Mixed-frequency Vector Autoregression Research Articles

Substitution or creation? Identifying the role of artificial intelligence in employment

Recognising the significant role of artificial intelligence in the labour market is essential for China to develop sustainably. The research utilises the mixed frequency vector auto-regression (MF-VAR) technique, which would innovatively incorporate data at different frequencies into one model to identify the intricate correlation between the monthly artificial intelligence index (AII) and the quarterly unemployment rate (UR) in China. Through comparison, the MF-VAR method has a more substantial explanatory power than the low-frequency VAR (LF-VAR) model, the impulse responses of the former reveal that AII exerts favourable and adverse influences on UR. Among them, the positive effect occurs on the AII in the first and second months. In contrast, the negative one appears on the AII in the third month, highlighting that artificial intelligence has both stimulating and inhibiting effects on the labour market in China. By analysing UR’s predictive error variance decomposition, the total impact of China’s artificial intelligence technology on employment is a substitution; this outcome is accordant with the theoretical dis¬cussion. In the new round of scientific and technological revolution and industrial transformation, meaningful recommendations for China would be put forward to avert the wave of unemployment brought by the development of artificial intelligence technology.

China's risk contagion using the mixed-frequency macro-financial network

We explore and compare the fundamental laws of risk contagion under different shocks during the global financial crisis and COVID-19 pandemic periods. Using the daily returns of financial market indices and the monthly growth rates of macroeconomic data for two periods, from January 1, 2003, to December 31, 2010, and from January 1, 2015, to December 31, 2021, we construct a macro-financial network with the mixed-frequency vector autoregression (MF-VAR) method to produce well-defined measures of intra-system and cross-system risk contagion. We investigate the differences in risk contagion under different types of major events using an event study model. The empirical results show that the financial system is the center of risk contagion. Regarding the direction of the cross-system risk contagion, the risk contagion from the financial to the macroeconomic system dominates the contagion in the opposite direction. The impacts of financial and pandemic shocks on the macro-financial risk contagion differ significantly. Importantly, both investor sentiment and consumer confidence mediate these impacts, providing useful insights into the prevention of risk contagion.

Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting

Abstract Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and ‘short’ data imply a ‘ragged edge’ at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we investigate various ways of including a wide range of short data into a regional mixed-frequency vector autoregression (MF-VAR) model. These short data include hitherto unexploited regional value-added tax turnover data. We address the problem of the two ragged edges by estimating regional factors using different missing data algorithms that we then incorporate into our MF-VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published.

On the Direction of Causality between Business and Financial Cycles

This paper investigates whether business cycles cause financial cycles or vice versa. We also assess whether the US plays a leading role in causing the domestic business and financial cycles of other countries. The literature has established that business and financial cycles are linked through several channels such as credit constraints, the real effects of financial information and the reversal of overoptimistic expectations. Our analysis evaluates the direction of Granger causality using a novel approach based on the mixed-frequency vector autoregression model for the G7 countries. Our approach exploits the fact that real economic activity measured by industrial production is observed at a higher frequency than aggregate credit. We find strong evidence of bidirectional causality between the business and financial cycles, especially in recessions. Furthermore, the US is a global leader since the US business cycle significantly affects other countries’ business cycles, especially in terms of expansions.

Retracted: Macroeconomic Forecasting Based on Mixed Frequency Vector Autoregression and Neural Network Models

Retracted: Macroeconomic Forecasting Based on Mixed Frequency Vector Autoregression and Neural Network Models

Revisiting economic growth and CO2 emissions nexus in Taiwan using a mixed-frequency VAR model

This study, for the first time in Taiwan, uses the mixed frequency vector autoregressive (MF-VAR) model in the high-frequency dataset to investigate the causal association between economic progress and carbon dioxide emissions over time periods spanning from 1970Q1-2019Q4. This study also includes primary energy consumption as a control variable for analysis since the bias of missing variables could lead to misleading results. In accordance with the forecast error variance decomposition and the Granger causality test, our empirical findings imply that the MF-VAR model has more explanatory power than the traditional VAR model. The empirical LF-VAR model results demonstrate a reciprocal and causal association between economic progress and carbon dioxide (CO2) emissions is supported by evidence; primary energy consumption significantly influences economic progress. Additionally, MF-VAR model results also indicate, in Taiwan, a reciprocal and causal association between economic progress and carbon dioxide emissions as well as between primary energy consumption and economic progress. Based on the finding of the results, this study suggests policy implications to the government that an effective energy strategy should be addressed along with the plan for economic development in Taiwan.

