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This paper investigates the predictive relationships among climate policy uncertainty (CPU), oil prices, and renewable energy (RE) stock market returns, particularly highlighting the challenges posed by the varying data frequencies of these variables. The study utilizes a comprehensive dataset comprising monthly CPU, daily oil prices, and RE stock returns, sourced globally. By applying a mixed-frequency causality test (MFCT), the analysis reveals significant predictability across different time horizons, particularly highlighting the strong influence of oil prices on RE stock returns over short-term horizons, while CPU demonstrates a more pronounced effect over medium to long-term horizons. In contrast, the application of the classical Granger causality test on low-frequency (monthly) data indicates an insignificant relationship between CPU and RE stocks, suggesting that traditional models may overlook important predictive dynamics. The analysis was conducted using Matlab code, and the findings provide valuable insights for policymakers in designing effective climate policies and for investors in optimizing portfolio strategies and hedging against risks. [ABSTRACT FROM AUTHOR]

This paper uses a two-regime Markov-switching GARCH model to illustrate that the state-switching of returns in the EU carbon market and its associated energy and stock markets is related to the occurrence of major events. Using the European economic policy uncertainty (EEPU) index as the proxy for the turbulence in the economic policies caused by major events, the Granger causality test shows that EEPU is associated with the three markets. To quantitatively analyze the impact of EEPU on the three markets, the GJRGARCH(1,1)-MIDAS model is constructed to solve the mixed frequency problem. The results indicate that EEPU has a negative effect on the long-term volatility of carbon and energy markets, but a positive effect on the stock market. The forecasting performance of the mixed frequency model considering EEPU is better than that of traditional GARCH models. Based on the decomposition of volatility in the mixed frequency model, the spillover index model is applied to analyze the spillover effects of the "carbon-energy-finance" system at three levels: total volatility, short-term volatility, and long-term volatility. The results indicate that at the level of long-term volatility affected by EEPU, the spillover effect of the system fluctuates more frequently and with greater intensity. The energy and stock markets play different roles at different levels of volatility, but the carbon market is always a risk receiver. The pairwise net spillover effects of the energy and stock markets on the carbon market are time-varying and related to the occurrence of major events. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the predictive relationships among climate policy uncertainty (CPU), oil prices, and renewable energy (RE) stock market returns, particularly highlighting the challenges posed by the varying data frequencies of these variables. The study utilizes a comprehensive dataset comprising monthly CPU, daily oil prices, and RE stock returns, sourced globally. By applying a mixed-frequency causality test (MFCT), the analysis reveals significant predictability across different time horizons, particularly highlighting the strong influence of oil prices on RE stock returns over short-term horizons, while CPU demonstrates a more pronounced effect over medium to long-term horizons. In contrast, the application of the classical Granger causality test on low-frequency (monthly) data indicates an insignificant relationship between CPU and RE stocks, suggesting that traditional models may overlook important predictive dynamics. The analysis was conducted using Matlab code, and the findings provide valuable insights for policymakers in designing effective climate policies and for investors in optimizing portfolio strategies and hedging against risks.

By considering the effect of long‐ and short‐run correlation (LS) networks, we propose an LS network‐augmented parametric portfolio selection model (LSNA‐PP). First, we combine the dynamic conditional correlation‐mixed data sampling (DCC‐MIDAS) model with the planar maximally filtered graph (PMFG) method to construct LS networks and extract network topological characteristics. Second, we design portfolio weights as a function of these topological characteristics to construct the LSNA‐PP model. Third, we apply the model to construct an international portfolio from 2010 to 2021. The empirical results illustrate the efficacy of the LSNA‐PP model in two ways. First, the LSNA‐PP model clarifies the economic interpretation of topological characteristics in portfolio selection, such as the positive effect of the long‐run correlation network and the negative effect of the short‐run correlation network on the weights. Second, the LSNA‐PP model performs well in terms of return expectations, risk diversification, and attractive risk‐adjusted returns, which are especially useful for stakeholders such as regulators, managers, and investors. [ABSTRACT FROM AUTHOR]

