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on Forecasting |
| By: | Stefan Neuwirth (KOF Swiss Economic Institute, ETH Zurich, Switzerland) |
| Abstract: | This paper tests the usefulness of time-varying parameters when forecasting with mixed-frequency data. For this we compare the forecast performance of bridge equations and unrestriced MIDAS models with constant and time-varying parameters. An out-of-sample forecasting exercise with US real-time data shows that the use of time-varying parameters does not improve forecasts significantly over all vintages. However, since the Great Recession, forecast errors are smaller when forecasting with bridge equations due to the ability of time-varying parameters to incorporate gradual structural changes faster. |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:17-430&r=for |
| By: | Kang, Wensheng (Kent State University); Ratti, Ronald. A. (Economics, Finance and Property, Western Sydney University); Vespignani, Joaquin (Tasmanian School of Business & Economics, University of Tasmania) |
| Abstract: | The value of the US dollar is of major importance to the world economy. Global liquidity has grown sharply in recent years with growing importance of China’s money supply to global liquidity. We develop out-of-sample forecasts of the US dollar exchange rate value using US and non-US global data on price level, output, interest rates, and liquidity on the US, China and non-US/non-China liquidity. Monetary model forecasts significantly outperform a random walk forecast in terms of MSFE in the long run. A monetary model/ECM with sticky prices performs best. Rolling sample analysis indicates changes over time in the influence of variables in forecasting the US dollar. China’s liquidity has a distinct, significant and changing influence on the US dollar exchange rate. Increases in the growth rate in the relative US-China M2 forecast a significantly higher value for the US dollar 1- and 6-month later. |
| Keywords: | China’s liquidity, trade-weighted US dollar, forecasting US dollar exchange rate |
| JEL: | E41 E51 F31 F41 |
| Date: | 2016–02 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:23110&r=for |
| By: | Alberto Marino (OECD); David Morgan (OECD); Luca Lorenzoni (OECD); Chris James (OECD) |
| Abstract: | Across the OECD, healthcare spending has typically outpaced economic growth in recent decades. While such spending has improved health outcomes, there are concerns about the financial sustainability of this upward trend, particularly as healthcare systems are predominantly funded from public resources in most OECD countries. To better explore this financial sustainability challenge, many countries and international institutions have developed forecasting models to project growth in future healthcare expenditure. Despite methodological differences between forecasting approaches, a common set of healthcare spending drivers can be identified. Demographic factors, rising incomes, technological progress, productivity in the healthcare sector compared to the general economy (Baumol’s cost disease) and associated healthcare policies have all been shown to be key determinants of healthcare spending. |
| JEL: | C53 H51 I18 J11 |
| Date: | 2017–06–22 |
| URL: | http://d.repec.org/n?u=RePEc:oec:elsaad:95-en&r=for |
| By: | Polanski, Arnold (University of East Anglia); Stoja, Evarist (School of Economics, Finance and Management, University of Bristol) |
| Abstract: | Multidimensional Value at Risk (MVaR) generalises VaR in a natural way as the intersection of univariate VaRs. We reduce the dimensionality of MVaRs which allows for adapting the techniques and applications developed for VaR to MVaR. As an illustration, we employ VaR forecasting and evaluation techniques. One of our forecasting models builds on the progress made in the volatility literature and decomposes multidimensional tail events into long-term trend and short-term cycle components. We compute short and long-term MVaR forecasts for several multidimensional time series and discuss their (un)conditional accuracy. |
| Keywords: | Multidimensional risk; multidimensional Value at Risk; two-factor decomposition; long-horizon forecasting |
| JEL: | C52 C53 |
| Date: | 2017–06–12 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0660&r=for |