Interesting Rules Research Articles

Monetary Policy and Sentiment-Driven Fluctuations

Sentiments, or beliefs about aggregate demand, can be self-fulfilling in models departing slightly from the complete information benchmark in the New Keynesian framework. Through its effect on aggregate variables, the policy stance determines the degree of complementarity in firms' production (pricing) decisions and consequently, the precision of endogenous signals that firms receive. As a result, aggregate fluctuations can be driven by both fundamental and non-fundamental shocks. The distribution of non-fundamental shocks is endogenous to policy, introducing a novel tradeoff between stabilizing output and inflation. Both strong inflation targeting and nominal flexibilities increase the variance of non-fundamental shocks, which are shown to be suboptimal. Moreover, the Taylor principle is no longer sufficient to rule out indeterminacy. Instead, an interest rate rule that places sufficiently low weight on inflation eliminates non-fundamental volatility and thereby the output-inflation tradeoff.

Exploring counter-cyclical monetary policy in a small open economy using the portfolio balance approach

Whether or not the money supply is endogenously determined in response to demand variations has been the subject of recent debates. Regardless of the answer to this question, central banks can and do react to cyclical fluctuations, and their responses, in turn, affect the balance of payments and the foreign asset position. We explore the feasibility of a counter-cyclical monetary policy rule in a small open-economy portfolio balance set-up where assets are imperfect substitutes, capacity utilization endogenously adjusts in a neo-Kaleckian manner to clear the goods market, the exchange rate is flexible, and prices are fixed. This set-up, under some non-restrictive conditions, generates a stable steady-state solution, suggesting that a monetary policy based on a counter-cyclical interest-rate rule is a viable option for macroeconomic stabilization.

Inflation targeting and output stabilization in an estimated monetary model

This paper studies determinacy conditions in a monetary model with an interest rate rule. In addition to the inflation rate as an argument of the policy rule, we introduce a second argument: output, a case empirically relevant but overlooked in the existing literature on monetary models with flexible prices. Firstly, we estimate a model with money in utility and the production function. We find that an aggressive response to inflation does not necessarily guarantee determinacy anymore. Secondly, we compare monetary policy in the pre-Volcker era and the post-Volcker era. We find that, after the appointment of Paul Volcker, the Federal Reserve started to respond more aggressively to inflation and less aggressively to output. Indeterminacy is pervasive: the equilibrium is indeterminate in both sub-samples. Thirdly, we estimate the impulse response functions. We find that monetary shocks, sunspot inflation shocks, and productivity shocks have long-lasting effects on inflation, output, and interest rates.

The political economy of currency unions

How can monetary and fiscal policy sustain a currency union when member states have an exit option? This paper derives an interest rate rule that features state-dependent country weights with which the central bank can prevent a break-up. A simulation reveals that this policy rule lacks firepower and can only extend the lifetime of the union for a while. While monetary policy is more potent in unions with more member states or setups with local currency pricing, it is still true that even a simple fiscal union with lump-sum transfers is better suited to prevent a break-up. Environments with lower risk sharing, the ZLB or wage rigidity make monetary policy even less effective.

Inflation targeting, output stabilization, and real indeterminacy in monetary models with an interest rate rule

AbstractCentral banks set the nominal interest rate to target inflation and stabilize output. In monetary models, monetary policy affects output directly via the wealth effect. I show that in these models, the response of the central bank to fluctuations in output may induce real indeterminacy even if the Taylor principle is satisfied. I find that the determinacy conditions depend on the interest elasticity of output and generally, the Taylor principle is neither necessary nor sufficient for determinacy. This is in stark contrast with the New Keynesian model where a sufficiently strong policy response to inflation or output usually ensures determinacy.

Taylor rules during a pandemic: a counterfactual exercise

ABSTRACT We conduct a counterfactual exercise to determine the potential effectiveness of conventional monetary policy to limit the economic damage caused by labour market uncertainties related to a pandemic. We model the labour market uncertainty as a second-order shock to a time-varying job separation rate. We show that the economic effects of heightened uncertainty in the job separation rate depend crucially on the Taylor-rule type adopted by the monetary authority. We explore the quantitative implications of pandemic-induced job separation uncertainty shocks under alternative interest rate rules and find that recessions following job separation uncertainty shocks are more severe under rules with no interest rate smoothing and strong inflation targeting.

Is TCR effective in reducing tax avoidance in Indonesia?

