Publications
This chapter presents a survey of some recent methods used in economics and finance to account for cyclical dependence and account for their multifaced dynamics: nonlinearities, extreme events, asymmetries, non-stationarity, time-varying moments. To circumvent the caveats of the standard spectral analysis, new tools are now used based on copula spectrum, quantile spectrum and Laplace periodogram in both non-parametric and parametric contexts. The chapter presents a comprehensive overview of both theoretical and empirical issues as well as a computational approach to explain how the methods can be implemented using the R Package.
The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models.
The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.
This paper discusses the theoretical choice of exchange rate regimes in Sub-Saharan African countries that are facing external vulnerabilities. To reduce instability, policymakers choose among promoting external competitiveness using a real anchor, lowering the burden of foreign debt using a nominal anchor or using a policy mix of both anchors. We observe that these countries tend to adopt mixed anchor policies. We solve a state space model to explain the determinants of and the strategy behind this policy. We find that the mixed targeting policy is a two-step strategy: First, monetary authorities choose the degree of nominal exchange rate flexibility according to the velocity of money, trade openness, foreign debt, degree of exchange rate pass-through and exchange rate target zone. Second, authorities seek to stabilize the real exchange rate depending on the degree of competition in the domestic goods market and the degree of foreign exchange intervention. We conclude with regime-switching estimations to provide empirical evidence of how these economic fundamentals influence exchange rate policy in Sub-Saharan Africa.
We assess the role of demand noise (excessive optimism or pessimism about demand) together with supply noise (excessive optimism or pessimism about supply). To do so, we propose a methodology to decompose business cycles into supply, demand, supply noise and demand noise shocks, using a structural vector autoregression model. Key to our identification of both supply noise and demand noise is the use of sign restrictions on survey expectation errors about output growth and about inflation. We show that demand-related noise shocks have a negative effect on output and contribute substantially to its fluctuations. Monetary policy and private information seem to play a key role in the transmission of demand noise shocks.
The COVID-19 pandemic and government intervention such as lockdowns may severely affect people’s mental health. While lockdowns can help to contain the spread of the virus, they may result in substantial damage to population well-being. We use Google Trends data to test whether COVID-19 and the associated lockdowns implemented in Europe and America led to changes in well-being related topic search-terms. Using difference-in-differences and a regression discontinuity design, we find a substantial increase in the search intensity for boredom in Europe and the US. We also found a significant increase in searches for loneliness, worry and sadness, while searches for stress, suicide and divorce on the contrary fell. Our results suggest that people’s mental health may have been severely affected by the pandemic and lockdown.
Currently, countries across the world are applying policies designed to combat the COVID-19 pandemic, such as lockdowns, international travel restrictions, subsectoral closures, and adjustments in public transportation. Although these restrictions can be effective in controlling the epidemiological dynamics, they also need to be assessed in terms of their acceptability by populations. The preferences of populations should matter, particularly after months of efforts, and the new requirements of lockdowns in several European countries despite these efforts.
To what extent do childhood experiences continue to affect adult wellbeing over the life course? Previous work on this link has been carried out either at one particular adult age or for some average over adulthood. We here use two British birth-cohort datasets (the 1958 NCDS and the 1970 BCS) to map out the time profile of the effect of childhood experiences on adult outcomes, including life satisfaction. We find that the effects of many aspects of childhood do not fade away over time but are rather remarkably stable. In both birth-cohorts, child non-cognitive skills are the strongest predictors of adult life satisfaction at all ages. Of these, emotional health is the strongest. Childhood cognitive performance is more important than good conduct in explaining adult life satisfaction in the earlier NCDS cohort, whereas this ranking is inverted in the more recent BCS.
This study explores whether an oath to honesty can reduce both shirking and lying among crowd-sourced internet workers. Using a classic coin-flip experiment, we first confirm that a substantial majority of Mechanical Turk workers both shirk and lie when reporting the number of heads flipped. We then demonstrate that lying can be reduced by first asking each worker to swear voluntarily on his or her honor to tell the truth in subsequent economic decisions. Even in this online, purely anonymous environment, the oath significantly reduced the percent of subjects telling “big” lies (by roughly 27%), but did not affect shirking. We also explore whether a truth-telling oath can be used as a screening device if implemented after decisions have been made. Conditional on flipping response, MTurk shirkers and workers who lied were significantly less likely to agree to an ex-post honesty oath. Our results suggest oaths may help elicit more truthful behavior, even in online crowd-sourced environments.
This paper assesses the effectiveness of risk sharing mechanisms in Europe by breaking down the factor income components into their sub-components, and aims to further examine whether financial integration and international portfolio diversification boosts or dampens risk sharing. Using a panel of European countries, we compare the years before and after the 2008 financial crisis. We extend the literature by properly taking into account the heterogeneity (in both country and time dimensions) in the panel through new econometric models. Our results show that financial income has become a major channel of risk sharing in recent years and that a higher integration in the bond and equity markets significantly improves risk sharing in the long term.
Beta coefficients are the cornerstone of asset pricing theory in the CAPM and multiple factor models. This chapter proposes a review of different time series models used to estimate static and time-varying betas, and a comparison on real data. The analysis is performed on the USA and developed Europe REIT markets over the period 2009–2019 via a two-factor model. We evaluate the performance of the different techniques in terms of in-sample estimates as well as through an out-of-sample tracking exercise. Results show that dynamic models clearly outperform static models and that both the state space and autoregressive conditional beta models outperform the other methods.
