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In many industrial manufacturing processes, the quality of products can depend on the relative amount between two quality characteristics X and Y. Often, this calls for the on-line monitoring of the ratio Z = X / Y as a quality characteristic itself by means of a control chart. A large number of control charts monitoring the ratio have been investigated in the literature under the assumption of independent normal observations of the two quality characteristics. In practice, due to the high frequency in sensor data collection, both autocorrelation and cross-correlation between consecutive observations can exist for X and Y and should be modelled to protect against the false alarm rate inflation when implementing a control chart for monitoring the ratio Z = X / Y. In this paper, we tackle this problem by investigating the performance of the Phase II Shewhart-type RZ control chart monitoring the ratio of two normal variables whose relationship is captured by a bivariate time series autoregressive model VAR(1), which can also account for the cross-correlation between the two quality characteristics. With the numerical study, we discuss how the design and the statistical performance of the Shewhart-type RZ control chart change with the VAR(1) model's parameters. We also provide an example to illustrate the use of the Shewhart-type RZ control chart with bivariate time series of observations in a furnace process. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. Furthermore, social media has garnered attention for its predictive capabilities in various fields, including financial markets and the economy. In this study, we exploit the predictive power of sentiment from Twitter and Reddit, alongside Google Trends indexes, to forecast log returns for 10 cryptocurrencies, namely Bitcoin, Ethereum, Tether, Binance Coin, Litecoin, Enjin Coin, Horizen, Namecoin, Peercoin and Feathercoin. We evaluate the performance of LASSO Vector Autoregression using daily data from January 2018 to January 2022. In a 30-day recursive forecast, we achieve a mean directional accuracy (MDA) rate of over 50%. Moreover, we observe a significant increase in forecast accuracy in terms of MDA when using sentiment and attention variables as predictors, but only for less capitalized cryptocurrencies. This improvement is not reflected in the RMSE. We also conduct a Granger causality test using post-double LASSO selection for high-dimensional VAR models. Our results suggest that social media sentiment does not Granger-cause cryptocurrencies returns. [ABSTRACT FROM AUTHOR]

Bayesian Vector Autoregressive models have become the natural response to the dense parametrization often required by multivariate time series modeling. However, the Bayesian approach is somehow new to the gretl ecosystem: classical analysis of Vector Autoregressions (VARs) is natively supported and supplemented via addons and function packages, but a Bayesian counterpart in the form of equally general and advanced functions or, at a lower level, in the form of didactic example scripts is missing. This paper pursues the second route describing, via a replication exercise, how to perform basic Bayesian inference in gretl using VARs, with particular reference to structural analysis. The contribution goes in the direction of providing new hints and tools, available to the gretl user, for a more complete and up-to-date understanding of modern macroeconometrics. [ABSTRACT FROM AUTHOR]

This study examines the effects of civil conflicts in Burundi on its economic performance. The analysis of the structural effects using a structural VAR model points to the long-lasting effects of a civil conflict shock on economic growth in Burundi. After 10 years, the effect on economic growth is still noticeable. The cumulative effect of a civil conflict shock is found to be a 7.1% decrease in economic growth over a horizon of 10 years. Using the Synthetic Control method, estimates indicate that the Burundian civil war led to an average GDP per capita decline of approximately 138 dollars per annum, compared to what it would have been in the absence of war. The average annual loss as a percentage of the counterfactual GDP per capita is 34%. The total monetary cost of the war during this period is estimated at USD 1514 per person and almost 10 billion USD for the entire country. The estimated effects are robust to placebo checks conducted. We compare these results with costs of conflict in other countries to better understand the relative financial burden of the civil war. The study highlights the need to consolidate peace to achieve long-term economic development in Burundi. [ABSTRACT FROM AUTHOR]

Purpose: The transmission of monetary policy has received considerable attention due to the sizable enlargement of the Federal Reserve's balance sheet and consequently of the large reserve balances held by the Depository Institutions. This paper aims to investigate whether changes in the quantity of the reserve balances during the so-called normalization period and the COVID-19 crisis put significant pressure on short-term interest rates and specifically on the Effective Federal Funds rate (EFFR). Design/methodology/approach: Under the new monetary policy regime, with two newly administered interest rates, the authors use the spread of the Federal Funds rates and the Interest on Reserve Balances (as a measure of the price of liquidity. With the means of various models such as the structural vector autoregression, the authors investigate, for two different subsample periods, the effectiveness of the monetary policy and the creation of (any) liquidity effects. Findings: The results showed that when the Fed decreases its balance sheet size, during the normalization period, significant liquidity effects are present meaning that the authorities could influence the stage of the short-term interest rates under the new monetary policy regime. However, this relationship appears to weaken considerably as the level of reserve balances, particularly in response to the COVID-19 pandemic, increases substantially. The authors enriched the findings by highlighting the role of the benchmark repo rate. During the COVID-19 period, and in light of abundant reserve balances, the repo rate reacts more vigorously to a reduction in reserves, whereas an increase in the repo rate seems to exert a strong positive influence on the EFFR. Originality/value: The findings are very important for the efficiency of the monetary transmission mechanism. An expanded balance sheet is still considered an arcane concept in regard to the structure and its effects on monetary policy implementation. This is one of the only few studies that investigates the effect of the abundant reserve balances on the short-term interest rates for two different in nature subsample periods. It shows as well the interplay between short-term interest rates, secured and unsecured. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the dynamic relationships between profitability, credit risk, liquidity risk and capital in Indonesian banking industry. Design/methodology/approach: The authors use a panel vector autoregression model that incorporates macroeconomic variables: growth, interest rate, foreign exchange. The analysis is based on a monthly panel data set of 88 banks spanning from January 2012 to September 2021, which comprises 10,296 bank-month observations. Findings: Our key findings highlight (i) permanent credit cost and liquidity cost pass through practices, (ii) complementary function of liquidity and capital, (iii) earning management motivated asset write off and (iv) credit risk-liquidity risk neutrality. In addition, the authors observe that the banks demonstrated resilience to macroeconomic shocks. Research limitations/implications: Our study have shown some interesting dynamic patterns of fundamentals; nevertheless, unified theoretical underpinning of the process is still unavailable. This should be an important future reasearch avenue. Practical implications: The study brings significant implications for regulatory and supervisory practices aimed at enhancing the financial stability of banks. Originality/value: We conduct estimation of Indonesian banks system in dynamic perspective and perform impulses responses. [ABSTRACT FROM AUTHOR]

