Search results

The purpose of this paper is to present the design and implementation of a zero‐initial‐velocity self‐motion scheme on a six degrees of freedom (six‐DOF) planar robot manipulator.

In view of the existence of physical limits in an actual robot manipulator, both joint‐angle limits and joint‐velocity limits are initially incorporated into the proposed self‐motion scheme for practical purposes. The proposed self‐motion scheme is then reformulated as a quadratic program (QP) and resolved at the joint‐velocity level. By combining the zero‐initial‐velocity constraint, the resultant QP can prevent the occurrence of a large initial joint velocity. Finally, based on the conversion technique of QP to a linear variational inequality, a numerical computing algorithm is presented to solve the QP and the corresponding self‐motion scheme.

The proposed zero‐initial‐velocity self‐motion scheme eliminates the phenomenon of the abrupt and drastic increase in joint velocity at the beginning of the self‐motion task execution. Simulative and experimental results based on a practical six‐DOF planar robot manipulator further verify the realizability, effectiveness and accuracy of the proposed self‐motion scheme. Based on the simulative results, the joint angle and the joint velocity meet the joint physical constraints.

The paper provides effective methods for handling the physical limits, the design of zero‐initial velocity, and the conversion from joint angle and joint velocity to motor‐driving pulses. Thus, the effective and safe self‐motion control of a manipulator is realized.

The paper describes the design and implementation of a zero‐initial‐velocity self‐motion scheme.

Significant evidence in the literature points to money demand instability and therefore inaccurate forecasting. In view of this issue, this chapter seeks to use a method, innovative for money demand literature, that is, the machine learning model to predict money demand. Specifically, this chapter uses Random Forest Regression to predict money demand using monthly data in the Indian context over the period April-1996 to December-2018 using the variables usually used in literature. The chapter finds that in money demand prediction, the Random Forest Regression performs fairly well. The results are also compared to traditional models and it is found that the Random Forest Regression model has the potential to enhance the prediction of money demand over what traditional models predicts.

This paper examines the predictive content of financial variables above and beyond past GDP growth in a small open economy in the Eurozone. We aim to clarify potential differences in forecasting economic activity during periods of steady growth and economic turbulence.

The out-of-sample forecasting analysis is conducted recursively for two different time periods: the steady growth period from 2004:1 to 2007:4 and the financial crisis period from 2008:1 to 2011:2.

Our results from Finland suggest that the proper choice of forecasting variables relates to general economic conditions. During steady economic growth, the preferable financial indicator is the short-term interest rate combined with past growth. However, during economic turbulence, the traditional term spread and stock returns are more important in forecasting GDP growth.

The results highlight the importance of long-term interest rates in determining the level of the term spread when the central bank implements a zero interest rate policy. Moreover, during economic turbulence, stock markets are able to signal the expected effects of unconventional monetary policy on GDP growth.

This paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating value-at-risk (VaR) and expected shortfall (ES) in emerging market at alternative risk levels.

Using the case study of the Vietnamese stock market, the author produced one-day-ahead VaR and ES forecast from seven individual risk models and ten alternative forecast combinations. Next, the author employed a battery of backtesting procedures and alternative loss functions to evaluate the global predictive accuracy of the different methods. Finally, the author investigated the relative performance over time of VaR and ES forecasts using fluctuation test.

The empirical results indicate that, although combined forecasts have reasonable predictive abilities, they are often outperformed by one individual risk model. Furthermore, the author showed that the complex combining methods with optimised weighting functions do not perform better than simple combining methods. The fluctuation test suggests that the poor performance of combined forecasts is mainly due to their inability to cope with periods of instability.

This study reveals the limitation of combining strategies in the one-day-ahead VaR and ES forecasts in emerging markets. A possible direction for further research is to investigate whether this finding holds for multi-day ahead forecasts. Moreover, the inferior performance of combined forecasts during periods of instability motivates further research on the combining strategies that take into account for potential structure breaks in the performance of individual risk models. A potential approach is to improve the individual risk models with macroeconomic variables using a mixed-data sampling approach.

