100 research outputs found
Mixed frequency structural vector auto-regressive models
No full textA mismatch between the timescale of a structural vector auto-regressive model and that of the time series data used for its estimation can have serious consequences for identification, estimation and interpretation of the impulse response functions. However, the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identification issues and yield more reliable responses to shocks. The problems and possible remedy are illustrated analytically and with both simulated and actual data
Economic Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model
No full textWe propose a Bayesian panel model for mixed frequency data, where parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. To estimate the model, we develop a Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution, and we assess its performance in simulation experiments. We use the model to study the effects of macroeconomic uncertainty and financial uncertainty on a set of variables in a multi-country context including the US, several European countries and Japan. We find that the long-run dynamic effects are larger for changes in financial uncertainty than macroeconomic uncertainty. Furthermore, we show that the effects of uncertainty differ whether the economy is in a contraction regime or in an expansion regime
A survey of econometric methods for mixed-frequency data
No full textThis chapter is organized as follows: In section 9.2, we survey the different approaches to model mixed frequency variables. In section 9.3, we discuss the additional estimation issues arising with a ragged-edge structure of the dataset. In section 9.4 we compare the main features of the different approaches. In section 9.5 we present a summary of the most significant empirical applications in this literature. Finally, in section 9.6 we summarize and conclude
Explaining the time-varying effects of oil market shocks on US stock returns
No full textThis paper documents time-variation in the relation between oil price and US equity returns based on both reduced-form and structural analyses. Our reduced-form analysis suggests that the sign of the relation between real oil returns and real stock returns has changed over time, and that in the recent period this relation has turned positive since early 2007 (but started increasing since 2005). Based on our structural analysis, we find that oil-specific demand shocks have had positive effects on the US stock market since 2009 as opposed to oil supply shocks, which have no large effects on stock returns. We also show that the time variation in the parameters of the structural VAR is very well explained by the level of the US short-term interest rate and shifts in consumer confidenc
Using low frequency information for predicting high frequency variables
No full textWe analyze ways of incorporating low frequency information into models for the pre- diction of high frequency variables. In doing so, we consider the two existing versions of the mixed frequency VAR, with a focus on the forecasts for the high frequency variables. Furthermore, we introduce new models, namely the reverse unrestricted MIDAS (RU- MIDAS) and reverse MIDAS (R-MIDAS), which can be used for producing forecasts of high frequency variables that also incorporate low frequency information. We then conduct several empirical applications for assessing the relevance of quarterly survey data for forecasting a set of monthly acroeconomic indicators. Overall, it turns out that low frequency information is important, particularly when it has just been released
Mixed-frequency models with moving-average components
No full textTemporal aggregation in general introduces a moving average (MA) component in the aggregated model. A similar feature emerges when not all but only a few vari-ables are aggregated, which generates a mixed frequency model. The MA component is generally neglected, likely to preserve the possibility of OLS estimation, but the consequences have never been properly studied in the mixed frequency context. In this paper, we show, analytically, in Monte Carlo simulations and in a forecasting application on U.S. macroeconomic variables, the relevance of considering the MA component in mixed-frequency MIDAS and Unrestricted-MIDAS models (MIDAS-ARMA and UMIDAS-ARMA). Specifically, the simulation results indicate that the short-term forecasting performance of MIDAS-ARMA and UMIDAS-ARMA is bet-ter than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting U.S. GDP growth, investment growth and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS-ARMA is better than UMIDAS-ARMA
Forecasting the Covid-19 recession and recovery: lessons from the financial crisis
No full textWe consider simple methods to improve the growth nowcasts and forecasts obtained by mixed-frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track using a specific form of intercept correction. Among these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and subsequent recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment, and very persistent negative effects on trend growth. Similar findings also emerge for forecasts by institutions, for survey forecasts, and for the other G7 countries. (c) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved
Markov-switching Mixed-Frequency VAR models
No full textThis paper introduces regime switching parameters in the Mixed-Frequency VAR model. We first discuss estimation and inference for Markov-switching Mixed-Frequency VAR (MSMF-VAR) models. Next, we assess the finite sample performance of the technique in Monte-Carlo experiments. Finally, the MSMF-VAR model is applied to predict GDP growth and business cycle turning points in the euro area. Its performance is compared with that of a number of competing models, including linear and regime switching mixed data sampling (MIDAS) models. The results suggest that MSMF-VAR models are particularly useful to estimate the status of economic activity
Mixed‐frequency structural models : identification, estimation, and policy analysis
No full textThe mismatch between the timescale of DSGE (dynamic stochastic general equilibrium) models and the data used in their estimation translates into identification problems, estimation bias, and distortions in policy analysis. We propose an estimation strategy based on mixed-frequency data to alleviate these shortcomings. The virtues of our approach are explored for two monetary policy models
A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates
No full textIn this paper, we focus on the different methods which have been proposed in the literature to date for dealing with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency VAR (MF-VAR) models. We discuss their performances for nowcasting the quarterly growth rate of the Euro area GDP and its components, using a very large set of monthly indicators. We investigate the behaviors of single indicator models, forecast combinations and factor models, in a pseudo real-time framework. MIDAS with an AR component performs quite well, and outperforms MF-VAR at most horizons. Bridge equations perform well overall. Forecast pooling is superior to most of the single indicator models overall. Pooling information using factor models gives even better results. The best results are obtained for the components for which more economically related monthly indicators are available. Nowcasts of GDP components can then be combined to obtain nowcasts for the total GDP growth
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