Using a panel quantile vector autoregression model, we investigate the global liquidity effect of quantitative easing (QE) in the US on emerging markets (EMs) over the period 2010:Q1 to 2019:Q3. Our empirical result suggests that tapering of QE in the US triggers a large capital outflow from the EMs. In addition, we find a significant asymmetric effect of QE on portfolio investment flows to EMs with a stronger effect in the higher quantiles. The implication of these findings is that tapering the large-scale asset purchases and other instruments of unconventional monetary policy have a larger effect on EMs. [ABSTRACT FROM AUTHOR]

Exploring the coordination and analysis of intangible cultural heritage (ICH) and tourism development is a key issue in advancing cultural heritage and conservation in Chinese society. This paper employs panel data from 30 Chinese provinces from 2013 to 2022, utilizing the coupled coordination degree model and the panel vector autoregression (PVAR) model. In terms of provinces and regions, the data investigates the development relationship between intangible cultural heritage and tourism. The results of the study are: 1. The degree of coupling coordination between the two has shown an increasing trend over the ten-year sample data period. In 2021 and 2022, COVID-19 will have a stronger negative impact on the coupling degree. 2. The eastern coastal provinces of China, as well as the municipal areas, have a higher level of coupling coordination. Inland provinces, such as those in central and western China, exhibit relatively lower levels of coordination. The PVAR model data reveals a positive correlation between the interactions generated by intangible cultural heritage. The development of intangible cultural heritage has a particular impact on tourism. Intangible cultural heritage, in turn, depends on its own development. We should strengthen the development of intangible cultural heritage in the future. The study's findings provide theoretical references for the coordinated development of intangible cultural heritage and tourism in China. [ABSTRACT FROM AUTHOR]

ABSTRACT Interval Time Series are present in everyday life. An example is the opening and closing value of some stock in a market for certain time intervals. The forecasting plays an essential role in many financial organisations. The development of new mathematical tools or improving the existing ones will lead to more accurate forecasting techniques. Interval Arithmetic is a mathematical field that uses intervals by nature and algorithms that use it are involved in the solution of Interval Time Series. Another classical algorithm is VAR models. In this paper, a method that comes from the insurance sector is used to forecast Brent Oil monthly values. The innovation of what we propose is that it converts the Interval Time Series system into a single time series and can propagate the results back to each time series of the system. This way the researcher works with only one time series instead of two (or more). The forecasting algorithm is a choice of the researcher, expediting the development of forecasting (even ARIMA can be applied). We demonstrate our methodology in forecasting the Brent Oil monthly prices by applying the ANFIS algorithm. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of monetary policy, banking liquidity, and macroeconomic factors on the government bond yield in Vietnam by employing a VAR model. Our findings show that the central bank policy interest rate plays a significant role in government bond yield both in the short- and long-term. There was a downward trend of government bonds in the period 2007–2019 thanks to the improvement of banking system liquidity as well as macroeconomic stability. The results are robust when the government bond yield is computed by three factors, including level, slope, and curvature. [ABSTRACT FROM AUTHOR]

A central aspect of the modernization of the armed forces is innovation in military technology, which in turn has been closely linked to defense spending. However, the connection between these variables has hardly been analyzed quantitatively. This is due, in part, to the scarcity of statistical data on military technology. This paper has constructed this indicator for the case of Spain based on patents registered in the country. This has allowed us to conduct a VAR model analysis to explore the relationship between defense spending and military technology and between the latter and the modernization of the armed forces from the late 19th century to the eve of the Civil War. Estimates have revealed that defense spending did not have a causal effect (in the Granger sense) on military patents, but there was a relationship in the opposite direction; that is, technological innovation preceded spending. In addition, it has been found that the patents that most influenced spending were foreign patents. On the other hand, there is evidence of a positive effect of military patents on the modernization of the armed forces, with foreign patents being the most influential in this case as well. [ABSTRACT FROM AUTHOR]

This study uses a family of VAR models with time-varying parameters and stochastic volatility (TVP-VAR-SV) to analyze the impact of external shocks on output growth and inflation in Peru in 1992Q1-2017Q1. The statistical relevance of the models is assessed using the deviance information criterion (DIC) and the marginal log-likelihood calculated using the cross-entropy method. The results show that: (i) it is more relevant to introduce SV than TVP; i.e., the best fitting model admits only varying intercepts and SV; and TVP-VAR and CVAR are the least performing models; (ii) the models' impulse response functions indicate that the impacts from external shocks are different under high inflation, economic crisis, and monetary policy change, with a greater impact in episodes of high uncertainty; (iii) the impact and importance of external shocks have increased over time; and (iv) the results are robust to changes in the priors, the lag structure, order of the variables, the choice of the external variable, and the selection of the variable for domestic economic activity. [ABSTRACT FROM AUTHOR]