First, the authors contribute to the literature on the forecasting combinations for VaR and ES measures. Second, the author explored a wide range of alternative risk models to forecast both VaR and ES with recent data including periods of the COVID-19 pandemic. Although forecast combination strategies have been providing several good results in several fields, the literature of forecast combination in the VaR and ES context is surprisingly limited, especially for emerging market returns. To the best of the author’s knowledge, this is the first study investigating predictive power of combining methods for VaR and ES in an emerging market.

This article analyzes the impact of monetary policy on income inequality across 16 advanced economies. The author investigates three key points: (1) the relationship between domestic monetary policy and domestic income inequality, (2) the spillover effect of USA monetary policy (including quantitative easing) on international inequality and (3) the quantitative influence of the monetary policies of both the USA and the Eurozone on the formation of domestic income inequalities.

The author employed the Global Vector Autoregressive (GVAR) model, which uses Vector Autoregressive with Exogenous Variables (VARXs) models of each economy to build an integrated system that enables us to evaluate individual responses to global shocks.

The author's analysis reveals that (1) contractionary monetary policy exacerbates domestic inequality and (2) USA monetary policy, including quantitative easing, affects international inequality. Furthermore, the author's variance decomposition analysis highlights that USA monetary policy is especially influential on income inequality in Norway and Sweden. Additionally, the cointegrating analysis confirms that monetary policy's impact on inequality persists in the long term.

Most of the studies focused on investigating domestic economies as closed economies. However, the author's approach differs in that the author uses the GVAR, which treats all economies as open. This allows us to incorporate the world economy into the domestic dynamics and connect the economies using bilateral trade. Another advantage of the GVAR is that it captures spillover effects by modeling each economy and constructing the international economy.

The purpose of this research is to examine the impact of alterations in the path of monetary policy rates on inflation via the supply side of an emerging economy.

The study employed semi-annual data covering the period 2007S1 to 2020S2 on the inflation rate, the combined outputs of industry and agriculture, the lending rate, and the monetary policy rate. The vector autoregression model was estimated and counterfactual simulation exercises were conducted.

The study revealed that a move from a higher to a lower monetary policy rate regime resulted in a shift in inflation from a higher to a lower regime. In particular, a 200-basis point reduction in the monetary policy rate over the simulation horizon produces a 1.3% fall in the inflation rate over the same period.

The study has a limitation due to the unavailability of a long-span dataset on all relevant variables. As a result, it is important to exercise caution when interpreting the study's findings. A potential area for further research is to explore how changes in interest rates impact inflation in the real economy by utilising other multiple-variable time series techniques.

It is the opinion of the authors that for inflation in Ghana to move to a lower regime, conscious efforts should be made by the monetary authorities to gradually move from a regime of a high monetary policy rate to a lower one.

In particular, a 200-basis point reduction in the MPR over the simulation horizon produces a 1.3% fall in the inflation rate over the same period.

This study enhances the authors' knowledge of how monetary policy can affect inflation in developing countries through the supply-side channel.

The purpose of this paper is to estimate the impact of the 2008 and 2020 economic crises on employment in Spain.

The authors perform a counterfactual analysis, combining intervention (interrupted time series) analysis and conditional forecasting to estimate a “crisis-free” scenario. These counterfactual estimates are used as a synthetic control, to be compared with the observed values of the main variables of the Spanish Labor Force Survey (EPA).

The authors measure the effect on Spanish employment of the 2008 recession and the ongoing COVID/Ukraine crisis and the speed of recovery, which yields a rigorous dating for the beginning and end of the crises studied. Finally, the authors provide estimates about which part of the employed and unemployed people was in furlough (ERTE) based on microdata provided by the Spanish Institute of Statistics.

To the best of the authors’ knowledge, there are no counterfactual studies covering all the basic variables in EPA and no estimates for the effect of ERTEs on the basic employment variables. Finally, the authors combine well-known intervention and forecasting techniques into an integrated framework to assess the effects of both, past and ongoing crises.