The ordered logit (OLogit) model is a regression model for an ordinal dependent variable. For a conventional time series OLogit model, both the dependent variable and the independent variables are required to be observed at the same frequency. However, this requirement is violated under the circumstance of mixed frequency data. To this end, we introduce the unrestricted MIDAS (U‐MIDAS) method into the OLogit model and develop a novel U‐MIDAS‐OLogit model, in which high‐frequency covariates are used to predict a low‐frequency outcome with ordinal categories. The U‐MIDAS‐OLogit model enlarges the application of OLogit and enables to produce timely forecast. To verify its effectiveness, we conduct extensive Monte Carlo simulations. The numerical results show that the U‐MIDAS‐OLogit model is superior to several typical OLogit models in terms of prediction performance. We then apply the U‐MIDAS‐OLogit model to predict credit ratings of listed companies in China and the US, respectively. The empirical results also confirm its promising in practical applications. [ABSTRACT FROM AUTHOR]

The use of mixed frequency data is now common in many applications, ranging from the analysis of high frequency financial time series to large cross-sections of macroeconomic time series. In this article, we show how state space methods can easily facilitate both estimation and inference in these settings. After presenting a unified treatment of the state space approach to mixed frequency data modeling, we provide a series of applications to demonstrate how our MATLAB toolbox can make the estimation and post-processing of these models straightforward.

The paper provides a disaggregated mixed-frequency framework for the estimation of GDP. The GDP is disaggregated into components that can be forecasted based on information available at higher sampling frequency, i.e., monthly, weekly, or daily. The model framework is applied for Greek GDP nowcasting. The results provide evidence that the more accurate nowcasting estimations require (i) the disaggregation of GDP, (ii) the use of a multilayer mixed-frequency framework, and (iii) the inclusion of financial information on a daily frequency. The simulation study provides evidence in favor of the disaggregation into components despite the inclusion of multiple sources of forecast errors. [ABSTRACT FROM AUTHOR]

In the United States, income and expenditure-side estimates of gross domestic product (GDP) (GDP I and GDP E ) measure "true" GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDP E , GDP I , unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (i.e., the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession. [ABSTRACT FROM AUTHOR]

We specify a mixed-frequency Nelson–Siegel term structure (MF-NS) model with fiscal policy information to investigate the influence of fiscal policy on interest rate term structure. This paper finds the following results: First, fiscal policy information can significantly improve both the in-sample fitting and out-of-sample predictions of the Chinese yield curve. More importantly, compared with short-term bonds, the fiscal variable has a greater contribution to long-term bonds. Second, the response of the level factor of the yield curve to monetary impulse is positive, while the response to fiscal surprise is negative. Furthermore, we suggest that monetary policy shows an inflation expectation effect for the level factor. However, fiscal policy shocks on the level factor via the wealth effect. Finally, fiscal policy account for the larger forecast variance of yield curve than monetary policy does in the short forecast horizon. Meanwhile, this paper further shows that fiscal policy has a greater contribution to forecast variance of long-term yield than short-term yield. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between unemployment and inflation in 29 African economies by re-examining the Phillips Curve hypothesis and evaluating the role of global oil prices in predicting inflation. Existing studies often use uniform-frequency variables, which can lead to biased results by omitting information from high-frequency series. We employ a mixed data sampling regression model to analyze inflation in selected African countries by regressing monthly inflation rates on annual unemployment rates and daily oil price returns. Our findings show that the Phillips Curve hypothesis holds in a few countries across Central Africa (Cameroon, the Central African Republic, Chad, and Republic of the Congo), East Africa (Rwanda), North Africa (Algeria), Southern Africa (Botswana and Malawi), and West Africa (Burkina Faso and Mauritania). In the other countries, the hypothesis does not hold, potentially indicating a predominance of cost-push inflation. Our analysis demonstrates that global oil prices impact inflation differently across these countries. Additionally, daily oil price data significantly enhance inflation forecasting compared to monthly data. Incorporating additional economic variables, such as exchange rates and interest rates, further improves forecast accuracy. We recommend targeted monetary policies to mitigate the impacts of oil price fluctuations and unemployment on inflation. • Reassesses traditional demand-side Phillips curve in Africa. • Integrates supply side, notably oil price, into Phillips curve. • MIDAS method exceeds uniform frequency in curve modeling. • Oil price inclusion improves curve's predictive capacity. • Results maintain robustness in all in/out-sample analyses. [ABSTRACT FROM AUTHOR]