The paper researches factors influencing green entrepreneurship in the five European Union's emerging markets from 2008 to 2020. We choose five countries from the UE not in the eurozone (International Monetary Fund's approach to assessing economies), including Bulgaria, Croatia, Hungary, Poland, and Romania. The theoretical part discusses selected problems related to green entrepreneurship (we focus on analysing green sectors of the economy) and its determinants. The practical part shows the research results. We use Pearson's R, the ordinary least squares, and the vector autoregression model (VAR) to verify our research hypothesis (p-value < 0.05). We focus on factors which are crucial for entrepreneurship OECD (2011). Measuring green entrepreneurship. Entrepreneurship at a Glance, including macroeconomic conditions (MacroCon), creation and diffusion of knowledge (CaDKnow), entrepreneurial skills and capabilities (CapEnt), regulations (RegFram), and the expenditure on environmental protection (EnvProt). According to the OLS regression, there is a substantial variation in the impact of individual factors on green entrepreneurship in emerging markets from 2010 to 2020. Moreover, green is conditioned by a sequence of events, with the past determining the present values. [ABSTRACT FROM AUTHOR]

Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short‐term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short‐term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out‐of‐sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model. [ABSTRACT FROM AUTHOR]

Is the importance of technology shocks in accounting for the fluctuations in output and hours sensitive to the measure of output? Using a vector autoregression, we empirically investigate the contribution of technological shocks to economic fluctuations when output is defined in consumption units, as commonly used in many macroeconomic models, and when output is tabulated according to the Divisia index in the U.S. National Income and Product Accounts (NIPAs). Based on a standard neoclassical growth framework that allows for a mapping of theory into any desired measure of output, we establish that the same restrictions may be used to identify technology shocks in a vector autoregression model regardless of the measure of output. However, our estimation reveals that while the combination of both investment-specific technology shocks and neutral technology shocks accounts for a large portion of the business cycle variability of hours and output, the choice of the measure of output via the use of the associated deflator greatly affects the amplification of the shocks in the responses and the variability of the responses in output and hours. [ABSTRACT FROM AUTHOR]

The agricultural sector is regarded as a fertile ground for achieving the African Continental Free Trade Area ambitions. However, agricultural productivity in Africa is low and falling. It is argued that intra-Africa trade and good governance can help increase agricultural productivity. Nonetheless, both are low, casting doubt on their ability to enhance agricultural productivity. This study attempts for the first time to examine the causal relationship between the intra-Africa agricultural trade, governance quality, and the agricultural total factor productivity (ATFP) for 47 countries over the period 1995–2018. We extend the analysis to regional economic communities (RECs) to understand the diversity in this relationship within the continent. Using the panel VAR model in the generalized method of moment (GMM) estimation approach, our results suggest a long-run equilibrium relationship between the three variables. Specifically, the results indicate that at the African level, intra-Africa agricultural trade has a statistically positive effect on governance quality and ATFP. In contrast, good governance has positive and negative impacts on ATFP and trade, respectively. We also find that ATFP positively influences intra-African agricultural trade and governance. At the RECs level, our estimations show significant heterogeneity in the three variables' impacts. Policy implications are discussed. [ABSTRACT FROM AUTHOR]

We introduce a novel tourism-specific business expectations sentiment index and explore whether it can operate as a leading indicator for international tourist arrivals in Greece. Using monthly data spanning 2002‐2021 and employing a VAR model, we document that this newly introduced tourism-specific business expectations index serves as a leading indicator, whose higher levels foreshadow increased demand for international travel. We also find that its inclusion in a tourism-oriented model increases forecasting accuracy, which can be utilized by travel agent businesses, local government officials, and policymakers in their efforts to predict tourist arrivals in Greece. [ABSTRACT FROM AUTHOR]

