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78 Results

Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis

Staff Working Paper 2013-25 Christiane Baumeister, Lutz Kilian, Xiaoqing Zhou
Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date.
August 15, 2013

CSI: A Model for Tracking Short-Term Growth in Canadian Real GDP

Canada’s Short-Term Indicator (CSI) is a new model that exploits the information content of 32 indicators to produce daily updates to forecasts of quarterly real GDP growth. The model is a data-intensive, judgment-free approach to short-term forecasting. While CSI’s forecasts at the start of the quarter are not very accurate, the model’s accuracy increases appreciably as more information becomes available. CSI is the latest addition to a wide range of models and information sources that the Bank of Canada uses, combined with expert judgment, to produce its short-term forecasts.
August 15, 2013

The Accuracy of Short-Term Forecast Combinations

This article examines whether combining forecasts of real GDP from different models can improve forecast accuracy and considers which model-combination methods provide the best performance. In line with previous literature, the authors find that combining forecasts generally improves forecast accuracy relative to various benchmarks. Unlike several previous studies, however, they find that, rather than assigning equal weights to each model, unequal weighting based on the past forecast performance of models tends to improve accuracy when forecasts across models are substantially different.
August 15, 2013

Big Data Analysis: The Next Frontier

The formulation of monetary policy at the Bank of Canada relies on the analysis of a broad set of economic information. Greater availability of immediate and detailed information would improve real-time economic decision making. Technological advances have provided an opportunity to exploit “big data” - the vast amount of digital data from business transactions, social media and networked computers. Big data can be a complement to traditional information sources, offering fresh insight for the monitoring of economic activity and inflation.

What Central Bankers Need to Know about Forecasting Oil Prices

Staff Working Paper 2013-15 Christiane Baumeister, Lutz Kilian
Forecasts of the quarterly real price of oil are routinely used by international organizations and central banks worldwide in assessing the global and domestic economic outlook, yet little is known about how best to generate such forecasts. Our analysis breaks new ground in several dimensions.

Forecasting with Many Models: Model Confidence Sets and Forecast Combination

Staff Working Paper 2013-11 Jon D. Samuels, Rodrigo Sekkel
A longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C5, C53

Short-Term Forecasting of the Japanese Economy Using Factor Models

Staff Working Paper 2012-7 Claudia Godbout, Marco J. Lombardi
While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis.

Real-Time Analysis of Oil Price Risks Using Forecast Scenarios

Staff Working Paper 2012-1 Christiane Baumeister, Lutz Kilian
Recently, there has been increased interest in real-time forecasts of the real price of crude oil. Standard oil price forecasts based on reduced-form regressions or based on oil futures prices do not allow consumers of forecasts to explore how much the forecast would change relative to the baseline forecast under alternative scenarios about future oil demand and oil supply conditions.
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