Determining the number of factors after stationary univariate transformations

A very common practice when extracting factors from non-stationary multivariate timeseries is to differentiate each variable in the system. As a consequence, the ratiobetween variances and the dynamic dependence of the common and idiosyncraticdifferentiated components may change with respect to the original components. In thispaper, we analyze the effects of these changes on the finite sample properties of somepopular procedures to determine the number of factors. In particular, we consider theinformation criteria of Bai and Ng (2002), the edge distribution of Onastki (2010) andthe ratios of eigenvalues proposed by Ahn and Horenstein (2013). The performance ofthese procedures when implemented to differentiated variables depend on both theratios between variances and dependences of the differentiated factor and idiosyncraticnoises. Furthermore, we also analyze the role of the number of factors in the originalnon-stationary system as well as of its temporal and cross-sectional dimensions.Acknowledgements: Financial support from the Spanish Ministry of Education and Science projects ECO2012-32401 and ECO2012-32854 is acknowledged by the second and third authors, respectivel

Risk-sharing among European Countries

This technical report details the results of risk sharing in the EU country by country. The great recession and the subsequent sovereign debt crisis in Europe have shown an asymmetric behavior of the different member countries of the EU, also with regards of risk sharing. We provide country specific measures decomposing risk sharing as that obtained via the capital markets, international transfers and savings or the credit markets channel. Afterwords, we use a mean group estimator to measure average risk sharing for the group of countries. This can help to identify where risk sharing is working and through which channels.JRC.B.1-Finance and Econom

Economic activity and C02 emissions in Spain

Carbon dioxide (CO2) emissions, largely by-products of energy consumption, account for the largest share of greenhouse gases (GHG). The addition of GHG to the atmosphere disturbs the earth's radiative balance, leading to an increase in the earth's surface temperature and to related effects on climate, sea level rise, ocean acidification and world agriculture, among other effects. Forecasting and designing policies to curb CO2 emissions globally is gaining interest. In this paper, we look at the relationship between CO2 emissions and economic activity using Spanish data from 1964 to 2020. We consider a structural (contemporaneous) equation between selected indicators of economic activity and CO2 emissions, that we further augment with dynamic common factors extracted from a large macroeconomic database. We show that the way the common factors are extracted is crucial to exploit their information content. In particular, when using standard methods to extract the common factors from large data sets, once private consumption and maritime transportation are considered, the information contained in the macroeconomic data set has only negligible explanatory power for emissions. However, if we extract the common factors oriented towards CO2 emissions, they add valuable information not contained in the individual economic indicators

JRC.B1 contribution to the SWD on the Movement of Capital and the Freedom of Payments

In the context of the institutional support to DG FISMA, JRC.B1 contributed to the 2018 Commission Staff Working Document on the Movement of Capital and the Freedom of Payments. JRC.B1 contribution included: (i) the analysis of home bias (tendency to invest in domestic financial assets); (ii) the analysis of diversification of cross-border investments and (iii) the estimation of the country specific degree of risk sharing for EU28 (risk sharing is the possibility to use cross-border capital markets to smooth domestic shock). JRC.B1 contribution appears in sections 2.5 and 2.6, and in Appendix III and IV.JRC.B.1-Finance and Econom

Estimating non-stationary common factors : implications for risk sharing

In this paper, we analyze and compare the finite sample properties of alternative factor extraction procedures in the context of non-stationary Dynamic Factor Models (DFMs). On top of considering procedures already available in the literature, we extend the hybrid method based on the combination of principal components and Kalman filter and smoothing algorithms to non-stationary models. We show that if the idiosyncratic noises are stationary, procedures based on extracting the factors using the non-stationary original series work better than those based on differenced variables. We apply the methodology to the analysis of cross-border risk sharing by fitting non-stationary DFM to aggregate Gross Domestic Product and consumption of a set of 21 industrialized countries from the Organization for Economic Co-operation and Development (OECD). The goal is to check if international risk sharing is a short- or long-run issue.Financial support from the Spanish Government Projects ECO2015-70331-C2-1-R and ECO2015-70331-C2-2-R (MINECO/FEDER) is gratefully acknowledged. This paper was started while Pilar Poncela was still at Universidad Autónoma de Madrid. We are very grateful for the detailed comments of an anonymous referee which have been very useful to improve the presentation of this paper. The views expressed in this paper are those of the authors and should not be attributed neither to the European Commission nor to INEGI