To improve financial sustainability and promote economic stability, it is important to understand the intricate relationship between finance and macroeconomy. Thus, focusing on financial stress and macroeconomic sectors, this paper investigates macro-financial spillovers in China. First, we develop a high-frequency financial stress index based on eight daily financial indicators to measure the stability of China's financial markets. Through event identification, we find that China's Financial Stress Index can effectively reflect the stress situation of China's financial market. Then, given that the traditional co-frequency method fails to deal with financial stress index and macroeconomic data with different frequencies, we employ the mixed-frequency spillover method to evaluate macro-financial spillovers to examine the connectedness between China's financial market and the real side of the economy. We find that financial stress is the leading net risk output and primarily affects the loan sector; deterioration of economic conditions can lead to more apparent fluctuations in spillover effects, with spillovers from financial stress to others being the most susceptible; within the sample, the 2015 stock crash, U.S.–China trade friction, and COVID-19 have the most impact on macro-financial spillover effects. In addition, we track the results of different risk events on spillover effects across sectors. [ABSTRACT FROM AUTHOR]

Although the Covid‐19 crisis has shown how high‐frequency data can help track the economy in real time, we investigate whether it can improve the nowcasting accuracy of world GDP growth. To this end, we build a large dataset of 718 monthly and 255 weekly series. Our approach builds on a Factor‐Augmented MIxed DAta Sampling (FA‐MIDAS), which we extend with a preselection of variables. We find that this preselection markedly enhances performances. This approach also outperforms a LASSO‐MIDAS—another technique for dimension reduction in a mixed‐frequency setting. Though we find that a FA‐MIDAS with weekly data outperform other models relying on monthly or quarterly data, we also point to asymmetries. Models with weekly data have indeed performances similar to other models during "normal" times but can strongly outperform them during "crisis" episodes, above all the Covid‐19 period. Finally, we build a nowcasting model for world GDP annual growth incorporating weekly data that give timely (one per week) and accurate forecasts (close to IMF and OECD projections but with 1‐ to 3‐month lead). Policy‐wise, this can provide an alternative benchmark for world GDP growth during crisis episodes when sudden swings in the economy make usual benchmark projections (IMF's or OECD's) quickly outdated. [ABSTRACT FROM AUTHOR]

Forecasting multiple-step value-at-risk (VaR) consistently across asset classes is hindered by the limited sample size of low-frequency returns and the potential model misspecification when assuming identical return distributions over different holding periods. This paper hence investigates the predictive power for multi-step VaR of a framework that models separately the volatility component and the error term of the return distribution. The proposed model is illustrated with ten asset returns series including global stock markets, commodity futures, and currency exchange products. The estimation results confirm that the volatility-filter residuals demonstrate distinguished tail dynamics to that of the return series. The estimation results suggest that volatility-filtered residuals may have either negative or positive tail dependence, unlike the unanimous negative tail dependence in the return series. By comparing the proposed model to several alternative approaches, the results from both the formal and informal tests show that the specification under concern performs equivalently well if not better than its top competitors at the 2.5% and 5% risk level in terms of accuracy and validity. The proposed model also generates more consistent VaR forecasts under both the 5-step and 10-step setup than the MIDAS-Q model. AcknowledgmentThe authors are grateful to the editor and an anonymous referee. This research is sponsored by the National Natural Science Foundation of China (Award Number: 71501117). All remaining errors are our own.

Summary: This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators. [ABSTRACT FROM AUTHOR]

The paper proposes a novel method to assess whether real investment can be nowcasted based on information that is available on the stock market. The stock market index on a daily sampling frequency is assessed as a predictor of gross fixed capital formation on a quarterly sampling frequency. For France, Germany, Greece, and Spain (four representative countries of eurozone), we find significant empirical evidence that the information from the stock market does produce accurate nowcasting values of gross fixed capital formation. [ABSTRACT FROM AUTHOR]

