Fiscal Policy in the BRICs

This paper assesses the macroeconomic impact of fiscal policy shocks for four key emerging market economies - Brazil, Russia, India and China (BRICs) – using a Bayesian Structural Vector Auto-Regressive (BSVAR) approach, a Sign-Restrictions Vector Auto-Regressive framework and a Panel Vector Auto-Regressive (PVAR) model. To get a deeper understanding of the government’s behaviour, we also estimate fiscal policy rules using a Fully Simultaneous System of Equations and analyze the importance of nonlinearity using a smooth transition (STAR) model. Drawing on quarterly frequency data, we find that government spending shocks have strong Keynesian effects for this group of countries while, in the case of government revenue shocks, a tax hike is harmful for output. This suggests that there is no evidence in favour of ‘expansionary fiscal contraction’ in the context of emerging economies where spending policies are largely pro-cyclical. Our findings also show that considerations about growth (in the case of China), exchange rate and inflation (for Brazil and Russia) and commodity prices (in India) drive the nonlinear response of fiscal policy to the dynamics of the economy. All in all, our results are consistent with the idea that fiscal policy can be a powerful stabilization tool and can provide an important short-term economic boost for emerging markets, in particular, in the context of severe downturns as in most recent financial turmoil.fiscal policy, emerging markets, fully simultaneous system of equations, sign-restrictions VAR, smooth transition regression model

Fiscal policy in the BRICs

This paper assesses the macroeconomic impact of fiscal policy shocks for four key emerging market economies - Brazil, Russia, India and China (BRICs) – using a Bayesian Structural Vector Auto-Regressive (BSVAR) approach, a Sign-Restrictions Vector Auto-Regressive framework and a Panel Vector Auto-Regressive (PVAR) model. To get a deeper understanding of the government’s behaviour, we also estimate fiscal policy rules using a Fully Simultaneous System of Equations and analyze the mportance of nonlinearity using a smooth transition (STAR) model. Drawing on quarterly frequency data, we find that government spending shocks have strong Keynesian effects for this group of countries while, in the case of government revenue shocks, a tax hike is harmful for output. This suggests that there is no evidence in favour of ‘expansionary fiscal contraction’ in the context of emerging economies where spending policies are largely pro-cyclical. Our findings also show that considerations about growth (in the case of China), exchange rate and inflation (for Brazil and Russia) and commodity prices (in India) drive the nonlinear response of fiscal policy to the dynamics of the economy. All in all, our results are consistent with the idea that fiscal policy can be a powerful stabilization tool and can provide an important short-term economic boost for emerging markets, in particular, in the context of severe downturns as in most recent financial turmoil.Fundação para a Ciência e a Tecnologia (FCT

Multivariate Contemporaneous-Threshold Autoregressive Models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific innovation covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.Nonlinear autoregressive model; Smooth transition; Stability; Threshold.

Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI

We propose generative artificial intelligence to measure systemic risk in the global markets of sovereign debt and foreign exchange. Through a comparative analysis, we explore three novel models to the econòmics literature and integrate them with traditional factor models. These models are: Time Variational Autoencoders, Time Generative Adversarial Networks, and Transformer-based Time-series Generative Adversarial Networks. Our empirical results provide evidence in support of the Variational Autoencoder. Results here indicate that both the Credit Default Swaps and foregin exchange markets are susceptible to systemic risk, with a historically high probability of distress observed by the end of 2022, as measured by both the Joint Probability of Distress and the Expected Proportion of Markets in Distress. Our results provide insights for governments in both developed and developing countries, since the realistic counterfactual scenarios generated by the AI, yet to occur in global markets, underscore the potential worst-case scenarios that may unfold if systemic risk materializes. Considering such scenarios is crucial when designing macroprudential policies aimed at preserving financial stability and when measuring the effectiveness of the implemented policies

Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI

We propose generative artificial intelligence to measure systemic risk in the global markets of sovereign debt and foreign exchange. Through a comparative analysis, we explore three novel models to the economics literature and integrate them with traditional factor models. These models are: Time Variational Autoencoders, Time Generative Adversarial Networks, and Transformer-based Time-series Generative Adversarial Networks. Our empirical results provide evidence in support of the Variational Autoencoder. Results here indicate that both the Credit Default Swaps and foreign exchange markets are susceptible to systemic risk, with a historically high probability of distress observed by the end of 2022, as measured by both the Joint Probability of Distress and the Expected Proportion of Markets in Distress. Our results provide insights for governments in both developed and developing countries, since the realistic counterfactual scenarios generated by the AI, yet to occur in global markets, underscore the potential worst-case scenarios that may unfold if systemic risk materializes. Considering such scenarios is crucial when designing macroprudential policies aimed at preserving financial stability and when measuring the effectiveness of the implemented policies

Multivariate contemporaneous-threshold autoregressive models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific innovation covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Common Trends and Common Cycles in Canadian Sectoral Output

The authors examine evidence of long- and short-run co-movement in Canadian sectoral output data. Their framework builds on a vector-error-correction representation that allows them to test for and compute full-information maximum-likelihood estimates of models with codependent cycle restrictions. They find that the seven sectors under consideration contain five common trends and five codependent cycles and use their estimates to obtain a multivariate Beveridge- Nelson decomposition to isolate and compare the common components. A forecast error variance decomposition indicates that some sectors, such as manufacturing and construction, are subject to persistent transitory shocks, whereas other sectors, such as financial services, are not. The authors also find that imposing common feature restrictions leads to a non-trivial gain in the ability to forecast both aggregate and sectoral output. Among the main conclusions is that manufacturing, construction, and the primary sector are the most important sources of business cycle fluctuations for the Canadian economy.