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In Doubly Fed Induction Generator (DFIG)-based wind farms with Static Var Generators (SVGs), high-frequency resonance will be more like to occur when an unloaded cable is put into operation, which will threaten the stable operation of the wind farm. To address this issue, the influence of power outer loops on the impedance of grid-connected inverters is considered. Based on harmonic linearization, theoretical models for the sequence impedances of DFIGs, Grid-following (GFL) SVGs, and Grid-forming (GFM) SVGs are established. The correctness of the three models is verified by impedance scanning using the frequency sweep method. Through a comparative analysis of these sequence impedances, it is found that unlike the GFM SVG (which exhibits inductive impedance), the GFL SVG exhibits capacitive impedance in the high-frequency band, which leads to negative damping characteristics in the high-frequency band for the wind farm system with the grid-following SVG; thereby, the risk of high-frequency resonance also increases accordingly. On the contrary, GFM control adopted by SVGs can effectively eliminate the negative damping region in the high-frequency band for wind farms to suppress high-frequency resonance. Meanwhile, for grid-forming SVGs, the parameter variations in power synchronous loops have no significant impact on the suppressing effect of high-frequency resonance for wind farms. Finally, an electromagnetic simulation model for a DFIG-based wind farm system with an SVG is established using the StarSim-HIL (hardware-in-the-loop) experiment platform, and the simulation results validate the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Purpose: The study examines the impact of fiscal policy shocks on economic growth and income inequality in Ghana. This has become necessary because of the interdependence between growth and income inequality and the role fiscal policy plays in this relationship in the development process of a country. Thus, a study that investigates how government expenditure shock and tax revenue shock influence the relationship between economic growth and income inequality could assist policymakers to adopt the best policy mix to ensure income equity and sustained economic growth in Ghana. Design/methodology/approach: It employs sacrifice ratio from structural VAR model using quarterly time series data from 1996 to 2019 on Ghana. Findings: Our results show that government expenditure shock impacts economic growth, exchange rate and education positively and significantly in the long run. Also, tax revenue shock has a positive impact on income inequality, economic growth and education. The findings further show that there exists a trade-off between economic growth and income inequality in the long run. Originality/value: The relationships between fiscal policy shocks, economic growth and income inequality have been extensively discussed among scholars. Understanding how these three macroeconomic variables are determined and their interrelationships are crucial for policymakers. This is because fiscal policy aids in both economic growth and income inequality. In the empirical literature, the emphasis has been on independently estimating the growth effects of fiscal policy or the distribution effects of fiscal policy, leaving out the existence of possible trade-off between economic growth and income inequality following a fiscal shock. To the best of our knowledge, no empirical study has been done on Ghana to empirically examine the trade-off between economic growth and income inequality as we do in this paper. [ABSTRACT FROM AUTHOR]

We explore the application of spectral methods in risk management as means of validating VaR models. We propose to replace earlier spectral VaR tests with the test based on the Anderson–Darling statistic. Based on assumptions relevant to VaR failure analysis, we experimentally prove that the Anderson–Darling spectral test displays strong power to reject inaccurate VaR. Its main advantage over the existing methods is the combination of two features: the lack of tendency to overreject properly predicted VaR and high sensitivity to limited evidence of incorrectness in VaR predictions. Thus, this test may play an important role in times of change in volatility dynamics, such as outbreaks of financial crises. We confirm this empirically, based on data starting before the subprime mortgage crisis, running through the COVID-19 pandemic, until the outbreak of the Russo–Ukrainian war. We give a number of examples when this method revealed the inaccuracy of VaR predictions not discovered by com- monly used tests. We also show that the proposed spectral test never failed at finding the models indicated as incorrect by other tests. [ABSTRACT FROM AUTHOR]

We intend to show the directional volatility spillovers between the European short term interbank lending rates (3‐month Euro Interbank Offered Rate [EURIBOR] and Euro Short‐Term Rate [ESTR]) and the Hungarian Budapest Interbank Offered Rate (BUBOR) and Euro‐Hungarian Forint exchange rate. To determine the extent to which the variables affect each other's volatilities we build a five‐variable vector autoregression (VAR) and determine the spillover table like in Diebold‐Yilmaz's 2012 work. This methodology is preferred to a simple impulse response function (IRF) because we manage to avoid the problem of non‐orthogonal innovations via the generalized forecast error variance decomposition framework. The issue of variable ordering, therefore, does not arise. We focus on three episodes of increased volatility in Hungarian and European short‐term interest rates: Q3–Q4 of 2019, Q1 of 2020 and Q3 of 2022. These episodes correspond to volatility spikes in EU markets that to some extent had a measurable spillover effect on Hungarian interbank rates. We find that on average, across the entire sample of 957 observations, about 6.3% of the volatility forecast error variance in all five European and Hungarian variables comes from spillovers. The total and directional spillovers over the sample are extremely low. We conclude that the European Central Bank's surprise policy decisions have a marginal impact on Hungarian interbank rates. We also find that BUBOR is primarily a net receiver of spillovers from the MAX short‐term government bond benchmark rather than the EURIBOR—this disproved our initial considerations. [ABSTRACT FROM AUTHOR]

Catastrophe (CAT) bonds necessitate a robust construction with regard to the estimated probability measure of their triggering parameter. This article concentrates on earthquakes as the primary natural catastrophe of concern. By leveraging the geometry of active faults for estimating default probability, we utilize seismic event information spanning up to 15,000 years in the past—thereby surpassing the restricted time range of available historical catalogs commonly used in other analyses, which typically cover only a few hundred years. This article introduces the design and pricing methodology of CAT bonds employing autoregressive neural networks, extending the standard VAR Nelson-Siegel model for yield curves. It presents a case study focused on the region of Greece, estimating that an additional spread of approximately 500 basis points over LIBOR constitutes the minimum premium required to attract an investor to undertake the associated risk. This premium could be absorbed by insured parties as an alternative to the conventional insurance process. [ABSTRACT FROM AUTHOR]

We study two sources of heteroscedasticity in high-frequency financial data and estimate their contribution to overall volatility by means of a Markov switching (MS) structural VAR model. We achieve identification for all coefficients by assuming that the structural errors follow a GARCH-DCC process. Using transaction data of the EUR/USD interdealer market in 2016, we first detect three regimesof volatility. Then we show that both sources of volatility matter for the transmission of shocks, and that information is channeled to the market mostly through demand shocks. This suggests that, on the EUR/USD market, some liquidity takers (LTs) are better informed than both liquidity providers and those LTs who follow a feedback strategy. [ABSTRACT FROM AUTHOR]