Thin capitalization is a tax avoidance technique using funding sources that prioritize debt over capital. Thin capitalization can be used as a technique to avoid taxes because there is a difference in treatment between debt and capital as a source of funding in tax regulations. Thin capitalization rule (TCR) is domestic tax system to reduce thin capitalization. This study aims to examine the effect of implementing thin capitalization rules on reducing tax avoidance in Indonesia. This study is a quantitative study. The data used are secondary data obtained from multinational companies listed on the IDX from 2013 to 2020 by excluding companies that are excluded from PMK-169: bank companies, financing institutions, insurance, reinsurance, operating in the oil and gas mining sector, companies whose entire income is subject to final tax, and infrastructure. The data analysis method used in this study is regression using the eviews 12.0 program. The results show that the implementation of thin capitalization rule (TCR) does not reduce tax avoidance. These results provide empirical evidence that the government need to consider using thin capitalization rule with the interest to Earnings Before Interest, Tax, Depreciation, and Amortization (EBITDA) rule mechanism rather than the Debt-to-Equity Ratio (DER) rule mechanism and arm’s length rule mechanism.

Monetary policy rules and zero lower bound on nominal interest rates in a cost-channel economy

This study analyzed monetary policy rules in a cost-channel economy with a zero lower bound on nominal interest rates (ZLB). The cost channel reflects the supply-side effects of the monetary policy. The ZLB is considered an occasionally binding constraint in a new Keynesian setting. Taylor-type interest rules represent the monetary policy rules, and the novel forward guidance rule introduced in this study is anticipated and endogenous. Under monetary policy rules, a cost-channel economy is more likely to fall into a liquidity trap and remain there longer than a no-cost-channel economy. Accordingly, monetary policy transmission differs from that in a no-cost-channel economy in the ZLB presence. Furthermore, if agents expect future recessions, achieving the inflation target is more challenging in cost-channel economies. Irrespective of the existence of a cost channel, the forward guidance rule reduces the frequency and depth of liquidity-trapped recessions compared to the Taylor-type rules and avoids deflation bias.

Exchange market pressure in interest rate rules

Many central banks pursue some kind of exchange rate objective. We derive what variables the central bank should look at when setting the interest rate to implement a given objective. Exchange market pressure (EMP), the tendency of the exchange rate to change, emerges as the key variable. This yields a policy rule for the interest rate where EMP is added to, say, a Taylor rule. The coefficient for EMP depends on two structural parameters, namely the effectiveness of the interest rate to ward off depreciation, and the degree of exchange rate management. The rule can implement many regimes, from floating to intermediate to fixed rates. It can be applied to many models, and we illustrate it in a New Keynesian model for a small open economy.

Uncertainty shocks and monetary policy rules in a small open economy

Abstract Post the great financial crisis (GFC) of 2008–2009, there has been a surge in the macroeconomics literature on aggregate uncertainty. Although the recent literature has recognized the adverse real effects of global uncertainty shocks in emerging market economies (EMEs), the role of monetary policy in offsetting these adverse effects and their link with the exchange rates is not explored in the literature. We find that the currently followed interest rate rules (IRRs) under a flexible inflation-targeting regime are ineffective in stabilizing the domestic economy during periods of high global uncertainty in the EMEs. Using a small open economy new Keynesian DSGE model with Epstein–Zin preferences and second-moment demand shocks, we compare and propose alternate monetary policy rules that significantly reduce welfare losses. We find that the best monetary policy rule in terms of welfare depends on the nature of shock that is, first-moment or second-moment shock.

(Mis)Alignment between observed and expected monetary policy: The case of Brazil

The economic agents’ perception of the monetary policy might not coincide with the central bank conduct. We investigate this issue by using both actual and expected data identified by professional forecasters for Brazil, an emerging economy that has long adopted the inflation-targeting regime. We estimate observed, expected, and forward-looking interest rate rules, apply Full-Information Rational Expectations (FIRE) tests, assess restrictions implied by models of information rigidity, and perform several robustness checks. The estimated policy rules respond to inflation deviations and output gap according to theoretical grounds. Professional forecasters, however, do not believe in aggressiveness to fight inflation in longer forecasting horizons, while FIRE tests unveil information rigidity in the short run. There is a misalignment between the Central Bank practice and the private agents’ perception of the monetary policy in the long but not in the short run, suggesting unanchored long-term inflation expectations.

Round-up of fashion-related IP decisions 2023

Abstract In 2023, courts and competent authorities in Australia, Czech Republic, Denmark, France, Germany, Italy, the Netherlands, Poland, Spain and the United Kingdom delivered a series of important rulings of interest to and affecting the fashion sector. This contribution provides a round-up of the most significant fashion-related IP judgments issued in the period 1 January to 31 December 2023. The analysis is organized by substantive IP right and enforcement aspects, and is organized by country.

