Adaptive Forecasting of Exchange Rates with Panel Data

This article investigates the statistical and economic implications of adaptive forecasting of exchange rates with panel data and alternative predictors. The candidate exchange rate predictors are drawn from (i) macroeconomic 'fundamentals', (ii) return/volatility of asset markets and (iii) cyclical and confidence indices. Exchange rate forecasts at various horizons are obtained from each of the potential predictors using single market, mean group and pooled estimates by means of rolling window and recursive forecasting schemes. The capabilities of single predictors and of adaptive techniques for combining the generated exchange rate forecasts are subsequently examined by means of statistical and economic performance measures. The forward premium and a predictor based on a Taylor rule yield the most promising forecasting results out of the macro 'fundamentals' considered. For recursive forecasting, confidence indices and volatility in-mean yield more accurate forecasts than most of the macro 'fundamentals'. Adaptive forecast combinations techniques improve forecasting precision and lead to better market timing than most single predictors at higher horizons.exchange rate forecasting; panel data; forecast combinations; market timing

Cointegration and the demand for gasoline

Since the early 1970s there has been a worldwide upsurge in the price of energy and in particular of gasoline. Therefore, demand functions for energy and its components like gasoline have received much attention. However, since confidence in the estimated demand functions is important for use in policy and forecasting, following Amarawickrama and Hunt (2008), this paper estimates the demand for gasoline is estimated with 6 alternative time series techniques with data from Fiji. Estimates with these 6 alternative techniques are very close and thus increase our confidence in them. We found that gasoline demand is both price and income inelastic

Does money growth granger-cause inflation in the Euro Area? Evidence from output-of-sample forecasts using Bayesian VARs

We use a mean-adjusted Bayesian VAR model as an out-of-sample forecasting tool to test whether money growth Granger-causes inflation in the euro area. Based on data from 1970 to 2006 and forecasting horizons of up to 12 quarters, there is surprisingly strong evidence that including money improves forecasting accuracy. The results are very robust with regard to alternative treatments of priors and sample periods. That said, there is also reason not to overemphasize the role of money. The predictive power of money growth for inflation is substantially lower in more recent sample periods compared to the 1970s and 1980s. This cautions against using money-based inflation models anchored in very long samples for policy advice. --Bayesian VAR,out-of-sample forecasting,Granger causality,monetary aggregates,inflation,monetary policy,European Central Bank.

Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?

Surveys do! We examine the forecasting power of four alternative methods of forecasting U.S. inflation out-of-sample: time series ARIMA models; regressions using real activity measures motivated from the Phillips curve; term structure models that include linear, non-linear, and arbitrage-free specifications; and survey-based measures. We also investigate several optimal methods of combining forecasts. Our results show that surveys outperform the other forecasting methods and that the term structure specifications perform relatively poorly. We find little evidence that combining forecasts using means or medians, or using optimal weights with prior information produces superior forecasts to survey information alone. When combining forecasts, the data consistently places the highest weights on survey information.

Forecasting exchange rates of major currencies with long maturity forward rates. Bruegel Working Paper | Issue 02 April 2020. Plus Annex in separate pdf

This paper presents unprecedented exchange rate forecasting results, based upon a new model that approximates the gap between the fundamental equilibrium exchange rate and the actual exchange rate with the longmaturity forward exchange rate. The theoretical derivation of our forecasting equation is consistent with the monetary model of exchange rates. Our model outperforms the random walk in out-of-sample forecasting of twelve major currency pairs over the short and long horizon forecasts for the 1990- 2020 period. The results are robust for all sub-periods, with the exception of the years around the collapse of Lehman Brothers in September 2008. Our results are robust to alternative model specifications, single equation and panel estimation, recursive and rolling estimation, and alternate data construction methods. The model performs better when the long-maturity forward exchange rate is assumed to be stationary, as opposed to assuming non-stationarity. The improvement in forecast accuracy from our model is economically and statistically significant for almost all exchange-rate series. The model is simple, linear, easy to replicate, and the data we use is available in real time and not subject to revision

Improved forecasting with leading indicators: the principal covariate index

We propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite CoincidentIndex. The forecast performance is compared with an alternative index based on principal components and with the Composite Leading Index of the Conference Board. The results show that the new index, which takes the forecast objective explicitly into account, provides significant gains over other single-index methods, both in terms of forecast accuracy and in terms of predicting recession probabilities.business cycles;turning points;index construction;principal covariate;principal component;time series forecasting

Back to basics: Data revisions

With few exceptions, most economic data undergo revisions. Although frequently neglected, data revisions may have implications, not only for economic analysis, but also for policy decisions, as revisions may alter current assessment and forecasts of economic developments. In this paper, we reassess data revisions analysis and its impact on forecasting, presenting an encompassing and unified perspective on this subject. For this purpose, we built a real-time database for Portuguese exports and imports of goods. We present a broad set of the measures typically used to gauge revisions and add to this discussion by clarifying the relations between revisions to deferent types of series (for example, revisions to month-on-month and year-on-year rates of change). Furthermore, regarding the (un)predictability of revisions, we suggest an alternative testing approach. The key feature of this approach is that it takes into account both in-sample and out-of-sample performances. We also discuss the impact of revisions on forecasting, focusing on short-term forecasting of first releases. Even though not accounting for data revision implications can lead to suboptimal results, our findings reinforce the need for a case by case analysis.