Claudia Foroni

A survey of econometric methods for mixed-frequency data

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent econometric literature. In this paper we provide an overview of the most common techniques, including bridge equations, MIxed DAta Sampling (MIDAS) models, mixed frequency VARs, and mixed frequency factor models. We also consider alternative techniques for handling the ragged edge of the data, due to asynchronous publication. Finally, we survey the main empirical applications based on alternative mixed frequency models.

Mixed-frequency vector autoregressive models

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

Mixed Frequency Structural Models: Identification, Estimation, and Policy Analysis

In this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identification problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that 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 policy conclusions. The problems and possible remedy are illustrated in the context of standard structural monetary policy models.

Mixed Frequency Structural VARs

A mismatch between the time scale of a structural VAR (SVAR) 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.

Using Low Frequency Information for Predicting High Frequency Variables

We analyze how to incorporate low frequency information in models for predicting high frequency variables. In doing so, we introduce a new model, the reverse unrestricted MIDAS (RU-MIDAS), which has a periodic structure but can be estimated by simple least squares methods and used to produce forecasts of high frequency variables that also incorporate low frequency information. We compare this model with two versions of the mixed frequency VAR, which so far had been only applied to study the reverse problem, that is, using the high frequency information for predicting low frequency variables. We then implement a simulation study to evaluate the relative forecasting ability of the alternative models in finite samples. Finally, we conduct several empirical applications to assess the relevance of quarterly survey data for forecasting a set of monthly macroeconomic indicators. Overall, it turns out that low frequency information is important, particularly so when it is just released.

LABOR SUPPLY FACTORS AND ECONOMIC FLUCTUATIONS: LABOR SUPPLY FACTORS AND ECONOMIC FLUCTUATIONS

We propose a new Vector Autoregressive identification scheme that enables us to disentangle labor supply shocks from wage bargaining shocks. Identification is achieved by imposing sign restrictions on the responses of the unemployment rate and the labor force participation rate to the two shocks. According to our analysis on United States data over the period 1985–2014, labor supply shocks and wage bargaining shocks are important drivers of output and unemployment both in the short run and in the long run. These results suggest that identification strategies used in estimated new Keynesian models to disentangle labor market shocks may be misguided.

A daily indicator of economic growth for the euro area

In this paper, we study alternative methods to construct a daily indicator of growth for the euro area. We aim for an indicator that (i) provides reliable predictions, (ii) can be easily updated at the daily frequency, (iii) gives interpretable signals, and (iv) it is linear. Using a large panel of daily and monthly data for the euro area we explore the performance of two classes of models: bridge and U-MIDAS models, and different forecast combination strategies. Forecasts obtained from U-MIDAS models, combined with the inverse MSE weights, best satisfy the required criteria.

Labor Supply Factors and Economic Fluctuations

We propose a new VAR identfi cation scheme that enables us to disentangle labor supply shocks from wage bargaining shocks. Identifi cation is achieved by imposing robust signrestrictions that are derived from a New Keynesian model with endogenous labor force participation. According to our analysis on US data over the period 1985-2014, labor supply shocks and wage bargaining shocks are important drivers of output and unemployment both in the short run and in the long run. These results suggest that identification strategies used in estimated New Keynesian models to disentangle labor market shocks may be misguided. We also analyze the behavior of the labor force participation rate through the lenses of our model. We find that labor supply shocks are the main drivers of the participation rate and account for about half of its decline in the aftermath of the Great Recession.