Sequence-space models are becoming increasingly popular in macroeconomics, especially in the heterogeneous-agent literature. However, the econometric toolkit for users of these models remains less developed than that available for traditional state-space methods. This note introduces an...

Forecasting hotel occupancy during external shocks is particularly challenging due to their disruptive effects. This study develops a forecasting framework that integrates multisource data using a time-varying parameter state-space model (TVP-SSM). In this framework, search engine data (SED) are...

Point forecasts of score-driven models have been shown to behave at par with those of state-space models under a variety of circumstances. We show, however, that density rather than point forecasts of plain-vanilla score-driven models substantially underperform their state-space counterparts in...

This study addresses the challenge of estimating the NAIRU in developing countries, focusing on Pakistan from 1972 to 2022. Due to data constraints in such contexts, it introduces robust methodologies, including Hodrick-Prescott and Kalman filters within a univariate framework, to derive NAIRU...

The COVID-19 pandemic and the implementation of strict lockdown measures have significantly impacted various dimensions of the global economy. This study examines the impact of COVID-19 and lockdown stringency on exchange rate volatility in India using three core variables, i.e., COVID-19 cases,...

This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state...

We examine technology ETF and uncertainty index (VIX, GVZ, and OVZ) spillover dynamics and quantile frequency interconnectedness across market states. This study is the first to use quantile-frequency spillover, quadruple wavelet coherence, and wavelet quantile correlation methodologies to...

This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns (CR) by employing various wavelet techniques. We concentrate on the recently published cryptocurrency uncertainty index (UCRY) and the top eight cryptocurrencies by market capitalization from...

This study estimates the long-run nominal equilibrium exchange rate (NEER) in Bolivia for the period 1990- 2024 using an innovative methodology that models it as an unobservable (latent) variable, grounded in purchasing power parity (PPP) theory and the hypothesis of cross-country productivity...

Climate change and environmental degradation threaten the world and global economic conditions. As one of countries' most important economic components, the financial sector might be an effective tool for reducing and even reversing environmental degradation. The financial sector can affect...

The energy-intensive nature of urban transport systems plays a major role in environmental decline, emphasizing the urgent need to foster sustainable energy adoption. This study takes Macao as an example and uses wavelet coherence to analyze the relationship between transportation and energy...

We estimate a New Keynesian model that allows endogenous transitions between a target equilibrium, with inflation fluctuating around the central bank's target and interest rates typically positive, and a low-inflation equilibrium, where the effective lower bound binds and de-anchored...

This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression...

The paper compares two forecasts of Slovak GDP, the first with high-frequency data and the second without them. We utilize the last observation from the economic activity index acting as a short-term GDP forecast. We use data from 2000 to 2024 in weekly frequencies and have 27 variables related...

This paper introduces a novel approach to simulation smoothing for nonlinear and non-Gaussian state space models. It allows for computing smoothed estimates of the states and nonlinear functions of the states, as well as visualizing the joint smoothing distribution. The approach combines...

Point forecasts of score-driven models have been shown to behave at par with those of state-space models under a variety of circumstances. We show, however, that density rather than point forecasts of plain-vanilla score-driven models substantially underperform their state-space counterparts in...

The CDS-bond basis quantifies the difference in risk premia between credit default swap (CDS) and bond markets. It is hard to measure at the individual firm level given substantial missing-value problems (30%-100%) in either or both markets, even for highly liquid blue-chip financial firms. We...