The Experts below are selected from a list of 40194 Experts worldwide ranked by ideXlab platform
Mihaela Simionescu - One of the best experts on this subject based on the ideXlab platform.
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A STRateGY TO IMPROVE THE Inflation Rate FORECASTS IN ROMANIA
Internal Auditing and Risk Management, 2020Co-Authors: Mihaela SimionescuAbstract:The main goal of this research is to improve the degree of accuracy for Inflation Rate forecasts in Romania. The Inflation was forecasted using a vectorialautoregressive model. According to Granger test for causality, the relationship between the two variables is reciprocal. The Inflation Rate volatility is due mainly to the evolution of this indicator, the influence decreasing insignificantly in time, not descending under 96%. More than 87% of the variation in unemployment Rate is explained by the own volatility for all lags. For the first lag the Inflation is explained only by its evolution, the contribution of the unemployment Rate to Inflation variation being null. The Inflation Rate dynamic simulations (deterministic and stochastic) on the horizon 2011-2013 were more accuRate than the predictions based on Dobrescu model. The combined forecasts proved to be a good stRategy of improving the VAR forecasts and those based on Dobrescu model only if the dynamic and deterministic simulations were combined with Dobrescu’s anticipations on the horizon 2011-2013.
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The Prediction of Monthly Inflation Rate in Romania 1
2020Co-Authors: Mihaela SimionescuAbstract:Predictions for Inflation Rate are constructed for underlying the decisional process at macroeconomic level. The central banks also build forecast intervals for Inflation Rate to reflect the degree of uncertainty of their point predictions. The purpose of constructing forecast intervals is related to the improvement of macroeconomic policies and of decisional process. The utility of this demarche is higher for the National Bank of Romania that proposes Inflation Rate prediction intervals in the context of indicator targeting. In this paper, prediction intervals are constructed for monthly Inflation Rate in Romania using various methods: the bootstrap technique (percentile prediction intervals and percentile-t prediction intervals) and historical errors method based on root mean square error. The latter method started from a moving average process of the Inflation Rate, the static and dynamic forecasts not having a high degree of accuracy, but being unbiased. For the monthly forecasts over March 2014-June 2014, the bootstrapped forecast intervals based on a simple regression model where the unemployment Rate is the exogenous variable are less plausible than the intervals based on historical root mean square error. However, in all the cases the Inflation has a tendency to increase in time. This research could be developed in order to construct a fan chart for Inflation Rate in Romania, this graph being based on forecast intervals.
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The Evaluation Of Quarterly Forecast Intervals For Inflation Rate In Romania
2020Co-Authors: Mihaela Simionescu, Irina DraganAbstract:The forecast uncertainty was one of the causes of the recent economic crisis and its evaluation became more necessary nowadays. The aim of this paper is to build and assess different types of forecast intervals for quarterly Inflation Rate in Romania. The Bootstrap Bias-corrected-acceleRated (BCA) forecast intervals outperformed the intervals based on historical errors, four out of six values of Inflation Rate being placed in the first type of intervals during Q3:2013-Q4:2014. The likelihood ratio tests and the chi-square test indicated that there are significant differences between the ex-ante probability of 0.95 and the real probabilities for both types of forecast intervals. As a methodological novelty, Monte Carlo and bootstrap simulations were used for assessing the uncertainty in Inflation Rate forecasts in Romania.
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THE IDENTIFICATION OF Inflation Rate DETERMINANTS IN THE USA USING THE STOCHASTIC SEARCH VARIABLE SELECTION
2020Co-Authors: Mihaela SimionescuAbstract:Inflation Rate determinants for the USA have been analyzed in this study starting with 2008, when the American economy was already in crisis. This research brings, as a novelty, the use of Bayesian Econometrics methods to identify the monthly Inflation Rate in the USA. The Stochastic Search Variable Selection (SSVS) has been applied for a subjective probability acceptance of 0.3. The results are validated also by economic theory. The monthly Inflation Rate was influenced during 2008-2015 by: the unemployment Rate, the exchange Rate, crude oil prices, the trade weighted U.S. Dollar Index and the M2 Money Stock. The study might be continued by considering other potential determinants of the Inflation Rate.