Effects of extreme temperature on the European equity market

The increasing frequency and severity of extreme temperatures are potential threats to financial stability. Indeed, physical risk related to these extreme phenomena can affect the whole financial system and, in particular, the equity market. In this study,we analyze the impact of extreme temperature exposure on firms' performance in Europe over the XXI century. We show that extreme temperatures can affect firms' profitability depending on their industry and the quarter of the year. Our results are of interest for both investors operating in the equity market and for regulators in charge of securing financial stability

New Risk Sharing Channels in OECD Countries: a Heterogeneous Panel VAR

We aim to improve upon the existing empirical literature on international risk sharing under three dimensions. First, we generalize dynamic multi-equation approaches to the estimation of risk sharing channels, by adopting a Heterogeneous Panel VAR model. Within this framework, the coefficients representing the extent of risk sharing achieved through the different mechanisms are allowed to vary across countries. Second, we introduce two new risk sharing channels – namely, government consumption and the real exchange rate (that we further decompose into relative prices and the nominal exchange rate) – which allow us to investigate the role of fiscal policy and international price adjustments in the absorption of macroeconomic shocks. Third, we establish a better link between the “channels” empirical model and a theoretical formulation of the risk sharing condition which allows for PPP violations. Our empirical analysis, for a set of 21 OECD countries over 1960-2016, contributes to identifying the geographical structure and dynamics of risk sharing channels and to describing their evolution in the latest half-century. For the OECD sample as a whole, we confirm through 2016 the strong smoothing role played by credit markets and the small degree of risk sharing achieved through factor incomes. Interestingly, government consumption tends to have a dis-smoothing effect, due to its counter-cyclical movements. Another noteworthy result is the negative risk sharing effect of the real exchange rate, driven by the dis-smoothing role played by the movements of the nominal exchange rate, only partially offset by relative price adjustments. The evolution of these risk sharing mechanisms is diverse, but the most important channels – namely credit markets and real exchange rate adjustments – exhibit slightly positive trends for the first half of the period, negative trends afterwards, and a recovery in more recent years. Our results demonstrate that the extent of risk sharing is strikingly different across countries, especially if we take into account valuation effects through the real exchange rate. Even considering only traditional risk sharing channels, the country-specific magnitude of risk sharing on impact ranges from around 15% to over 80%. In addition, dynamics are also quite diverse across countries; for example, risk sharing through credit markets, while quite effective on impact, provokes dis-smoothing for about two thirds of the countries from the second year onwards. Our approach is of particular interest for policy makers, as it allows identifying the strengths and the weaknesses of the institutional and behavioral risk sharing mechanisms at work in different countries.JRC.B.1-Finance and Econom

Factor extraction using Kalman filter and smoothing: this is not just another survey

Dynamic Factor Models, which assume the existence of a small number of unobservedlatent factors that capture the comovements in a system of variables, are the main "bigdata" tool used by empirical macroeconomists during the last 30 years. One importanttool to extract the factors is based on Kalman lter and smoothing procedures that cancope with missing data, mixed frequency data, time-varying parameters, non-linearities,non-stationarity and many other characteristics often observed in real systems of economicvariables. This paper surveys the literature on latent common factors extracted using Kalmanfilter and smoothing procedures in the context of Dynamic Factor Models. Signal extractionand parameter estimation issues are separately analyzed. Identi cation issues are also tackledin both stationary and non-stationary models. Finally, empirical applications are surveyedin both cases

Estimating non-stationary common factors: Implications for risk sharing

In this paper, we analyze and compare the finite sample properties of alternative factor extraction procedures in the context of non-stationary Dynamic Factor Models (DFMs). On top of considering procedures already available in the literature, we extend the hybrid method based on the combination of principal components and Kalman filter and smoothing algorithms to non-stationary models. We show that, if the idiosyncratic noises are non-stationary, procedures based on extracting the factors using the non-stationary original series work better than those based on differenced variables. We apply the methodology to the analysis of cross-border risk-sharing fitting non-stationary DFM to aggregate GDP and consumption of the set of 21 OECD industrialized countries. The goal is to check if international risk sharing is a short or long-run issue.JRC.B.1-Finance and Econom

Markov-switching dynamic factor models in real time

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.JRC.B.1-Finance and Econom