Pilar Poncela - Academia.edu (original) (raw)
Papers by Pilar Poncela
RePEc: Research Papers in Economics, Feb 1, 2012
We develop a twofold analysis of how the information provided by several economic indicators can ... more We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the "shortcut" of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.
International Journal of Forecasting, Jul 1, 2005
Fertility forecasting is the weak point of stochastic population forecasts. Changing trends accou... more Fertility forecasting is the weak point of stochastic population forecasts. Changing trends account for large forecasting errors even a few years ahead. On the other hand, fertility trends have been shown to be common to different European countries. This paper explores the possibility of improving forecasts by jointly modelling total fertility rate (TFR) trends within relatively homogeneous clusters of countries. We propose different varieties of non-stationary dynamic factor models applied to Southern European countries. The forecasting performance of the common factor models is compared to alternative univariate and multivariate forecasting methods using data for the period 1950-2000. Joint forecasts show forecasting gains in terms of root mean square error of prediction (RMSE), particularly for longer forecast horizons. This corroborates the convenience of modelling fertility jointly for population forecasting.
Social Science Research Network, 2023
In economics, Principal Components, its generalized version that takes into account heteroscedast... more In economics, Principal Components, its generalized version that takes into account heteroscedasticity, and Kalman lter and smoothing procedures are among the most popular procedures for factor extraction in the context of Dynamic Factor Models. This paper analyses the consequences on point and interval factor estimation of using these procedures when the idiosyncratic components are wrongly assumed to be cross-sectionally uncorrelated. We show that not taking into account the presence of cross-sectional dependence increases the uncertainty of point estimates of the factors. Furthermore, the Mean Square Errors computed using the usual expressions based on asymptotic approximations, are underestimated and may lead to prediction intervals with extremely low coverages.
arXiv (Cornell University), Jun 7, 2022
In this paper, we survey recent econometric contributions to measure the relationship between eco... more In this paper, we survey recent econometric contributions to measure the relationship between economic activity and climate change. Due to the critical relevance of these effects for the well-being of future generations, there is an explosion of publications devoted to measuring this relationship and its main channels. The relation between economic activity and climate change is complex with the possibility of causality running in both directions. Starting from economic activity, the channels that relate economic activity and climate change are energy consumption and the consequent pollution. Hence, we first describe the main econometric contributions about the interactions between economic activity and energy consumption, moving then to describing the contributions on the interactions between economic activity and pollution. Finally, we look at the main results on the relationship between climate change and economic activity. An important consequence of climate change is the increasing occurrence of extreme weather phenomena. Therefore, we also survey contributions on the economic effects of catastrophic climate phenomena.
International Journal of Forecasting, Oct 1, 2018
We extend the Markov-switching dynamic factor model to account for some of the specifi cities of ... more We extend the Markov-switching dynamic factor model to account for some of the specifi cities of the day-today monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefi ts of this extension and corroborate the results through several Monte Carlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.
International Journal of Forecasting, 2012
RePEc: Research Papers in Economics, May 1, 2017
In this paper, we analyze and compare the finite sample properties of alternative factor extracti... more 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, unless the idiosyncratic noise is non-stationary, procedures based on extracting the factors using the nonstationary original series work better than those based on differenced variables. The results are illustrated in an empirical application fitting non-stationary DFM to aggregate GDP and consumption of the set of 21 OECD industrialized countries. The goal is to check international risk sharing is a short or long-run issue.
RePEc: Research Papers in Economics, Feb 1, 2012
We develop a twofold analysis of how the information provided by several economic indicators can ... more We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the "shortcut" of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.
Journal of International Money and Finance
International Journal of Forecasting, Oct 1, 2013
She was invited to be a coeditor of the special issue of the International Journal of Forecasting... more She was invited to be a coeditor of the special issue of the International Journal of Forecasting ''Introduction to nonlinearities, business cycles, and forecasting''.