Advocates of quantitative easing (QE) policies have emphasized some evidence that structural models do not predict long-term asset yields as well as naive forecasts, implying that predictions of price reversals cannot be profitable and that QE effects are not transitory. The purpose of this study is to reconsider the out-of-sample forecasting performance of structural time series processes relative to that of a random walk with or without drift.

This study uses bivariate vector autoregression and Markov switching representations to generate out-of-sample forecasts of ten-year sovereign bond yields, when the information set is augmented by including the growth rate of the monetary base, and the estimation relies on monthly data from countries that have pursued unconventional policies over the last decade.

The results show that naive forecasts are not better than those of structural time series models, based on root mean squared errors, while the Markov model provides additional information on price reversals, through probabilistic inferences regarding policy regime switches, which can induce agents to counteract QE interventions and reduce their effectiveness.

The novel features of this work are the use of a large information set including the instrument of unconventional monetary policy, the use of a structural model (Markov process) that can really inform about potential asset price reversals and the use of a large sample over which QE policies have been pursued.

The purpose of this study is to suggest a new framework that we call the CIR#, which allows forecasting interest rates from observed financial market data even when rates are negative. In doing so, we have the objective is to maintain the market volatility structure as well as the analytical tractability of the original CIR model.

The novelty of the proposed methodology consists in using the CIR model to forecast the evolution of interest rates by an appropriate partitioning of the data sample and calibration. The latter is performed by replacing the standard Brownian motion process in the random term of the model with normally distributed standardized residuals of the “optimal” autoregressive integrated moving average (ARIMA) model.

The suggested model is quite powerful for the following reasons. First, the historical market data sample is partitioned into sub-groups to capture all the statistically significant changes of variance in the interest rates. An appropriate translation of market rates to positive values was included in the procedure to overcome the issue of negative/near-to-zero values. Second, this study has introduced a new way of calibrating the CIR model parameters to each sub-group partitioning the actual historical data. The standard Brownian motion process in the random part of the model is replaced with normally distributed standardized residuals of the “optimal” ARIMA model suitably chosen for each sub-group. As a result, exact CIR fitted values to the observed market data are calculated and the computational cost of the numerical procedure is considerably reduced. Third, this work shows that the CIR model is efficient and able to follow very closely the structure of market interest rates (especially for short maturities that, notoriously, are very difficult to handle) and to predict future interest rates better than the original CIR model. As a measure of goodness of fit, this study obtained high values of the statistics R² and small values of the root of the mean square error for each sub-group and the entire data sample.

A limitation is related to the specific dataset as we are examining the period around the 2008 financial crisis for about 5 years and by using monthly data. Future research will show the predictive power of the model by extending the dataset in terms of frequency and size.

Improved ability to model/forecast interest rates.

The original value consists in turning the CIR from modeling instantaneous spot rates to forecasting any rate of the yield curve.

This study aims to address the fundamental question on how the major players in the economy dynamically interact with each other: among the central bank, the investors in the bond market and the firms and consumers that contribute to the economic growth, who gets information from whom, when and why?

To answer “who follows whom,” the authors apply a novel technique to examine the lead–lag relations between three time series, the federal funds rate, the treasury yield curve and the gross domestic product (GDP). To investigate “when and why,” the authors combine the lead–lag relations with principal component analysis to cluster economic states that are similar with respect to the eight macroeconomic variables.

The authors show that during the period 1977–2019, the bond market potentially obtained information from the federal funds rate (61% of the time) and less often (34% of time) from the changes in the GDP. Meanwhile, the funds rate decision by the Federal Reserve seems to lead the economic growth about 63% of the time. The analysis also suggests that the bond market obtained information directly from GDP when unemployment and inflation was high. In addition, the authors find that the federal funds rate was leading the GDP when the GDP deviated from the target value, consistent with the Federal Reserve’s policy of boosting and damping the economy when the GDP growth is low or high, respectively.

This study provides insights into the fundamental questions that have important implications for empirical work on the monetary policy, financial stability and economic activities.