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...

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...

We propose a new approach to sample unobserved states conditional on available data in (conditionally) linear unobserved component models when some of the observations are missing. The approach is based on the precision matrix of the states and model variables, which is sparse and banded in many...

The purpose of this paper is to compare the accuracy of the three types of models: Autoregressive Integrated Moving Average (ARIMA) models, Holt-Winters models and Neural Network Auto-Regressive (NNAR) models in forcasting the Harmonized Index of Consumer Prices (HICP) for the countries of...

We study alternative models for capturing abrupt structural changes (level shifts) in a times series. The problem is confounded by the presence of transient outliers. We compare the performance of non-Gaussian time-varying parameter models and multiprocess mixture models within a Monte Carlo...

In this chapter, a procedure is presented to use the bootstrap in choosing the best approximation in terms of forecasting performance for the equivalent state-space representation of a vector autoregressive model. It is found that the proposed procedure, which uses each approximant's forecasting...

We propose a new information criterion for impulse response function matching estimators (IRFMEs) of the structural parameters of dynamic stochastic general equilibrium (DSGE) macroeconomic models. An advantage of our procedure is that it allows researchers to select the impulse responses that...