Practitioners do not always use research fi ndings, as the research is not always conducted in a ... more Practitioners do not always use research fi ndings, as the research is not always conducted in a manner relevant to real-world practice. This survey seeks to close the gap between research and practice in respect of short-term forecasting in real time. To this end, we review the most relevant recent contributions to the literature, examining their pros and cons, and we take the liberty of proposing some avenues of future research. We include bridge equations, MIDAS, VARs, factor models and Markov-switching factor models, all allowing for mixed-frequency and ragged ends. Using the four constituent monthly series of the Stock-Watson coincident index, industrial production, employment, income and sales, we evaluate their empirical performance to forecast quarterly US GDP growth rates in real time. Finally, we review the main results having regard to the number of predictors in factorbased forecasts and how the selection of the more informative or representative variables can be made.
Journal of Forecasting, Apr 4, 2014
This paper focuses on the effects of disaggregation on forecast accuracy for nonstationary time s... more This paper focuses on the effects of disaggregation on forecast accuracy for nonstationary time series using dynamic factor models. We compare the forecasts obtained directly from the aggregated series based on its univariate model with the aggregation of the forecasts obtained for each component of the aggregate. Within this framework (first obtain the forecasts for the component series and then aggregate the forecasts), we try two different approaches: (i) generate forecasts from the multivariate dynamic factor model and (ii) generate the forecasts from univariate models for each component of the aggregate. In this regard, we provide analytical conditions for the equality of forecasts. The results are applied to quarterly gross domestic product (GDP) data of several European countries of the euro area and to their aggregated GDP. This will be compared to the prediction obtained directly from modeling and forecasting the aggregate GDP of these European countries. In particular, we would like to check whether long-run relationships between the levels of the components are useful for improving the forecasting accuracy of the aggregate growth rate. We will make forecasts at the country level and then pool them to obtain the forecast of the aggregate. The empirical analysis suggests that forecasts built by aggregating the country-specific models are more accurate than forecasts constructed using the aggregated data.
Oxford University Press eBooks, Nov 19, 2015
In the context of dynamic factor models (DFM), it is known that, if the cross-sectional and time ... more In the context of dynamic factor models (DFM), it is known that, if the cross-sectional and time dimensions tend to in…nity, the Kalman …lter yields consistent smoothed estimates of the underlying factors. When looking at asymptotic properties, the cross-sectional dimension needs to increase for the …lter or stochastic error uncertainty to decrease while the time dimension needs to increase for the parameter uncertainty to decrease. In this paper, assuming that the model speci…cation is known, we separate the …nite sample contribution of each of both uncertainties to the total uncertainty associated with the estimation of the underlying factors. Assuming that the parameters are known, we show that, as far as the serial dependence of the idiosyncratic noises is not very persistent and regardless of whether their contemporaneous correlations are weak or strong, the …lter uncertainty is a non-increasing function of the cross-sectional dimension. Furthermore, in situations of empirical interest, if the cross-sectional dimension is beyond a relatively small number, the …lter uncertainty only decreases marginally. Assuming weak contemporaneous correlations among the serially uncorrelated idiosyncratic noises, we prove the consistency not only of smooth but also of real time …ltered estimates of the underlying factors in a simple case, extending the results to non-stationary DFM. In practice, the model parameters are unknown and have to be estimated, adding further uncertainty to the estimated factors. We use simulations to measure this uncertainty in …nite samples and show that, for the sample sizes usually encountered in practice when DFM are …tted to macroeconomic variables, the contribution of the parameter uncertainty can represent a large percentage of the total uncertainty involved in factor extraction. All results are illustrated estimating common factors of simulated time series.
Economia Politica, Dec 21, 2016
This paper analyzes oil price pass-through into inflation at disaggregate level for the euro area... more This paper analyzes oil price pass-through into inflation at disaggregate level for the euro area and its four main economies (France, Germany, Italy and Spain). The pattern of responses to oil price changes is quantitatively diverse across economies and across items of disaggregate inflation. Moreover, we suggest an alternative method to the direct calculation of aggregate effects on inflation given that indirect and second-round effects may offset the positive effects found in energy inflation and dissipate the effect on total inflation.
