamic stochastic general equilibrium (DSGE) model for Germany for the period from 1975 to 1998 and use it to identify the structural shocks, which have driven the business cycle. For this purpose we apply indirect inference methods, that is we specify the parameters of the theoretical model such that simulated data mimics observed data as closely as possible. In addition to the identification of structural shocks, we uncover the unobservable output gap, which is a prominent indicator in business cycle analysis. Furthermore,we show to which extent each identified shock has contributed to the business cycle fluctuations.Business cycle accounting, dynamic stochastic general equilibrium models, Germany, indirect inference, New Keynesian macroeconomics

Forecasting the Price of Oil

We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real or nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures markets in forecasting the price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future demand and supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?Econometric and statistical methods; International topics

Introducing shrinkage in heavy-tailed state space models to predict equity excess returns

We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space model. The shrinkage priors are complemented by heavy tailed state innovations that cater for potential large breaks in the latent states. Moreover, we allow for leptokurtic stochastic volatility in the observation equation. The empirical findings indicate that several variants of the proposed approach outperform typical competitors frequently used in the literature, both in terms of point and density forecasts

Bootstrap Hypothesis Testing

This paper surveys bootstrap and Monte Carlo methods for testing hypotheses in econometrics. Several different ways of computing bootstrap P values are discussed, including the double bootstrap and the fast double bootstrap. It is emphasized that there are many different procedures for generating bootstrap samples for regression models and other types of model. As an illustration, a simulation experiment examines the performance of several methods of bootstrapping the supF test for structural change with an unknown break point.bootstrap test, supF test, wild bootstrap, pairs bootstrap, moving block bootstrap, residual bootstrap, bootstrap P value

FORECASTING CATTLE PRICES IN THE PRESENCE OF STRUCTURAL CHANGE

Recent empirical research and development in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univeriate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.Demand and Price Analysis,

Leading Indicators of Inflation for Brazil

The goal of this project is to construct leading indicators that anticipate inflation cycle turning points on a real time monitoring basis. As a first step, turning points of the IPCA inflation are determined using a periodic stochastic Markov switching model. These turning points are the event timing that the leading indicators should anticipate. A dynamic factor model is then used to extract common cyclical movements in a set of variables that display predictive content for inflation. The leading indicators are designed to serve as practical tools to assist real-time monitoring of monetary policy on a month-to-month basis. Thus, the indicators are built and ranked according to their out-of-sample forecasting performance. The leading indicators are found to be an informative tool for signaling future phases of the inflation cycle out-of-sample, even in real time when only preliminary and unrevised data are available.

How to Solve the Price Puzzle? A Meta-Analysis

The short-run increase in prices following an unexpected tightening of monetary policy represents a frequently reported puzzle. Yet the puzzle is easy to explain away when all published models are quantitatively reviewed. We collect and examine about 1,000 point estimates of impulse responses from 70 articles using vector autoregressive models to study monetary transmission in various countries. We find some evidence of publication selection against the price puzzle in the literature, but our results also suggest that the reported puzzle is mostly caused by model misspecifications. Finally, the long-run response of prices to monetary policy shocks depends on the characteristics of the economy.Monetary policy transmission; Price puzzle; Meta-analysis; Publication selection bias

How to Solve the Price Puzzle? A Meta-Analysis

The short-run increase in prices following an unexpected tightening of monetary policy represents a frequently reported puzzle. Yet the puzzle is surprisingly easy to explain away when all published models are quantitatively reviewed. We collect about 1,000 point estimates of impulse responses from 70 articles using vector autoregressive models and present a simple method of research synthesis for graphical results. We find some evidence of publication selection against the price puzzle. Our results suggest that the reported impulse responses depend systematically on the study design: when misspecifications are filtered out, the average impulse response shows that prices decrease soon after a tightening. The long-run response of prices to monetary policy shocks depends on the characteristics of the economy.Meta-analysis, monetary policy transmission, price puzzle, publication selection bias.

Changes in Predictive Ability with Mixed Frequency Data

This paper proposes a new regression model - a smooth transition mixed data sampling (STMIDAS) approach - that captures recurrent changes in the ability of a high frequency variable in predicting a low frequency variable. The STMIDAS regression is employed for testing changes in the ability of financial variables in forecasting US output growth. The estimation of the optimal weights for aggregating weekly data inside the quarter improves the measurement of the predictive ability of the yield curve slope for output growth. Allowing for changes in the impact of the short-rate and the stock returns in future growth is decisive for finding in-sample and out-of-sample evidence of their predictive ability at horizons longer than one year.Smooth transition, MIDAS, Predictive ability, Asset prices, Output growth