Domestic and Foreign institutional investors are professional investors who aim to profit from price movements in the stock market. These investors not only profit from their investments but also drive the stock market of any country. The study examines the association between Sensex Nifty 50’s movements and investing patterns of FIIs, DIIS and the causal relationship between stock market returns and institutional investments (FIIS and DIIs). The study also analyses the impact of FIIs and DIIs in driving the Indian stock market. The study employed correlation analysis, vector autoregression model, Block exogeneity Wald-test and the Granger causality test for the objectives. Our results indicate the gradual fall in the FIIs-DIIs ratio in the Indian stock market, making domestic institutional investors a prominent counterforce to FIIs and establishing it as an equal driver. DIIs act as positive feedback traders, while FIIs act as neutral to negative traders. The causality test demonstrates that market return has a significant impact on both the players, but neither FIIs nor DIIs have a significant impact on market movement [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a dynamic stochastic general equilibrium (DSGE) model for the State Bank of Vietnam (SBV) to assess the response from the household sector to monetary policy shocks through the consumption function. Moreover, the transmission from monetary policy to household consumption and income distribution is experimented with through the vector autoregression (VAR) model. Design/methodology/approach: In this study, the authors used the maximum likelihood estimation to estimate the DSGE and VAR models with the sample from 1996Q1 to the end of 2021Q4 (104 observations). Findings: The DSGE model's results show that the response of the household sector is as expected in the theory: a monetary policy shock occurs that increases the policy interest rate by 0.29%, leading to a decrease in consumer spending of about 0.041%, the shock fades after one year. Estimates from the VAR model give similar results: a monetary policy shock narrows income inequality after about 2–3 quarters and this process tends to slow down in the long run. Research limitations/implications: Based on the research results, the authors propose policy implications for the SBV to achieve the goal of price stability, and stabilizing the macro-economic environment in Vietnam. Originality/value: The findings of the study have theoretical contributions and empirical scientific evidence showing the effectiveness of the implementation of the SBV's monetary policy in the context of macro-instability, namely: flexibility, caution and coordination of different measures promptly. [ABSTRACT FROM AUTHOR]

This paper examines the short- and medium-run effects of U.S. federal personal income and corporate income tax cuts on a wide array of economic policy variables in a data-rich environment. Using a panel of U.S. macroeconomic data set, made up of 132 quarterly macroeconomic series for 1959–2018, we estimate factor-augmented vector autoregression (FAVARs) models where an extended narrative tax changes dataset combined with unobserved factors. The narrative approach classifies if tax changes are exogenous or endogenous. This paper identifies narrative tax shocks in the vector autoregression model using the sign restrictions with the Uhlig's (J Monet Econ 52(2):381–419, 2005. https://doi.org/10.1016/j.jmoneco.2004.05.007) penalty function. Empirical findings show a significant expansionary effect of tax cuts on the macroeconomic variables. Cuts in personal and corporate income taxes cause a rise in output, investment, employment, and consumption; however, the effects of corporate tax cuts have relatively smaller effects on output and consumption but show immediate and higher effects on fixed investment and price levels. We validate the model's specification and the identification of tax shocks through a reliability test based on the Median-Target method. Additionally, sensitivity analysis employing the local projection vector autoregression model, number of iterations of the algorithm, and incorporating diverse factor specifications reaffirms tax cuts' persistent and expansionary effects. Our contribution to the narrative tax literature lies in providing empirical evidence that aligns with the notion that reductions in personal taxes demonstrate a higher efficacy as a fiscal policy tool when compared to reductions in corporate income taxes. [ABSTRACT FROM AUTHOR]

This paper proposes a panel Granger causality approach for heterogeneous panels with cross-sectional dependence. We define a panel VAR model with unobserved common factors and apply the PANIC procedure to obtain the de-factored data. We then estimate the lag augmented (LA)-VAR model for each cross section and construct the panel statistics based on the meta-analytic approach that combines the p-values of the individual statistics. The Monte Carlo simulations indicate that the combination tests show good size and power properties and appear suitable for the panels where cross sections may have different unit root or co-integration properties. We finally re-investigate Granger causality between export and economic growth in OECD countries. The results shed light on the importance of accounting for cross-sectional dependence within a factor model framework in determining direction of Granger causality for country-specific analysis. The results further reveal that export and economic growth do not cause each other in the majority of the European Union countries. [ABSTRACT FROM AUTHOR]

Amaç: Sanayi üretim endeksi ile ilgili literatür incelendiğinde sanayi üretim endeksini etkileyen faktörlerin birbirinden farklılaştığı görülmektedir. Çalışmanın amacı, makroekonomik göstergelerin ve borsa performansının Türkiye'nin sanayi üretim endeksi üzerindeki etkisini ampirik olarak analiz etmektir. Yöntem: Çalışma, 2016:01 ile 2023:12 arasındaki dönemi kapsamaktadır. İncelenen makroekonomik değişkenler enflasyon, mevduat faiz oranı ve reel efektif döviz kuru ve ek olarak Borsa İstanbul 100 endeksidir. Değişkenlerin durağanlığını araştırmak için ADF testi kullanılmış ve vektör otoregresyon (VAR) metodolojisinin Etki-Tepki Analizi, makroekonomik değişkenler ile sanayi üretim endeksi üzerindeki borsa getirisi arasındaki ilişkiye uygulanmıştır. Amaçlanan, değişkenlerden birinde oluşan bir birimlik şokun diğer değişken üzerindeki etkisini ölçmektir. Bulgular: Çalışma bulguları sanayi üretim endeksinin kendisinde ve diğer değişkenlerdeki birim şokluk değişimlerin etkisinin ortalama 2-4 dönem sonrasında sıfıra yakınsadığını göstermektedir. Sanayi üretim endeksinin reel efektif döviz kuru ve enflasyon oranına tepkisi farklı büyüklüklerde olsa da aynı patikayı izlemekte ancak mevduat faiz oranına tepkisi ise tam zıt yönlü olmaktadır.Özgünlük: Elde edilen bulgulara göre, ilgili değişkenlerde oluşan değişimler 2-4 aylık dönemde etkisini yitirip sistem dengeye gelmektedir. Buna göre büyümenin öncü göstergesi olan sanayi üretim endeksine ilişkin oluşturulacak politikalarda takip edilecek ve etkilerinin yönüne göre dikkate alınacak değişkenler belirlenmiş olmakta ve uzun dönemli etkiler için diğer makroekonomik değişkenlerin araştırılması gerekliliği ortaya konmaktadır.