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Quarterly Inflation Rate target and forecasts in Romania
Economics Management and Sustainability, 2016Co-Authors: Mihaela SimionescuAbstract:In this study, we proposed some Inflation Rate predictions based on econometric models that performed better than the targets of the National Bank of Romania. Few econometric models (multiple regressions model and a vector-autoregression) were used to predict the quarterly Inflation Rate in Romania during 2000:Q1-2016:Q4. The GDP growth has a negative impact on Inflation Rate in Romania, an increase in logarithm of GDP with one percentage point determining a decrease in Inflation logarithm with less than 0.1 units according to both proposed models. However, an increase in Inflation Rate in the previous period determined an increase in this variable in the current period. The inverse of unemployment Rate is positively correlated with the index of prices. The causal relationship between Inflation Rate and unemployment Rate is reciprocal. In the first period the index of prices evolution is explained only by changes in this variable. The Inflation Rate volatility is due mainly to the evolution of this indicator, the influence decreasing insignificantly in time, not descending under 88%. More than 99% of the variation in unemployment Rate is explained by the own volatility for all lags. The annual forecasts based on these models performed better than the targets on the horizon 2015-2016.
Donald A. Otieno - One of the best experts on this subject based on the ideXlab platform.
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The impact of Inflation Rate on stock market returns: evidence from Kenya
Journal of Economics and Finance, 2018Co-Authors: Donald A. Otieno, Rose W. Ngugi, Peter W MuriuAbstract:This study examined the stochastic properties of Inflation Rate, stock market returns and their cointegrating residuals using monthly data for the period 1993 to 2015. The Autoregressive Fractionally IntegRated Moving Average (ARFIMA)-based exact maximum likelihood estimation was employed to determine the integration orders of the individual variables as well as the cointegrating residuals. Results from the ARFIMA model indicate that the month-on-month Inflation Rate, year-on-year Inflation Rate and stock market returns have non-integer orders of integration. This is inconsistent with the stationary/nonstationary results often obtained from the conventional unit root tests and implies that any shocks to the variables are highly persistent but eventually disappear. The results also reveal that the cointegrating residuals have non-integer orders of integration, suggesting that deviations from the long run equilibrium are prolonged, contrary to the assumption held under the conventional cointegration framework. The Fractionally IntegRated Error Correction Model (FIECM) reveals that the year-on-year Inflation Rate positively granger causes stock market returns. This supports Fisher Effect and implies that stock market returns in Kenya provide shelter against Inflationary pressures. This is the first study to empirically examine fractional cointegration and ARFIMA-based Granger Causality between Inflation Rate and stock market returns in Kenya.
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The Global Financial Crisis, Inflation Rate And Stock Market Returns In Kenya
European scientific journal, 2017Co-Authors: Donald A. Otieno, Rose W. Ngugi, Nelson H. W. WawireAbstract:The moderating effect of events such as the 2008 Global Financial Crisis (GFC) on the relation between stock market returns and macroeconomic variables has attracted very little attention. This study investigates the extent to which the 2008 GFC modeRated the relationship between Inflation Rate and stock market returns. The study uses month-onmonth Inflation Rate and year-on-year Inflation Rate from 1st January 1993 to 31st December 2015 and divides the sample data into pre-crisis period (from 1st January 1993 to 31st December 2007); crisis period (from 1st January 2008 to 30th June 2009); and post-crisis period (from 1st July 2009 to 31st December 2015). It uses a product-term regression model instead of the most widely applied additive regression model. Results indicate that a unit increase in the both measures of Inflation Rate had significant depressing effects on stock market returns after the crisis compared to before the crisis. Likewise, the results reveal that average stock market returns were significantly higher after the crisis compared to before the crisis at low rather than medium or high values of the two measures of Inflation Rate. These results suggest that the Kenyan stock market is highly sensitive to variations in Inflation Rate, especially as it emerges from a financial or political turmoil. This study is empirically innovative in the sense that it is the first to examine the moderating effect of the 2008 GFC on the relation between Inflation Rate and stock market returns in Kenya using a product-term model.