We investigate mixed data sampling (MIDAS) regression models, which include time series data sampled at different frequencies. MIDAS framework introduced by Ghysels et al. (2004, 2005, 2006, 2007) comprises diverse lag structures that are employed for parameterizing the regression model. Since some specific parametric functions are utilized, MIDAS is a parametric approach and thus nonlinear least squares (NLS) technique is used to estimate the parameters. In this parametric approach, the shape of the lag distribution is controlled by an arbitrary class of parametric functions, but unfortunately, inaccurate approximations of the lag distribution may arise under such a circumstance. Breitung and Roling (2015) suggested a nonparametric approach as a solution to this complication. The product of this approach is smoothed least squares (SLS) estimator which depends on a smoothing parameter. The main objective of this paper is to solve the complexity of selecting this parameter. We offer various selection methods for the smoothing parameter to enhance the performance of the SLS estimator. A Monte Carlo simulation is conducted to test the effect of different choices of the smoothing parameter on the performance of the SLS estimator by means of both in‐sample and out‐of‐sample forecasting. Additionally, we carry out a data analysis where future values of gross domestic product are forecasted by unemployment rate. [ABSTRACT FROM AUTHOR]

We employ time series data to empirically determine the causal relationship between economic policy uncertainty and the GDP growth rates of seven emerging market economies while controlling for the effect of oil price, interest rates, and the CPI. Due to differences in sampling frequencies between the GDP series and other variables, a multi-horizon mixed frequency VAR model is specified. This model fully exploits the recently developed mixed frequency Granger causality test in order to circumvent the distorting effects of temporal aggregation. The empirical results show a strong statistical evidence for causality flowing from EPU to GDP in Brazil, Chile, and India in the mixed frequency case while weak statistical evidence is found for Colombia, Mexico, and Russia. For comparative analysis, the low-frequency Granger causality test is also employed and strong statistical evidence of causality flowing from EPU to GDP in Brazil, Chile, India, Mexico is uncovered. Analyzing the causal patterns uncovered in both specifications show that the low-frequency Granger causality results are less intuitively appealing than those that are obtained from the mixed frequency Granger causality test specifications. The results have empirical as well as policy implications which are discussed. [ABSTRACT FROM AUTHOR]

In this article, by using the mixed-frequency data sampling (MIDAS) model, we investigate whether economic policy uncertainty (EPU) can predict financial stress. Our estimation results show that EPU has a significant positive effect on the future financial stress, indicating that EPU is a determinant of financial stress. Moreover, the out-of-sample prediction results show that the MIDAS model performs better than the traditional time-series OLS model. [ABSTRACT FROM AUTHOR]

This paper subjects six alternative indicators of global economic activity to empirically examine their relative predictive powers in the forecast of crude oil market volatility. GARCH‐MIDAS approach is constructed to accommodate all the relevant series at their available data frequencies, thereby circumventing information loss and any associated bias. We find evidence in support of global economic activity as a good predictor of energy market volatility. Our forecast evaluation of the various indicators places a higher weight on the newly developed indicator of global economic activity which is based on a set of 16 variables covering multiple dimensions of the global economy, whereas other indicators do not seem to capture. Furthermore, we find that accounting for any inherent asymmetry in the global economic activity proxies improves the forecast accuracy of the GARCH‐MIDAS‐X model for oil volatility. The results leading to these conclusions are robust to multiple forecast horizons and consistent across alternative energy sources. [ABSTRACT FROM AUTHOR]

Abstract We study macro-financial linkages and their importance within the Swiss economy from a network perspective. First, we investigate the real-financial connectedness in the Swiss economy, using the KOF economic barometer, obtained from real and financial variables, and, the real activity index (RAI), we distilled from a small set of real variables, as two alternative proxies for the real side. Whereas the KOF-barometer-based analysis shows that both sides transmit sizeable shocks to each other without one dominating the other, the RAI-based analysis shows that in the aggregate, the financial side turns out to be the net shock transmitter to the real sector. In the second part, we focus on the relative importance of financial markets as shock propagators using a network centrality measure. We find that 2008–2009 recession in Switzerland and the Swiss National Bank’s (SNB) exchange rate policy changes in 2011 and 2015 have significantly altered the way the shocks are transmitted across the two sides of the economy. During 2009–2011, stock, bond, and foreign exchange (FX) markets, in descending order, played important roles as shock propagators. Following the SNB’s 2015 policy decision to discontinue the lower bound for the EUR/CHF exchange rate, FX market has become equally important as the stock market but more important than the bond market as a shock propagator.