Journal of Forecasting, May 8, 2002
In this paper we present an extensive study of annual GNP data for five European countries. We lo... more In this paper we present an extensive study of annual GNP data for five European countries. We look for intercountry dependence and analyse how the different economies interact, using several univariate ARIMA and unobserved components models and a multivariate model for the GNP incorporating all the common information among the variables. We use a dynamic factor model to take account of the common dynamic structure of the variables. This common dynamic structure can be non-stationary (i.e. common trends) or stationary (i.e. common cycles). Comparisons of the models are made in terms of the root mean square error (RMSE) for one-step-ahead forecasts. For this particular group of European countries, the factor model outperforms the remaining ones.
Social Science Research Network, 2012
We develop a twofold analysis of how the information provided by several economic indicators can ... more We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the "shortcut" of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.
Social Science Research Network, 2012
We extend the Markov-switching dynamic factor model to account for some of the specifi cities of ... more We extend the Markov-switching dynamic factor model to account for some of the specifi cities of the day-today monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefi ts of this extension and corroborate the results through several Monte Carlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.
Empirical Economics, Sep 27, 2016
A very common practice when extracting factors from non-stationary multivariate time series is to... more A very common practice when extracting factors from non-stationary multivariate time series is to differentiate each variable in the system. As a consequence, the ratio between variances and the dynamic dependence of the common and idiosyncratic differentiated components may change with respect to the original components. In this paper, we analyze the effects of these changes on the finite sample properties of several procedures to determine the number of factors. In particular, we consider the informa
Computational Economics, Dec 12, 2018
In this paper, we analyze and compare the finite sample properties of alternative factor extracti... more 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 nonstationary DFM to aggregate Gross Domestic Product and consumption of a set of 21 industrialized countries from the Organization for Economic Cooperation and Development (OECD). The goal is to check if international risk sharing is a short-or long-run issue. Keywords Consumption smoothing • Non-stationary Dynamic Factor Models • Kalman filter • Principal components • Risk sharing 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.
Economics Letters, Sep 1, 2023
RePEc: Research Papers in Economics, Feb 1, 2012
We develop a twofold analysis of how the information provided by several economic indicators can ... more We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the "shortcut" of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.
International Journal of Forecasting, Jul 1, 2005
Fertility forecasting is the weak point of stochastic population forecasts. Changing trends accou... more Fertility forecasting is the weak point of stochastic population forecasts. Changing trends account for large forecasting errors even a few years ahead. On the other hand, fertility trends have been shown to be common to different European countries. This paper explores the possibility of improving forecasts by jointly modelling total fertility rate (TFR) trends within relatively homogeneous clusters of countries. We propose different varieties of non-stationary dynamic factor models applied to Southern European countries. The forecasting performance of the common factor models is compared to alternative univariate and multivariate forecasting methods using data for the period 1950-2000. Joint forecasts show forecasting gains in terms of root mean square error of prediction (RMSE), particularly for longer forecast horizons. This corroborates the convenience of modelling fertility jointly for population forecasting.
Social Science Research Network, 2023
In economics, Principal Components, its generalized version that takes into account heteroscedast... more In economics, Principal Components, its generalized version that takes into account heteroscedasticity, and Kalman lter and smoothing procedures are among the most popular procedures for factor extraction in the context of Dynamic Factor Models. This paper analyses the consequences on point and interval factor estimation of using these procedures when the idiosyncratic components are wrongly assumed to be cross-sectionally uncorrelated. We show that not taking into account the presence of cross-sectional dependence increases the uncertainty of point estimates of the factors. Furthermore, the Mean Square Errors computed using the usual expressions based on asymptotic approximations, are underestimated and may lead to prediction intervals with extremely low coverages.
arXiv (Cornell University), Jun 7, 2022
In this paper, we survey recent econometric contributions to measure the relationship between eco... more In this paper, we survey recent econometric contributions to measure the relationship between economic activity and climate change. Due to the critical relevance of these effects for the well-being of future generations, there is an explosion of publications devoted to measuring this relationship and its main channels. The relation between economic activity and climate change is complex with the possibility of causality running in both directions. Starting from economic activity, the channels that relate economic activity and climate change are energy consumption and the consequent pollution. Hence, we first describe the main econometric contributions about the interactions between economic activity and energy consumption, moving then to describing the contributions on the interactions between economic activity and pollution. Finally, we look at the main results on the relationship between climate change and economic activity. An important consequence of climate change is the increasing occurrence of extreme weather phenomena. Therefore, we also survey contributions on the economic effects of catastrophic climate phenomena.