The aim of this paper is to develop a forecasting method with global interval input and output for interval-valued financial time series by combining the VAR(p) process, the volatility information and neural network, namely VAR-NN. To reflect the volatility information, four types of interval-valued data volatility information from both the relative and absolute perspectives are constructed. Furthermore, the neural network is combined to produce the parameters. The developed forecasting model is finally applied to the highest and lowest hourly prices of the Shanghai Composite Index prediction. Numerical study shows the feasibility and validity of the developed improved VAR model. [ABSTRACT FROM AUTHOR]

We estimate a series of Threshold Vector Autoregression models to examine the nonlinear nature of interactions among housing market condition, output growth, credit market condition, monetary policy, and inflation across different housing market regimes. We find that the effects of housing, output, monetary, and credit shocks are highly regime-dependent, both qualitatively and quantitatively. Almost all shocks have larger impacts when the housing market is in the distress regime than normal regime. Furthermore, we find that the asymmetrical effects of shocks are the most prominent when the housing market moves in and out of the distress regime. However, once the housing market is in the normal regime, the asymmetry becomes economically insignificant when the housing market condition crosses between moderate and booming growth phases. [ABSTRACT FROM AUTHOR]

This study analyzes the response of economic fundamentals to a misalignment shock of the real effective exchange rate in Korea. The estimation results of the equilibrium exchange rate determination model and time series model show that there is no significant difference in the direction of the deviation from equilibrium and that the won is significantly undervalued during the period before 1988, or during the currency and global financial crises. The cumulative impulse response analysis of the VAR model over the full period shows that an upward shock to the deviation from the equilibrium exchange rate reduces the GDP gap and inflation rate, while the effect on the call rate is not statistically significant. Furthermore, an upward misalignment shock initially worsens the goods and services balance, but the deficit in the goods and services balance shrinks significantly over time. In rolling regressions analysis, the entire sample is divided into two periods to estimate the impulse response function from the first period, and then the same procedure is repeated by moving the sample forward one by one. The cumulative impulse response results show that, as is the case for the full period, a positive exchange rate misalignment shock initially reduces the GDP gap, inflation, and worsens the goods and services balance, but the impact of this upward shock on these variables becomes increasingly weaker in the more recent sample. It also shows that the negative impact of upward shocks on the current account is smoothed out more recently during periods of undervaluation than during periods of overvaluation. [ABSTRACT FROM AUTHOR]

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in Mplus to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM. [ABSTRACT FROM AUTHOR]

Vector autoregressive (VAR) models are popularly adopted for modeling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modeling, the number of parameters grow quadratically with the dimensionality which necessitates the sparsity assumption in high dimensions. However, it is debatable whether such an assumption is adequate for handling datasets exhibiting strong serial and cross-sectional correlations. We propose a piecewise stationary time series model that simultaneously allows for strong correlations as well as structural changes, where pervasive serial and cross-sectional correlations are accounted for by a time-varying factor structure, and any remaining idiosyncratic dependence between the variables is handled by a piecewise stationary VAR model. We propose an accompanying two-stage data segmentation methodology which fully addresses the challenges arising from the latency of the component processes. Its consistency in estimating both the total number and the locations of the change points in the latent components, is established under conditions considerably more general than those in the existing literature. We demonstrate the competitive performance of the proposed methodology on simulated datasets and an application to U.S. blue chip stocks data. for this article are available online. [ABSTRACT FROM AUTHOR]

Currently, some time series models are formed to accommodate several aspects: the reciprocal relationship between variables, the influence of exogenous variables, spatial relationships, and nonlinear relationships between variables. Threshold vector autoregressive with exogenous variables (E-TVAR) is formed to overcome the reciprocal relationship between variables, the influence of exogenous variables, and the nonlinear relationship between variables (Tsagkanos et al. 2018). Spatial vector autoregressive (SpVAR) of Beenstock-Felsenstein (2019) can capture the phenomenon of the relationship between endogenous variables and spatial influence. However, these models cannot consider all four aspects simultaneously. Some economic variables, such as inflation and money outflow, are reciprocally related, influenced by metric exogenous variables, interrelated between regions, and have a nonlinear relationship (Islam-Ahmed 2023, Hendayanti et al. 2017, Yuhan-Sohibien 2018, Suhartono et al. 2018). Therefore, this study aims to propose the TSpVARX that can contain all four of these simultaneously. We conduct a theoretical study to prove the consistent and asymptotically normal properties of the maximum likelihood estimation (MLE) estimator in the g-th regime of the TSpVARX model. In addition, we also conduct a simulation study with some scenarios to evaluate the performance of the MLE estimator in the TSpVARX model. After we conduct theoretical studies and simulations, the TSpVARX model is applied to predict the inflation and money outflow of Yogyakarta, Solo, and Semarang. Our study shows that TSpVARX is better than SpVARX in predicting the inflation of those three cities and the money outflow of Semarang based on the root mean square error (RMSE) and and symmetric mean absolute percentage error (SMAPE). [ABSTRACT FROM AUTHOR]

