Bayesian group-shrinkage based estimation for panel vector autoregressive models with mixed frequency data

An artificial neural network for mixed frequency data

A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models

Mixed data sampling expectile regression with applications to measuring financial risk

Mixed frequency data and portfolio selection: A novel approach integrating DEA with mixed frequency data sources

Innovations in multiple time series analysis

Identification of Generalized Dynamic Factor Models from mixed-frequency data

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

We set-up a real business cycle model with search and matching frictions driven by several shocks, which nests full Nash Bargaining and wage rigidity as special cases and includes other transmission mechanisms suggested by the literature for the propagation and amplification of disturbances. The model is estimated using full information methods for two Anglo-Saxon countries (the US and the UK), two Continental European countries (France and Germany) and two Scandinavian countries (Norway and Sweden). We conduct inference with mixed frequency data, combining quarterly series for unemployment, vacancies, GDP, consumption, and investment, with annual data on unemployment flows. Parameters and shocks are estimated separately for each country, which can then vary in terms of search and hiring costs, workers' bargaining power, unemployment benefits levels, wage rigidity and the stochastic properties of disturbances. Overall, the structural model accounts reasonably well for differences in labor market dynamics observed between the two sides of the Atlantic and within Europe. Our estimates indicate that there is considerable cross-country variation in the contribution of technology shocks to the cyclical fluctuations of the labor market. Technology shocks alone replicate remarkably well the volatility in vacancies, unemployment and finding probabilities observed in US, with mixed success in Europe. In contrast, matching shocks and job destruction shocks play a larger role in most European countries relative to the US.

Nowcasting economic activity in a small open CESEE economy using mixed frequency data

Extended Yule-Walker Identification of VARMA Models with Single- or Mixed-Frequency Data

The time aggregation of vector linear processes containing (i) mixed stock‐flow data and (ii) aggregated at mixed frequencies, is explored, focusing on a method to translate the parameters of the underlying continuous time model into those of an equivalent model of the observed data. Based on manipulations of a general state‐space form, the results may be used to model multiple frequencies or aggregation schemes. Estimation of the continuous time parameters via the ARMA representation of the observable data vector is discussed and demonstrated in an application to model stock price and dividend data. Simulation evidence suggests that these estimators have superior properties to the traditional approach of concentrating the data to a single low frequency.

High-mixed frequency forecasting methods in R—With applications to Philippine GDP and inflation

In view of the problem that the traditional electric power prosperity index lacks the support of the mixed frequency and multi-dimension data, a new consistency measure method is constructed, which uses time difference correlation analysis and principal component and consider the linkage of electric power and economy at the same time. Take the Hunan province for example, we set the pool of kinds of sectional electricity power consumption and other relatives data, use the method of principal component analysis to capture main character of electricity’s change. The results show that the proposed consistency measurement model can describe the fluctuation characteristics of Hunan’s Power Prosperity Index well. The model can be used for monitor the changes in the power industry status and promoting the policy-making about the adjustment of the energy-saving and environment-friendly structure.

We combine the issues of dealing with variables sampled at mixed frequencies and the use of real-time data. In particular, the repeated observations forecasting (ROF) analysis of Stark and Croushore (2002) is extended to an autoregressive distributed lag setting in which the regressors may be sampled at higher frequencies than the regressand. For the US GDP quarterly growth rate, we compare the forecasting performances of an AR model with several mixed-frequency models among which the MIDAS approach. The additional dimension provided by dierent vintages allows us to compute several forecasts for a given calendar date and use them to construct forecast densities. Scoring rules are employed to test for their equality and to construct combinations of them. Given the change of the implied weights over time, we propose time-varying ROF-based weights using vintage data which present an alternative to traditional weighting schemes.

This article extends the Factor-Augmented Vector Autoregression Model (FAVAR) to mixed-frequency and incomplete panel data. Within the scope of a fully parametric two-step approach, the alternating application of two expectation-maximization algorithms jointly estimates model parameters and missing data. In contrast to the existing literature, we do not require observable factor components to be part of the panel data. For this purpose, we modify the Kalman Filter for factors consisting of latent and observed components, which significantly improves the reconstruction of latent factors according to the performed simulation study. To identify model parameters uniquely, the loadings matrix is constrained. In our empirical application, the presented framework analyzes US data for measuring the effects of the monetary policy on the real economy and financial markets. Here, the consequences for the quarterly Gross Domestic Product (GDP) growth rates are of particular importance.