International Journal of Forecasting, Oct 1, 2018
We extend the Markov-switching dynamic factor model to account for some of the specifi cities of ... more We extend the Markov-switching dynamic factor model to account for some of the specifi cities of the day-today monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefi ts of this extension and corroborate the results through several Monte Carlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.
International Journal of Forecasting, 2012
RePEc: Research Papers in Economics, May 1, 2017
In this paper, we analyze and compare the finite sample properties of alternative factor extracti... more 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, unless the idiosyncratic noise is non-stationary, procedures based on extracting the factors using the nonstationary original series work better than those based on differenced variables. The results are illustrated in an empirical application fitting non-stationary DFM to aggregate GDP and consumption of the set of 21 OECD industrialized countries. The goal is to check international risk sharing is a short or long-run issue.
RePEc: Research Papers in Economics, Feb 1, 2012
We develop a twofold analysis of how the information provided by several economic indicators can ... more We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the "shortcut" of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.
Journal of International Money and Finance
International Journal of Forecasting, Oct 1, 2013
She was invited to be a coeditor of the special issue of the International Journal of Forecasting... more She was invited to be a coeditor of the special issue of the International Journal of Forecasting ''Introduction to nonlinearities, business cycles, and forecasting''.
Practitioners do not always use research fi ndings, as the research is not always conducted in a ... more Practitioners do not always use research fi ndings, as the research is not always conducted in a manner relevant to real-world practice. This survey seeks to close the gap between research and practice in respect of short-term forecasting in real time. To this end, we review the most relevant recent contributions to the literature, examining their pros and cons, and we take the liberty of proposing some avenues of future research. We include bridge equations, MIDAS, VARs, factor models and Markov-switching factor models, all allowing for mixed-frequency and ragged ends. Using the four constituent monthly series of the Stock-Watson coincident index, industrial production, employment, income and sales, we evaluate their empirical performance to forecast quarterly US GDP growth rates in real time. Finally, we review the main results having regard to the number of predictors in factorbased forecasts and how the selection of the more informative or representative variables can be made.
Journal of Forecasting, Apr 4, 2014
This paper focuses on the effects of disaggregation on forecast accuracy for nonstationary time s... more This paper focuses on the effects of disaggregation on forecast accuracy for nonstationary time series using dynamic factor models. We compare the forecasts obtained directly from the aggregated series based on its univariate model with the aggregation of the forecasts obtained for each component of the aggregate. Within this framework (first obtain the forecasts for the component series and then aggregate the forecasts), we try two different approaches: (i) generate forecasts from the multivariate dynamic factor model and (ii) generate the forecasts from univariate models for each component of the aggregate. In this regard, we provide analytical conditions for the equality of forecasts. The results are applied to quarterly gross domestic product (GDP) data of several European countries of the euro area and to their aggregated GDP. This will be compared to the prediction obtained directly from modeling and forecasting the aggregate GDP of these European countries. In particular, we would like to check whether long-run relationships between the levels of the components are useful for improving the forecasting accuracy of the aggregate growth rate. We will make forecasts at the country level and then pool them to obtain the forecast of the aggregate. The empirical analysis suggests that forecasts built by aggregating the country-specific models are more accurate than forecasts constructed using the aggregated data.