Bentouir Naima - One of the best experts on this subject based on the ideXlab platform.
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The Relationship Between Inflation Rate and Nominal Interest Rate in Bolivarian Republic Of Venezuela: Revisiting Fisher’s Hypothesis
Journal of Applied Management and Investments, 2020Co-Authors: Mustafa Kasim, Bentouir NaimaAbstract:Stability of economics over the world represented by understanding the relationship among the interest Rate and Inflation Rate. This paper investigates the relationship between Inflation Rate and nominal interest Rate based on Fisher equation, using a monthly frequency data in case of Venezuela between 1/1/1990 to 31/12/2016. The Dickey Fuller (ADF) test and Phillips-Perron (PP) test both have empirically used to check the unit root. Also, Johansen for Co-integration test is exploited to study the equilibrium relation for long run between the Inflation Rate and the nominal interest Rate in the time series data. The result shows that both variables are non-stationary at Level I (0) in both tests (ADF and PP), after converting the variables to first difference I (1) with taking the log both of interest Rate and Inflation Rate become stationary. The Johansen co-integration null hypothesis is failed to be rejected in both tests Trace-statistics test and Max-Eigen statistics. This means that the long-run equilibrium relation between the Inflation Rate and nominal interest Rate in Venezuela during 1990 to 2016 is not existed, i.e. the Fisher hypothesis does not hold through the sub-period in Venezuela.
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the relationship between Inflation Rate and nominal interest Rate in bolivarian republic of venezuela revisiting fisher s hypothesis
Journal of Applied Management and Investments, 2018Co-Authors: Mustafa Kasim, Bentouir NaimaAbstract:Stability of economics over the world represented by understanding the relationship among the interest Rate and Inflation Rate. This paper investigates the relationship between Inflation Rate and nominal interest Rate based on Fisher equation, using a monthly frequency data in case of Venezuela between 1/1/1990 to 31/12/2016. The Dickey Fuller (ADF) test and Phillips-Perron (PP) test both have empirically used to check the unit root. Also, Johansen for Co-integration test is exploited to study the equilibrium relation for long run between the Inflation Rate and the nominal interest Rate in the time series data. The result shows that both variables are non-stationary at Level I (0) in both tests (ADF and PP), after converting the variables to first difference I (1) with taking the log both of interest Rate and Inflation Rate become stationary. The Johansen co-integration null hypothesis is failed to be rejected in both tests Trace-statistics test and Max-Eigen statistics. This means that the long-run equilibrium relation between the Inflation Rate and nominal interest Rate in Venezuela during 1990 to 2016 is not existed, i.e. the Fisher hypothesis does not hold through the sub-period in Venezuela.
Yuliy Sannikov - One of the best experts on this subject based on the ideXlab platform.
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on the optimal Inflation Rate
The American Economic Review, 2016Co-Authors: Markus K Brunnermeier, Yuliy SannikovAbstract:In our incomplete markets economy financial frictions affect the optimal Inflation target. Households choose portfolios consisting of risky (uninsurable) capital and money. Money is a bubbly store of value. The market outcome is constrained Pareto inefficient due to a pecuniary externality. Each individual agent takes the real interest Rate as given, while in the aggregate it is driven by the economic growth Rate, which in turn depends on individual portfolio decisions. Higher Inflation due to higher money growth lowers the real interest Rate (on money) and tilts the portfolio choice towards physical capital investment. The optimal Inflation target boosts growth and welfare and is higher for emerging market economies.
Peter W Muriu - One of the best experts on this subject based on the ideXlab platform.
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The impact of Inflation Rate on stock market returns: evidence from Kenya
Journal of Economics and Finance, 2018Co-Authors: Donald A. