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on Forecasting |
| By: | Markus Heinrich; Magnus Reif |
| Abstract: | We extend the literature on economic forecasting by constructing a mixed-frequency time-varying parameter vector autoregression with stochastic volatility (MF-TVP-SVVAR). The latter is able to cope with structural changes and can handle indicators sampled at different frequencies. We conduct a real-time forecast exercise to predict US key macroeconomic variables and compare the predictions of the MF-TVP-SV-VAR with several linear, nonlinear, mixed-frequency, and quarterly-frequency VARs. Our key finding is that the MF-TVPSV-VAR delivers very accurate forecasts and, on average, outperforms its competitors. In particular, inflation forecasts benefit from this new forecasting approach. Finally, we assess the models’ performance during the Great Recession and find that the combination of stochastic volatility, time-varying parameters, and mixed-frequencies generates very precise inflation forecasts. |
| Keywords: | Time-varying parameters, forecasting, mixed-frequency models, Bayesian methods |
| JEL: | C11 C53 E32 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ifowps:_273&r=for |
| By: | Chiu,Ching-Wai; Hayes, Simon; Kapetanios, George (King's College London); Theodoridis, Konstantinos (Cardiff Business School) |
| Abstract: | Forecasts play a critical role at inflation targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. Commonly used statistical procedures, however, implicitly put a lot of weight on type I errors (or false positives), which result in a relatively low power of tests to identify forecast breakdowns in small samples. We develop a procedure which aims at capturing the policy cost of missing a break. We use data-based rules to find the test size that optimally trades of the costs associated with false positives with those that can result from a break going undetected for too long. In so doing, we also explicitly study forecast errors as a multivariate system. The covariance between forecast errors for different series, though often overlooked in the forecasting literature, not only enables us to consider testing in a multivariate setting but also increases the test power. As a result, we can tailor the choice of the critical values for each series not only to the in-sample properties of each series but also to how the series for forecast errors covary. |
| Keywords: | Forecast Breaks, Statistical Decision Making, Central Banking |
| JEL: | C53 E47 E58 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:cdf:wpaper:2018/24&r=for |
| By: | Francis X. Diebold (Department of Economics, University of Pennsylvania); Minchul Shin (Department of Economics, University of Illinois) |
| Abstract: | Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found superiority of simple-average combinations, we propose LASSO-based procedures that select and shrink toward equal combining weights. We then provide an empirical assessment of the performance of our "egalitarian LASSO" procedures. The results indicate that simple averages are highly competitive, and that although out-of-sample RMSE improvements on simple averages are possible in principle using our methods, they are hard to achieve in real time, due to the intrinsic difficulty of small-sample real-time cross validation of the LASSO tuning parameter. We therefore propose alternative direct combination procedures, most notably "best average" combination, motivated by the structure of egalitarian LASSO and the lessons learned, which do not require choice of a tuning parameter yet outperform simple averages. |
| Keywords: | Forecast combination, forecast surveys, shrinkage, model selection, LASSO, regularization |
| JEL: | C53 |
| Date: | 2017–08–20 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:17-017&r=for |
| By: | Ramis Khabibullin (Bank of Russia, Russian Federation); Alexey Ponomarenko (Bank of Russia, Russian Federation); Sergei Seleznev (Bank of Russia, Russian Federation) |
| Abstract: | We develop a stock-flow-consistent agent-based model that comprises a realistic mechanism of money creation and parametrize it to fit actual data. The model is used to make out-of-sample projections of broad money and credit developments under the commencement/termination of foreign reserve accumulation by the Bank of Russia. We use direct forecasts from the agent-based model as well as the two-step approach, which implies the use of artificial data to pre-train the Bayesian vector autoregression model. We conclude that the suggested approach is competitive in forecasting and yields promising results. |
| Keywords: | Money supply, foreign exchange reserves, forecasting, agent-based model, Russia. |
| JEL: | C53 C63 E51 E58 F31 G21 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:bkr:wpaper:wps37&r=for |
| By: | Jin-Kyu Jung; Manasa Patnam; Anna Ter-Martirosyan |
| Abstract: | Forecasting macroeconomic variables is key to developing a view on a country's economic outlook. Most traditional forecasting models rely on fitting data to a pre-specified relationship between input and output variables, thereby assuming a specific functional and stochastic process underlying that process. We pursue a new approach to forecasting by employing a number of machine learning algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true relationship between input and output variables. We apply the Elastic Net, SuperLearner, and Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and emerging economies and find that these algorithms can outperform traditional statistical models, thereby offering a relevant addition to the field of economic forecasting. |
| Date: | 2018–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:18/230&r=for |