Conformable Triple Sumudu Transform with Applications

One of the important topics in applied mathematics is the topic of integral transformations, due to their importance in electrical engineering applications, including communications in particular, and other sciences. In this work, one of the most important transformations in its three dimensions was presented, which is the triple Sumudu transform, including solving some real-life applications of physics, some of which have not been solved using such an integral transform before. In this work, we extend the Sumudu transform formula to the conformable fractional order, as well as other interesting and significant rules. The general analytical solution of a singular and nonlinear conformable fractional differential equation based on the conformable fractional Sumudu transform is also presented in this paper. The general solutions of several linear and nonhomogeneous conformable fractional differential equations can be obtained using the method we’ve proposed. As a result, our results reveal that our proposed method is an efficient one that can be used for solving all conformable fractional differential equations. The relationship between the Sumudu integral transform and other important and recently proposed integral transforms are also discussed. Finally, the triple Sumudu transform is used to solve boundary value problems, such as the heat equation with boundary values. The triple Sumudu integral transform is also used to solve linear partial integro-differential equations. The transform capability to handle such equations has been proven via its utilization in three applications.

Determinacy and E‐Stability with Interest Rate Rules at the Zero Lower Bound

AbstractWe evaluate and compare alternative interest rate rules, namely, average inflation targeting (AIT), price‐level targeting (PLT), and traditional inflation targeting rules, in a standard New Keynesian model that features recurring, transient zero lower bound regimes. We use determinacy and expectational stability (E‐stability) of equilibrium as the criteria for stabilization policy. We find that PLT policy, including nominal GDP targeting as a special case, most effectively promotes determinacy and E‐stability among the policy frameworks, whereas standard inflation targeting rules are prone to indeterminacy. AIT can induce determinacy and E‐stability effectively, provided the averaging window is sufficiently long.

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China.

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

Deep Structured State Learning for Next-Period Recommendation.

User activities in real systems are usually time-sensitive. But, most of the existing sequential models in recommender systems neglect the time-related signals. In this article, we find that users' temporal behaviors tend to be driven by their regularly changing states, which provides a new perspective on learning users' dynamic preference. However, since the individual state is usually latent, the event space is high dimensional, and meanwhile, temporal dependency of states is personalized and complex; it is challenging to represent, model, and learn the time-evolving patterns of user's state. Focusing on these challenges, we propose a deep structured state learning (DSSL) framework, which is able to learn the representation of temporal states and the complex state dependency for time-sensitive recommendation. Extensive experiments demonstrate that the DSSL achieves competitive results on four real-world recommendation datasets. Furthermore, experiments also show some interesting rules for designing the state dependency network.

Promoting Educational Reform to Enhance Talent Cultivation Quality of Civil Engineering Majors Based on Deep Learning Background

Abstract This paper focuses on improving the quality of talent cultivation for civil engineering students through reforming teaching quality and course selection management. Regarding teaching quality management, the article proposes to use improved Apriori algorithm to generate high interest rules and utilize SQL for effective data manipulation. Regarding course selection management, the content-based recommendation algorithm is used to optimize the course selection mechanism. After the implementation of the reform, the proportion of low scores in the self-quality evaluation of college students was significantly reduced, and the evaluation indexes of teachers also showed significant differences. In addition, employers were satisfied with the competence of civil engineering graduates. These results indicate that data-driven teaching management and personalized course selection recommendation can effectively improve education quality and student satisfaction.

Mining Several Kinds of Multi-Dimensional Temporal Association Rule

To improve the effectiveness and efficiency of mining tasks, constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules. Previously proposed methods are mainly contributed to handling a single constraint and only consider the constraints which are characterized by a single attribute value. In this paper, we propose an approach to mine association rules with multiple constraints constructed by multi-dimensional attribute values. Our proposed approach basically consists of three phases. First, we collect the frequent items and prune infrequent items according to the Apriority property. Second, we exploit the properties of the given constraints to prune search space or save constraint checking in the conditional databases. Third, for each item set possible to satisfy the constraint, we generate its conditional database and perform the three phases in the conditional database recursively. Our proposed algorithms can exploit the properties of constraints to prune search space or save constraint checking. Therefore, our proposed algorithm is more efficient than the revised FP (Frequent Pattern)-growth and FIC (Fractal Image Compression) algorithms.

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.