Revisit economic growth and CO2 emission nexus in G7 countries: mixed frequency VAR model.

Applying a mixed frequency vector autoregressive (MF-VAR) approach, we examine relationships between CO2 emissions and economic growth from 1970Q1 to 2019Q4 among G7 countries. We incorporate primary energy consumption as a control variable, to avoid any bias from an omitted variable. Our empirical results, using forecast error variance decomposition and a Granger causality check, suggest that MF-VAR exhibits better explanatory ability over the more commonly used VAR model employing single frequency data. Results from LF-VAR exhibit a feedback loop connecting economic expansion and emissions of CO2 with one-way Granger causality from primary energy consumption to growth of the economies studied. MF-VAR model results also indicate that, in G7 countries, economic expansion exhibits a one-way causal link to CO2 emissions in Canada, UK, and US cases. Interestingly, MF-VAR shows feedback between economic expansion and primary energy use in Germany, while LF-VAR quantifies the link from economic growth to CO2 emissions in Canada and from CO2 to economic growth in the UK. Results raise implications for the G7 samples' policymakers.

Macroeconomic Forecasting Based on Mixed Frequency Vector Autoregression and Neural Network Models

Macroeconomic indicators include gross domestic product (GDP), consumer price index (CPI), and retail price index (RPI). These indicators are important for understanding the macroeconomic situation and controlling the macroeconomic trend, as they provide a macroscopic view of a country or region’s economic performance. If macroeconomic indicators can be predicted accurately in advance, the government and relevant macroeconomic control departments can propose more forward-looking and targeted macroeconomic control policies and deploy the necessary control measures. In addition, individuals can make more reasonable decisions on their investments and savings if they know the macroeconomic indexes in advance. From these two aspects, prediction of macroeconomic indexes is of great research significance. In this paper, we propose a prediction model based on chaotic vector autoregression and neural networks for macroeconomic forecasting, and we model and test the prediction with GDP and inflation as the main concerns, and we find that the improvement of GDP forecasting shows an increase of expected inflation rate, indicating the usefulness of using expected GDP data.

Oil price, green innovation and institutional pressure: A China's perspective

Oil is of dominated role in global energy market, and its price volatility would bring significant economic and environmental consequents. Given China is the largest oil importer, this paper applies the mixed frequency vector autoregression (MF-VAR) to investigate the causal relationship among oil price (OP), green innovation (GI), and institutional pressure (IP). The empirical results from MF-VAR model show that quarterly OP would stimulate GI with time-varying, which is not found in low frequency vector autoregression (LF-VAR). The new evidence mainly due to the mix frequency technique, which allows heterogeneous impacts exist between high and low frequency variables. In addition, higher IP is also discovered to promote GI. The major contribution is that a theoretical framework is initially constructed, and oil price is proven to as important booster for China's green innovation. Therefore, some policies such as innovation subsidy and green credit need to be employed to reduce the impacts from oil price fluctuations and main sustainable GI capability. Meanwhile, IP mainly from environmental protection requires government to launch stricter environmental regulations and laws, and establish reward and punishment mechanism to promote GI.

Do pandemic, trade policy and world uncertainties affect oil price returns?

We investigate the influence of pandemic and trade policy uncertainty on the dynamics of oil price returns over the two last decades, using a Mixed-Frequency Vector Autoregressive (MF-VAR) model. We find that pandemic uncertainty and, more importantly, trade policy uncertainty significantly explain EU Brent and WTI oil price returns. Additionally, pandemic and trade policy uncertainty shocks are linked with lower (higher) oil price returns in the short-term (medium-term). Finally, while our mixed-frequency approach captures the persistent response of oil price returns to the uncertainty shocks, the single common-frequency (i.e., quarterly) framework only uncovers a muted reaction.