We develop a tractable way of estimating the parameters ruling the nonlinearity in the popular smooth transition VAR model, and identify structural shocks using external instruments. This jointly offers an alternative to the option of identifying shocks recursively and calibrating key parameters. In an illustration, we show that monetary policy shocks generate larger effects on economic activity during economic expansions compared to economic recessions. We then document that calibrating rather than estimating the parameters ruling the nonlinearity of the model can lead to values for which the key results are lost. This suggests caution in the calibration of these parameters. [ABSTRACT FROM AUTHOR]

Smart cities are rapidly gaining momentum around the world as new opportunities to shift towards a sustainable urban future. Despite their promising solutions, there is so far little evidence on how smart city initiatives can achieve urban sustainability. This study explores the impact of smartness on urban sustainability by introducing the concept of smart complementarity and investigating whether the systemic governance of smart complementarities has a positive impact on sustainability. To this end, we employ a panel vector autoregression model with panel data for 26 smart cities from 2009 to 2019 to test the complex dynamic relationships between smart city drivers and dimensions of sustainability. Such analysis seems crucial for smart city governance and whether the vision of a smart agenda is compatible with urban sustainability. The findings suggest that while smart city initiatives have improved economic sustainability, they have compromised environmental and social sustainability. This points to the need for a coordinated approach to address the complexity of urban systems and sustainability challenges. [ABSTRACT FROM AUTHOR]

The article is devoted to highlighting the results of research aimed at improving existing approaches to conducting a comprehensive analysis of the nature of the relationships between the parameters of the state's monetary and debt policy. Among other things, such indicators as the size of the domestic public debt, the size of international reserves, the level of inflation, the exchange rate and the discount rate are taken into account. The proposed approach to the analysis of the influence of managed monetary indicators on the size of the internal state debt proved that the use of the ARDL model gives slightly better results than the VECM in terms of the adequacy of the description and forecasting of the studied process. At the same time, both models proved their ability to describe the change in the size of the internal state debt, and have high values of coefficients of determination and low values of the indicator of the average absolute percentage error. In the future, it is recommended to use it for the analysis of the causality of the influence, discriminant analysis and the expediency of changing monetary instruments independently of other instruments. On the basis of the developed models, the results were obtained, which allow us to draw conclusions that the system continues to remain stable after fluctuations of key factors capable of destabilizing it and will be able to return to its previous state after a shock for some time. This is confirmed by the fact that, on the basis of statistical tests, the existence of a long-term relationship between the investigated indicators and the impulse response function together with the decomposition of the variance of the VAR model was proved. [ABSTRACT FROM AUTHOR]

Value at risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital. Estimation of the VaR entails the quantification of prospective losses in a portfolio of investments, using a certain likelihood, under normal market conditions within a specific time period. The objective of this article is to construct a model and estimate the VaR for a diversified portfolio consisting of multiple cash commodity positions driven by standard Brownian motions and jump processes. Subsequently, a thorough analytical estimation of the VaR is conducted for the proposed model. The results are then applied to two distinct commodities—corn and soybean—enabling a comprehensive comparison of the VaR values in the presence and absence of jumps. [ABSTRACT FROM AUTHOR]

Public debt is determined by borrowings undertaken by a government to finance its short- or long-term financial needs and to ensure that macroeconomic objectives are met within budgetary constraints. In Romania, public debt has been on an upward trajectory, a trend that has been further exacerbated in recent years by the COVID-19 pandemic. Additionally, a significant non-economic event influencing Romania's public debt is the war in Ukraine. To analyze this, a stochastic debt sustainability analysis was conducted, incorporating the unique characteristics of Romania's emerging market into the research methodology. The projections focused on achieving satisfactory results by following two lines of research. The first direction involved developing four scenarios to assess the risks presented by macroeconomic shocks. Particular emphasis was placed on an unusual negative shock, specifically the war in Ukraine, with forecasts indicating that the debt-to-GDP ratio could reach 102% by 2026. However, if policymakers implement discretionary measures, this level could be contained below 88%. The second direction of research aimed to establish the maximum safe limit of public debt for Romania, which was determined to be 70%. This threshold would allow the emerging economy to manage a reasonable level of risk without requiring excessive fiscal efforts to maintain long-term stability. [ABSTRACT FROM AUTHOR]

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This paper investigates the interconnectedness patterns between agricultural commodities, crude oil, and ethanol, along with their determinants before and during the COVID-19 pandemic and the Russia–Ukraine conflict. We employ a time-varying parameter vector autoregression model to analyze interconnected behaviors among energy and agricultural commodities. Additionally, quantile regression is used to assess the impact of financial and economic fundamentals on transmission mechanisms in commodity markets. The empirical findings reveal time-varying and crisis-responsive linkages between energy and agricultural commodities, particularly during the COVID-19 pandemic and Russia–Ukraine conflict. Furthermore, economic and financial market uncertainties emerge as significant determinants of the interconnectedness between these commodity groups. [ABSTRACT FROM AUTHOR]

