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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.
Governments’ Payment Discipline
This paper considers the impact of changes in the payment discipline of governments on the private sector. We argue that increased delays in public payments can affect private sector liquidity and profits and hence ultimately economic growth. We test this prediction empirically for European Union countries using two complementary approaches. First, we use annual panel data, including a newly constructed proxy for government arrears. We find that payment delays and to some extent estimated arrears lead to a higher likelihood of bankruptcy, lower profits, and lower economic growth. However, while this approach allows a broad set of variables to be included, it restricts the number of time periods. We therefore complement it with a Bayesian VAR approach on quarterly data for selected countries faced with significant payment delays. We again find that the likelihood of bankruptcies rises when governments increase the average payment period.
Bayesian Econometrics
Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
Essays in Honour of Fabio Canova
Both parts of Volume 44 of Advances in Econometrics pay tribute to Fabio Canova for his major contributions to economics over the last four decades.
Global Financial Stability Report, April 2017
Financial stability has continued to improve since the October 2016 Global Financial Stability Report (GFSR). Economic activity has gained momentum, as outlined in the April 2017 World Economic Outlook (WEO), amid broadly accommodative monetary and financial conditions, spurring hopes for reflation. Chapter 2 analyzes the potential long-term impact of a scenario of sustained low growth and low real and nominal rates for the business models of financial institutions and the products offered by the financial sector. Chapter 3 examines whether countries still retain influence over their domestic financial conditions in a globally integrated financial system. The chapter develops financial conditions indices that make it possible to compare a large set of advanced and emerging market economies.
Bayesian Econometric Methods
Illustrates Bayesian theory and application through a series of exercises in question and answer format.
The Term Structure of Growth-at-Risk
Using panel quantile regressions for 11 advanced and 10 emerging market economies, we show that the conditional distribution of GDP growth depends on financial conditions, with growth-at-risk (GaR)—defined as growth at the lower 5th percentile—more responsive than the median or upper percentiles. In addition, the term structure of GaR features an intertemporal tradeoff: GaR is higher in the short run; but lower in the medium run when initial financial conditions are loose relative to typical levels, and the tradeoff is amplified by a credit boom. This shift in the growth distribution generally is not incorporated when solving dynamic stochastic general equilibrium models with macrofinancial linkages, which suggests downside risks to GDP growth are systematically underestimated.
Beyond Energy: Inflationary Effects of Metals Price Shocks in Production Networks
We examine the role of metals as economic inputs by using a production network model, calibrated for various countries using input-output (I-O) tables. Empirically, we employ local projections to study how metal shocks influence inflation, testing country-level heterogeneity in the sensitivity to these shocks. Our findings indicate that metals price shocks have significant and persistent effects on core and headline inflation, with particularly pronounced effects on countries that are highly exposed to metals in their production networks. This is in contrasts to oil supply shocks, which predominantly affect headline inflation. A shift of the global economy towards a higher relative metals intensity due to the energy transition could lead to commodity price shocks increasingly influencing core rather than headline inflation. This could make commodity price shocks less visible on impact but more persistent. Central banks should consider this shift when assessing inflation dynamics and risks.
Bayesian Econometrics
Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb–Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.
Shock from Graying
Abstract Empirical evidence is mounting that, in advanced economies, changes in monetary policy have a more benign impact on the economy—given better anchored inflation expectations and inflation being less responsive to variation in unemployment—compared to the past. We examine another aspect that could explain this empirical finding, namely the demographic shift to an older society. The paper first clarifies potential transmission channels that could explain why monetary policy effectiveness may moderate in graying societies. It then uses Bayesian estimation techniques for the U.S., Canada, Japan, U.K., and Germany to confirm a weakening of monetary policy effectiveness over time with regards to unemployment and inflation. After proving the existence of a panel co-integration relationship between ageing and a weakening of monetary policy, the study uses dynamic panel OLS techniques to attribute this weakening of monetary policy effectiveness to demographic changes. The paper concludes with policy implications.