Reconciled Estimates of Monthly GDP in the United States

In the United States, income and expenditure-side estimates of gross domestic product (GDP) (GDP and GDP) 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, GDP, 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.

How Do Economic Fluctuations Affect the Mortality of Infectious Diseases?

This paper uses the mixed frequency vector autoregression model to explore the impact of economic fluctuations on infectious diseases mortality (IDM) from China perspective. We find that quarterly gross domestic product (GDP) fluctuations have a negative impact on the annual IDM, indicating that the mortality of infectious diseases varies counter-cyclically with the business cycle in China. Specifically, IDM usually increases with deterioration in economic conditions, and vice versa. The empirical results are consistent with the hypothesis I derived from the theoretical analysis, which highlights that economic fluctuations can negatively affect the mortality of infectious diseases. The findings can offer revelations for the government to consider the role of economic conditions in controlling the epidemic of infectious diseases. Policymakers should adopt appropriate and effective strategies to mitigate the potential negative effects of macroeconomic downturns on the mortality of infectious diseases. In the context of the COVID-19 pandemic, these analyses further emphasize the importance of promoting economic growth, increasing public health expenditure, and preventing and controlling foreign infectious diseases.

Does high crude oil dependence influence Chinese military expenditure decision-making?

This paper employs the mixed frequency vector autoregression (MF-VAR) to discuss the causal link between crude oil dependence and military expenditure in China. The empirical results demonstrate that the crude oil dependence Granger causes military expenditure and accounts for 66.8% of the long-run forecast error variance decomposition. That means the MF-VAR model owns stronger explanatory and asymptotic powers than conventional VAR with single frequency data. Besides, the impulse-response analysis finds time varying relationship between crude oil dependence and military expenditure across four quarters within one year period. Therefore, the Chinese People's Liberation Army, especially its Navy, is needed to take responsibility and active activities to assure crude oil transport security. Government, enterprises and institutes should increase investment in military innovation to promote the upgrading of weapons and equipment. The peaceful rise strategy should be reasserted to construct harmonious international relations, which is helpful for crude oil security, foreign energy investment and reduce unnecessary military expenditure.

Measuring the effectiveness of US monetary policy during the COVID‐19 recession

The COVID‐19 recession that started in March 2020 led to an unprecedented decline in economic activity across the globe. To fight this recession, policy makers in central banks engaged in expansionary monetary policy. This paper asks whether the measures adopted by the US Federal Reserve (Fed) have been effective in boosting real activity and calming financial markets. To measure these effects at high frequencies, we propose a novel mixed frequency vector autoregressive (MF‐VAR) model. This model allows us to combine weekly and monthly information within a unified framework. Our model combines a set of macroeconomic aggregates such as industrial production, unemployment rates, and inflation with high‐frequency information from financial markets such as stock prices, interest rate spreads, and weekly information on the Fed's balance sheet size. The latter set of high‐frequency time series is used to dynamically interpolate the monthly time series to obtain weekly macroeconomic measures. We use this setup to simulate counterfactuals in absence of monetary stimulus. The results show that the monetary expansion caused higher output growth and stock market returns, more favorable long‐term financing conditions and a depreciation of the US dollar compared with a no‐policy benchmark scenario.

Large mixed-frequency VARs with a parsimonious time-varying parameter structure

SummaryIn order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.

The impact of high-frequency economic policy uncertainty on China’s macroeconomy: evidence from mixed-frequency VAR

We investigate the impact of high-frequency economic policy uncertainty on investments of state-owned and private-owned enterprises (SOEs and POEs), as well as short-, medium- and long-term bank loans in China by employing the mixed-frequency vector autoregression model. Impulse response analysis suggests that monthly economic policy uncertainty is allowed to have heterogeneous effects on investments and bank loans in China. Variance decomposition analysis shows that aggregating monthly economic policy uncertainty into the quarterly level underestimates the influence of economic policy uncertainty in shaping China’s macroeconomy at business cycle frequencies. By further decomposing the SOEs’ investment, we reveal that the effects of economic policy uncertainty on SOEs’ investment are strengthened due to the existence of the injection of the government investment into SOEs. Trade policy uncertainty has a similar impact on China’s investments and bank loans as economic policy uncertainty. The counterfactual analysis shows that the impact of economic policy uncertainty on China’s investments and bank loans is alleviated when the interest rate channel exists. Our major conclusions are insensitive to a series of robustness checks.

