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We evaluate different approaches for using monthly indicators to predict Chinese GDP for the current and the next quarter (‘nowcasts’ and ‘forecasts’, respectively). We use three types of mixed-frequency models, one based on an economic activity indicator (Liu et al., 2007), one based on averaging over indicator models (Stock and Watson, 2004), and a static factor model (Stock and Watson, 2002).

We evaluate forecasts for the euro area in data-rich and ‘data-lean' environments by comparing three different approaches: a simple PMI model based on Purchasing Managers' Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data from national economies (pseudo-real time data).

Forecasts of global economic activity and inflation are important inputs when conducting monetary policy in small open economies such as Canada. As part of the Bank of Canada's broad agenda to augment its short-term forecasting tools, the author constructs simple mixed-frequency forecasting equations for quarterly global output, imports, and inflation using the monthly global Purchasing Managers Index (PMI).

The forecast of world economic growth plays a key role in the conduct of Canadian monetary policy. In this context, the authors study the usefulness of the monthly Purchasing Managers’ Indexes (PMIs) in predicting short-term real GDP growth in the euro area, Japan, the United Kingdom, and China, as well as in the world economy.

The good forecasting performance of factor models has been well documented in the literature. While many studies focus on a very limited set of variables (typically GDP and inflation), this study evaluates forecasting performance at disaggregated levels to examine the source of the improved forecasting accuracy, relative to a simple autoregressive model. We use the latest revision of over 100 U.S. time series over the period 1974-2009 (monthly and quarterly data).

To complement its existing set of tools to analyze and forecast developments in the global economy, the Bank of Canada recently developed a version of the Global Projection Model (GPM) jointly with staff at the International Monetary Fund.

Weak identification is likely to be prevalent in multi-equation macroeconomic models such as in dynamic stochastic general equilibrium setups. Identification difficulties cause the breakdown of standard asymptotic procedures, making inference unreliable.

The Bank of Canada's version of the Global Economy Model (BoC-GEM) is derived from the model created at the International Monetary Fund by Douglas Laxton (IMF) and Paolo Pesenti (Federal Reserve Bank of New York and National Bureau of Economic Research).

This paper revisits Canada's pioneering experience with floating exchange rate over the period 1950–1962. It examines whether the floating rate was the best option for Canada in the 1950s by developing and estimating a New Keynesian small open economy model of the Canadian economy.

This paper examines the ability of linear and nonlinear models to replicate features of real Canadian GDP. We evaluate the models using various business-cycle metrics.