This paper proposes a measure of real-time inflation expectations based on metadata, i.e., data about data, constructed from internet search queries performed on the search engine Google. The forecasting performance of the Google Inflation Search Index (GISI) is assessed relative to 37 other...

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16...

An empirical small labor market model for the Czech Republic is estimated in the state-space framework. Its purpose is joint modeling of the labor force, employment, wages, hours worked, output, and the GDP deflator in a consistent “structural” framework suitable for short-run forecasting....

In this paper we assess whether monetary variables convey marginal information on the state of the Italian economy, taking as a benchmark the forecasting errors generated by the quarterly model used by the Bank of Italy in the 1990s. We follow two alternative approaches. First we map monetary...

This paper is the ?rst one to analyze the ability of linear and nonlinear monetary policy rule specifications as well as nonparametric and semiparametric models in forecasting the nominal interest rate setting that describes the South African Reserve Bank (SARB) policy decisions. We augment the...

This paper is the first one to: (i) provide in-sample estimates of linear and nonlinear Taylor rules augmented with an indicator of financial stability for the case of South Africa, (ii) analyse the ability of linear and nonlinear monetary policy rule specifications as well as nonparametric and...

We construct Bayesian vector autoregressive (BVAR) models optimized by the Posterior Information Criterion (PIC), in which hyper-parameters are data-determined in the same way as the lag length and trend order. We also assess the performance of the selected models by one-step ahead forecasts...

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a dataset consisting of monthly data on global stock indices the BVAR model inherently incorporates co-movements in the stock markets. The...