Nowcasting economic activity in a small open CESEE economy using 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

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

Panel vector autoregressive (VAR) models are effective tools for capturing temporal relationships between a set of variables (e.g., macroeconomic indicators of an economy) while accounting for interdependencies between a set of entities (e.g., market sectors or whole economies). For modeling macroeconomic data, this challenge is often further accentuated by the presence of variables observed at different frequencies. Existing Bayesian approaches that link entity-specific VAR models often impose strict fusion of VAR coefficients to a common value across entities. This paper develops a balanced and less stringent Bayesian approach for mixed frequency panel VAR models that employ group shrinkage prior distributions to borrow strength across entities while allowing for entity-specific idiosyncrasies. A key novel feature is the ability to incorporate and learn the interdependence structure between entities through an interentity covariance (matrix) parameter. The proposed methodology is evaluated both on synthetic data and on two economic applications: employment indices across neighboring U.S. states and macroeconomic indicators of tightly integrated European economies. Finally, we establish the theoretical properties of the proposed approach.

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.

Identification of Generalized Dynamic Factor Models from 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.

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.

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

SummaryGross domestic product (GDP) is a key summary of macroeconomic conditions and it is closely monitored both by policy makers and by decision makers in the private sector. However, it is only available on a quarterly frequency, and in many countries it is released with a substantial delay. There are, however, many higher frequency and more timely economic and financial indicators that could be used for nowcasting and short-term forecasting GDP. Against this backdrop, we propose a modification of the three-pass regression filter to make it applicable to large mixed frequency data sets with ragged edges in a forecasting context. The resulting method, labelled MF-3PRF, is very simple but compares well with alternative mixed frequency factor estimation procedures in terms of theoretical properties and actual GDP nowcasting and forecasting for the USA and a variety of other countries.

Accurate and stable natural gas price forecasts are essential for effective management of energy systems. However, due to the mixed frequency of data and the inherent nonlinear fluctuation characteristics of natural gas price changes, it is difficult to achieve satisfactory forecasting performance. In order to effectively improve the forecasting results of mixed frequency data. In this study, the MIDAS regression model and the machine learning MIDAS models are successfully combined to form a novel combination forecasting model. Moreover, the extreme learning machine with the multi-objective grey-wolf algorithm is used to combine univariate MIDAS results and further improve the forecasting accuracy. In the empirical analysis, the weekly natural gas futures prices of the Intercontinental Exchange UK NBP are used to generate real-time forecasts to evaluate the forecasting performance of the proposed combination model. The experimental results show that the comprehensive forecasting accuracy of the novel combination MIDAS model is 26.35%, 8.82% and 12.91% higher than that of the benchmark MIDAS regression models, the combination MIDAS models and the multivariate MIDAS models, respectively. Based on the forecasting results, the non-linear, non-stationary and irregular natural gas futures prices can be effectively managed, which provides better investment and management tools.