This paper uses real-time data for the U.S. to estimate out-of-sample forecast uncertainty about the Federal Funds Rate. By combining a Taylor rule with an unobserved components model of economic fundamentals I separate forecast uncertainty into economically interpretable components that...

Using real-time data I estimate out-of-sample forecast uncertainty about the Federal Funds Rate. Combining a Taylor rule with a model of economic fundamentals I disentangle economically interpretable components of forecast uncertainty: uncertainty about future economic conditions and uncertainty...

We use a Taylor rule with time-varying policy coefficients in combination with an unobserved components model for the output gap to estimate the uncertainty about future values of the Federal Funds Rate. The model makes it possible to separate ex-ante interest rate uncertainty into three...

This paper analyzes the effects of oil price and monetary shocks on the Iranian housing market in a Bayesian SVAR framework. The prior information for the contemporaneous identification of the SVAR model is derived from standard economic theory. To deal with uncertainty in the identification...

This paper analyzes the performance of central banks in 27 inflation targeting countries by examining their success in achieving their explicit inflation targets. For this purpose, we decompose the inflation gap, the difference between actual inflation and inflation target, into predictable and...

Stablecoins are a pivotal and debated topic within decentralized finance (DeFi), attracting significant interest from researchers, investors, and crypto-enthusiasts. These digital assets are designed to offer stability in the volatile cryptocurrency market, addressing key challenges in...

This paper investigates the economic importance of nonparametrically/semiparametrically modelling the shape and the change in the unknown distribution of returns in portfolio allocation problems from a Bayesian perspective. Besides parametric multivariate GARCH (MGARCH) benchmark models for...

The ability of Google Trends data to forecast the number of new daily cases and deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the computations of lag correlations between confirmed cases and Google data, Granger causality tests, and an out-of-sample...

Sergey Aivazian was the head of my department at the Moscow School of Economics, but he was much more than that. He played an important role in my life, and he contributed to my studies devoted to copula modelling. This small memoir reports how this amazingly polite and smart scientist helped me...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...