Geopolitical risk and crude oil security: A Chinese perspective

This paper employs the mixed-frequency vector autoregression (MF-VAR) to assess the causal relationship between geopolitical risk (GPR) and crude oil security (COS) for China. In contrast to prior papers, we regard GPR as an independent variable rather than including it in the indicator system and construct oil supply and price channels to better understand how GPR influences Chinese COS. The mixed-frequency data regression provides a new insight that quarterly geopolitical risk threatens Chinese oil security, and a time-varying nexus is discovered in different quarters within a one-year period. Therefore, the Chinese state-owned oil giants should implement diversified strategies such as equity acquisition and oil field investment to ensure a stable crude oil supply. China also needs to construct friendly relations with oil-exporting countries and join United Nations activities such as Somali escorts and peacekeeping in Africa to protect oil production and transportation. The establishment of crude oil futures dominated by the RMB is an effective way to strengthen oil affordability to cope with the oil price volatility caused by geopolitical instabilities.

A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior

Abstract We propose a Bayesian vector autoregressive (VAR) model for mixed-frequency data. Our model is based on the mean-adjusted parametrization of the VAR and allows for an explicit prior on the “steady states” (unconditional means) of the included variables. Based on recent developments in the literature, we discuss extensions of the model that improve the flexibility of the modeling approach. These extensions include a hierarchical shrinkage prior for the steady-state parameters, and the use of stochastic volatility to model heteroskedasticity. We put the proposed model to use in a forecast evaluation using US data consisting of 10 monthly and three quarterly variables. The results show that the predictive ability typically benefits from using mixed-frequency data, and that improvement can be obtained for both monthly and quarterly variables. We also find that the steady-state prior generally enhances the accuracy of the forecasts, and that accounting for heteroskedasticity by means of stochastic volatility usually provides additional improvements, although not for all variables.

Exploring the influence of economic policy uncertainty on the relationship between tourism and economic growth with an MF-VAR model

Using a mixed-frequency vector autoregressive (MF-VAR) model, this article attempts to determine whether or not the relationship between tourism and economic growth changes in the presence or absence of economic policy uncertainty (EPU) shock. Moreover, we further our analysis by focusing on whether or not there is a significant difference in the distinct impact intensity of Hong Kong, Chinese, and global EPU. The study period spans April 1998 to March 2018. The results indicate the following. First, the existence of Hong Kong, Chinese, and global EPU does not affect the direction of the impulse response; rather, its primary influence is on the size of the impact. Second, the different ranges of EPU have different impact intensities. Third, compared to the MF-VAR model, the quarterly frequency vector autoregressive model does not fully capture the impact of EPU, especially the negative impact of global EPU on tourism. Therefore, policymakers and tourism stakeholders should develop targeted marketing plans to maintain expected tourism demand if economic uncertainty increases.

The impacts of high-frequency US uncertainty shocks on China’s investment and bank loans: evidence from mixed-frequency VAR

ABSTRACT This paper evaluates the effects of high-frequency US uncertainty shocks on China’s investment and bank loans through the mixed-frequency vector autoregression model. We find that time-stamped US uncertainty shocks generate partly heterogeneous impacts on China’s investment and bank loans. The responses of bank loans with different maturities and private-owned enterprise investment are consistent with the theoretical results and policy operations except for the pattern of state-owned enterprise investment in the face of uncertainty shocks. By further decomposing the state-owned enterprise investment, we reveal that the injection of government investment to state-owned enterprise biases the time-varying responses of state-owned enterprise investment to US uncertainty shocks. Compared to the single-frequency approach, the mixed frequency approach produces richer economic results and insights.