Oxford University Press eBooks, Nov 19, 2015
In the context of dynamic factor models (DFM), it is known that, if the cross-sectional and time ... more In the context of dynamic factor models (DFM), it is known that, if the cross-sectional and time dimensions tend to in…nity, the Kalman …lter yields consistent smoothed estimates of the underlying factors. When looking at asymptotic properties, the cross-sectional dimension needs to increase for the …lter or stochastic error uncertainty to decrease while the time dimension needs to increase for the parameter uncertainty to decrease. In this paper, assuming that the model speci…cation is known, we separate the …nite sample contribution of each of both uncertainties to the total uncertainty associated with the estimation of the underlying factors. Assuming that the parameters are known, we show that, as far as the serial dependence of the idiosyncratic noises is not very persistent and regardless of whether their contemporaneous correlations are weak or strong, the …lter uncertainty is a non-increasing function of the cross-sectional dimension. Furthermore, in situations of empirical interest, if the cross-sectional dimension is beyond a relatively small number, the …lter uncertainty only decreases marginally. Assuming weak contemporaneous correlations among the serially uncorrelated idiosyncratic noises, we prove the consistency not only of smooth but also of real time …ltered estimates of the underlying factors in a simple case, extending the results to non-stationary DFM. In practice, the model parameters are unknown and have to be estimated, adding further uncertainty to the estimated factors. We use simulations to measure this uncertainty in …nite samples and show that, for the sample sizes usually encountered in practice when DFM are …tted to macroeconomic variables, the contribution of the parameter uncertainty can represent a large percentage of the total uncertainty involved in factor extraction. All results are illustrated estimating common factors of simulated time series.
Economia Politica, Dec 21, 2016
This paper analyzes oil price pass-through into inflation at disaggregate level for the euro area... more This paper analyzes oil price pass-through into inflation at disaggregate level for the euro area and its four main economies (France, Germany, Italy and Spain). The pattern of responses to oil price changes is quantitatively diverse across economies and across items of disaggregate inflation. Moreover, we suggest an alternative method to the direct calculation of aggregate effects on inflation given that indirect and second-round effects may offset the positive effects found in energy inflation and dissipate the effect on total inflation.
Journal of Forecasting, May 8, 2002
In this paper we present an extensive study of annual GNP data for five European countries. We lo... more In this paper we present an extensive study of annual GNP data for five European countries. We look for intercountry dependence and analyse how the different economies interact, using several univariate ARIMA and unobserved components models and a multivariate model for the GNP incorporating all the common information among the variables. We use a dynamic factor model to take account of the common dynamic structure of the variables. This common dynamic structure can be non-stationary (i.e. common trends) or stationary (i.e. common cycles). Comparisons of the models are made in terms of the root mean square error (RMSE) for one-step-ahead forecasts. For this particular group of European countries, the factor model outperforms the remaining ones.
Social Science Research Network, 2012
We develop a twofold analysis of how the information provided by several economic indicators can ... more We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the "shortcut" of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.
Social Science Research Network, 2012
We extend the Markov-switching dynamic factor model to account for some of the specifi cities of ... more We extend the Markov-switching dynamic factor model to account for some of the specifi cities of the day-today monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefi ts of this extension and corroborate the results through several Monte Carlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.
Empirical Economics, Sep 27, 2016
A very common practice when extracting factors from non-stationary multivariate time series is to... more A very common practice when extracting factors from non-stationary multivariate time series is to differentiate each variable in the system. As a consequence, the ratio between variances and the dynamic dependence of the common and idiosyncratic differentiated components may change with respect to the original components. In this paper, we analyze the effects of these changes on the finite sample properties of several procedures to determine the number of factors. In particular, we consider the informa
Computational Economics, Dec 12, 2018
In this paper, we analyze and compare the finite sample properties of alternative factor extracti... more 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 nonstationary DFM to aggregate Gross Domestic Product and consumption of a set of 21 industrialized countries from the Organization for Economic Cooperation and Development (OECD). The goal is to check if international risk sharing is a short-or long-run issue. Keywords Consumption smoothing • Non-stationary Dynamic Factor Models • Kalman filter • Principal components • Risk sharing 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.
Economics Letters, Sep 1, 2023