This note proposes time series models for data where the frequency changes over time. As an example, for many countries, in the past, real GDP growth was observed annually and since a few years or decades, the data are available per quarter. Modifying the time series models allows for the incorporation of these older annual data, without a need for imputation. Illustrations for real GDP growth in China and the Netherlands show the merits of the method. [ABSTRACT FROM AUTHOR]

We propose measuring business conditions via estimating a smooth function of time that serves as a common factor for explaining the comovement of economic indicators across the occurred business cycles. This smooth measure is useful for reducing the noises in assessing the state of business conditions, and can be easily established using mixed-frequency indicators and updated in real time. We also conduct an empirical study to show its usefulness in real data. [ABSTRACT FROM AUTHOR]

We propose a new measure of macroeconomic disagreement, using dispersions of forecasts of a wide range of financial, activity and inflation variables from both household and professional surveys at various frequencies. With a mixed-frequency state-space model, we construct macroeconomic disagreement estimates of the one-year ahead expected state of the economy. Impulse responses show disagreement shocks lead to a contraction in economic activity. [ABSTRACT FROM AUTHOR]

The statistics and cyclical swings of macroeconomics are necessary for exploring the internal laws and features of the market economy. To realize intelligent and efficient macroeconomic forecast, this paper puts forward a macroeconomic forecast model based on improved long short-term memory (LSTM) neural network. Firstly, a scientific evaluation index system (EIS) was constructed for macroeconomy. The correlation between indices was measured by Spearman correlation coefficient, and the index data were preprocessed by interpolating the missing items and converting low-frequency series into high-frequency series. Next, the corresponding mixed frequency dataset was constructed, followed by the derivation of the state space equation. Then, the LSTM neutral network was optimized by the Kalman filter or macroeconomic forecast. The effectiveness of the proposed forecast method was verified through experiments. The research results lay a theoretical basis for the application of LSTM in financial forecasts. [ABSTRACT FROM AUTHOR]

There is renewed interest in levelling up the regions of the UK. The combination of social and political discontent, and the sluggishness of key UK macroeconomic indicators like productivity growth, has led to increased interest in understanding the regional economies of the UK. In turn, this has led to more investment in economic statistics. Specifically, the Office for National Statistics (ONS) recently started to produce quarterly regional GDP data for the nine English regions and Wales that date back to 2012Q1. This complements existing real GVA data for the regions available from the ONS on an annual basis back to 1998; with the devolved administrations of Scotland and Northern Ireland producing their own quarterly output measures. In this paper we reconcile these two data sources along with UK quarterly output data that date back to 1970. This enables us to produce both more timely real terms estimates of quarterly economic growth in the regions of the UK and a new reconciled historical time-series of quarterly regional real output data from 1970. We explore a number of features of interest of these new data. This includes producing a new quarterly regional productivity series and commenting on the evolution of regional productivity growth in the UK. [ABSTRACT FROM AUTHOR]

We study macro-financial linkages and their importance within the Swiss economy from a network perspective. First, we investigate the real-financial connectedness in the Swiss economy, using the KOF economic barometer, obtained from real and financial variables, and, the real activity index (RAI), we distilled from a small set of real variables, as two alternative proxies for the real side. Whereas the KOF-barometer-based analysis shows that both sides transmit sizeable shocks to each other without one dominating the other, the RAI-based analysis shows that in the aggregate, the financial side turns out to be the net shock transmitter to the real sector. In the second part, we focus on the relative importance of financial markets as shock propagators using a network centrality measure. We find that 2008–2009 recession in Switzerland and the Swiss National Bank's (SNB) exchange rate policy changes in 2011 and 2015 have significantly altered the way the shocks are transmitted across the two sides of the economy. During 2009–2011, stock, bond, and foreign exchange (FX) markets, in descending order, played important roles as shock propagators. Following the SNB's 2015 policy decision to discontinue the lower bound for the EUR/CHF exchange rate, FX market has become equally important as the stock market but more important than the bond market as a shock propagator. [ABSTRACT FROM AUTHOR]

