Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two...

The role that the price of oil plays in economic analysis in central banks as well as in financial markets has evolved over time. Oil is not seen anymore just as a input to production but also as a barometer of global economic activity as well as a financial asset. A high frequency structural...

Time-varying exchange rate pass-through effects to domestic prices under fixed euro exchange rate perspective represent one of the most challenging implications of the common currency. The problem is even more crucial when examining crisis related redistributive effects associated with relative...

This study analyzes the impact of supply and demand shocks on income and price inequality in the economy using data from Korea. First, supply and demand shocks are identified from output and price data in Korea using the methods found in Blanchard and Quah (1989) and Bashar (2011). In addition,...

In this paper we present various techniques to estimate Sri Lanka's potential output and output gap, including statistical and model-based approaches. Compared to conventional statistical filters that rely exclusively on information in a single series, the model-based approaches allow potential...

We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated...

Expectations affect economic decisions, and inaccurate expectations are costly. Expectations can be wrong due to either bias (systematic mistakes) or noise (unsystematic mistakes). We develop a framework for quantifying the level of noise in survey expectations. The method is based on the...

Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to...