This paper empirically examines the effect of domestic monetary policy shock on external commercial borrowings (ECBs) to India for the period 2000:Q2 to 2019:Q4. The impact of domestic monetary policy shock on ECBs is assessed using two variables: interest rate differential and domestic money supply growth. The paper employs a structural VAR model and utilizes impulse response functions and forecast error variance decomposition analysis to derive empirical results. The empirical results based on impulse response functions indicate that the interest rate differential has a positively significant effect on external commercial borrowings to India, while the effect of domestic money supply growth is found to be negative. The forecast error variance decomposition analysis indicates that the interest rate differential explains about 14.6% and domestic money supply growth explains about 2% of the total variation in external commercial borrowings, implying that domestic monetary policy variables together explain about 16.6% of the total variation in ECBs to India. The results suggest that the central bank, through monetary policy interventions, can influence and manage the flow of external commercial borrowings to India. Furthermore, the study finds that the exchange rate, domestic industrial activity, domestic output growth, domestic country's creditworthiness, and domestic macroeconomic instability are other significant determinants of external commercial borrowings. [ABSTRACT FROM AUTHOR]

This paper studies the terms‐of‐trade effects of economy‐specific shocks to productivity in developing economies using a panel vector autoregression model with interactive fixed effects and the "max‐share" approach. We find that the terms of trade after unanticipated changes in economy‐specific productivity mostly exhibit insignificant dynamics in the developing economies. However, we present evidence that they can significantly deteriorate in a particular set of developing countries, namely the ones with a high score on technological deepening and upgrading. [ABSTRACT FROM AUTHOR]

Summary: We provide new evidence that the transmission of oil price shocks to the US economy has changed with the shale oil boom. To show this, we develop a time‐varying parameter factor‐augmented vector autoregressive (FAVAR) model with a large data environment of state‐level, industry, and aggregate US data. The model effectively captures potential spillovers between oil and non‐oil industries, as well as variation over time. Specified in this way, we find that investment, income, industrial production, and (non‐oil) employment in most oil‐producing and some manufacturing‐intensive US states increase following an oil‐specific shock—effects that were not present before the shale oil boom. [ABSTRACT FROM AUTHOR]

The reformed Basel framework has left value at risk (VaR) as a basic tool of validating risk models. Within this framework, VaR independence tests have been regarded as critical to ensuring stability during periods of financial turmoil. However, until now, there is no consent among researchers regarding the choice of the appropriate test. The available procedures are either inaccurate in finite samples or need to rely on Monte Carlo simulations. To remedy these problems, we propose a new method for testing VaR models, based on the distribution of the number of runs. It outperforms the existing methods in two main aspects: First, it is exact in finite samples and thus allows for perfect control over the Type 1 error; second, its distribution is available in a closed form, so it does not require simulations before implementation. We show that it is the most adequate current procedure for testing low‐level VaR series, which corresponds to today's regulatory standards. [ABSTRACT FROM AUTHOR]

Analysing the coordination and spatial spillover effects between industrial development and environmental protection is crucial for regional sustainable development. This paper examines the spatial and temporal evolution characteristics of the coupling coordination level between industrial development and environmental protection, as well as the evolution of the decoupling type within this system. under the evolution of the coupling coordination mode and decoupling model. Additionally, it analyzes the spatial spillover effects using the spatial panel vector autoregression model. The main conclusions are as follows: First, the coordination level between industrial development and environmental protection is increasing over time in each province. Second, the decoupling status of industrial development and environmental protection shows distinct stages, progressing towards strong decoupling over time. Third, regional industrial development has a positive spillover effect on both industrial development and environmental protection in neighbouring regions, whereas regional environmental protection has a negative spillover effect on industrial development but a positive spillover effect on environmental protection in neighbouring regions. This paper suggests that provinces should focus on the synchronisation industrial development and environmental protection and implement differentiated strategies based on their specific conditions. Additionally, joint management and governance should be pursued between neighbouring provinces. [ABSTRACT FROM AUTHOR]

The vector autoregressive (VAR) model has been popularly employed in operational practice to study multivariate time series. Despite its usefulness in providing associated metrics such as the impulse response function (IRF) and forecast error variance decomposition (FEVD), the traditional VAR model estimated via the usual ordinary least squares is vulnerable to outliers. To handle potential outliers in multivariate time series, this paper investigates two robust estimation methods of the VAR model, the reweighted multivariate least trimmed squares and the multivariate MM-estimation. The robust information criteria are also proposed to select the appropriate number of temporal lags. Via extensive simulation studies, we show that the robust VAR models lead to much more accurate estimates than the original VAR in the presence of outliers. Our empirical results include logged daily realized volatilities of six common safe haven assets: futures of gold, silver, Brent oil and West Texas Intermediate (WTI) oil and currencies of Swiss Francs and Japanese Yen. Our sample covers July 2017–June 2020, which includes the history-writing price drop of WTI on April 20, 2020. Our baseline results suggest that the traditional VAR model may significantly overestimate some parameters, as well as IRF and FEVD metrics. In contrast, robust VAR models provide more reliable results, the validity of which is verified via various approaches. Empirical implications based on robust estimates are further illustrated. [ABSTRACT FROM AUTHOR]

Background: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. Methods: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. Results: When the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9). Conclusion: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications. [ABSTRACT FROM AUTHOR]