Summary: Output growth data for the UK regions are available at only annual frequency and are released with significant delay. Regional policy makers would benefit from more frequent and timely data. We develop a stacked, mixed frequency vector auto‐regression to provide, each quarter, nowcasts of annual output growth for the UK regions. The information that we use to update our regional nowcasts includes output growth data for the UK as a whole, as these aggregate data are released in a more timely and frequent (quarterly) fashion than the regional disaggregates which they comprise. We show how entropic tilting methods can be adapted to exploit the restriction that UK output growth is a weighted average of regional growth. In our realtime nowcasting application we find that the stacked mixed frequency vector‐autoregressive model, with entropic tilting, provides an effective means of nowcasting the regional disaggregates exploiting known information on the aggregate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a new mixed‐frequency approach to predict stock return volatilities out‐of‐sample. Based on the strategy of momentum of predictability (MoP), our mixed‐frequency approach has a model switching mechanism that switches between generalized autoregressive conditional heteroskedasticity (GARCH)‐class models that only use low‐frequency data and heterogeneous autoregressive models of realized volatility (HAR‐RV)‐type that only use high‐frequency data. The MoP model simply selects a forecast with relatively good past performance between the GARCH‐class and HAR‐RV‐type forecasts. The model confidence set (MCS) test shows that our MoP strategy significantly outperforms the competing models, which is robust to various settings. The MoP test shows that a relatively good recent past forecasting performance of the GARCH‐class or HAR‐RV‐type model is significantly associated with a relatively good current performance, supporting the success of the MoP model. Highlights: This paper proposes a new mixed‐frequency approach to predict volatilities.Our mixed‐frequency approach is based on the momentum of predictability (MoP).Our MoP model has a model switching mechanism.The MoP model significantly outperforms the competing models out‐of‐sample.We demonstrate the existence of MoP between the GARCH‐class and HAR‐RV‐type models. [ABSTRACT FROM AUTHOR]

This paper investigates the trade‐off between timeliness and quality in nowcasting practices. This trade‐off arises when the frequency of the variable to be nowcast, such as gross domestic product (GDP), is quarterly, while that of the underlying panel data is monthly; and the latter contains both survey and macroeconomic data. These two categories of data have different properties regarding timeliness and quality: the survey data are timely available (but might possess less predictive power), while the macroeconomic data possess more predictive power (but are not timely available because of their publication lags). In our empirical analysis, we use a modified dynamic factor model which takes three refinements for the standard dynamic factor model of Stock and Watson (Journal of Business and Economic Statistics, 2002, 20, 147–162) into account, namely mixed frequency, preselections and cointegration among the economic variables. Our main finding from a historical nowcasting simulation based on euro area GDP is that the predictive power of the survey data depends on the economic circumstances; namely, that survey data are more useful in tranquil times, and less so in times of turmoil. [ABSTRACT FROM AUTHOR]

We derive asymptotic properties of estimators and test statistics to determine—in a grouped data setting—common versus group‐specific factors. Despite the fact that our test statistic for the number of common factors, under the null, involves a parameter at the boundary (related to unit canonical correlations), we derive a parameter‐free asymptotic Gaussian distribution. We show how the group factor setting applies to mixed‐frequency data. As an empirical illustration, we address the question whether Industrial Production (IP) is still the dominant factor driving the U.S. economy using a mixed‐frequency data panel of IP and non‐IP sectors. We find that a single common factor explains 89% of IP output growth and 61% of total GDP growth despite the diminishing role of manufacturing. [ABSTRACT FROM AUTHOR]

Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector autoregressive models. We present a unifying framework for parameter identifiability and estimation under subsampling and mixed frequencies when the noise, or shocks, is non-Gaussian. By studying the structural case, we develop identifiability and estimation methods for the causal structure of lagged and instantaneous effects at the desired time scale. We further derive an exact expectation-maximization algorithm for inference in both subsampled and mixed-frequency settings. We validate our approach in simulated scenarios and on a climate and an econometric dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a mixed-frequency small open economy structural model, in which the structure comes from a New Keynesian dynamic stochastic general equilibrium (DSGE) model. An aggregation rule is proposed to link the latent aggregator to the observed quarterly output growth via aggregation. The resulting state-space model is estimated by the Kalman filter and the estimated current aggregator is used to nowcast the quarterly GDP growth. Taiwanese data from January 1998 to December 2015 are used to illustrate how to implement the technique. The DSGE-based mixed-frequency model outperforms the reduced-form mixed-frequency model and the MIDAS model on nowcasting Taiwan's quarterly GDP growth. [ABSTRACT FROM AUTHOR]

Summary: Gross 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. [ABSTRACT FROM AUTHOR]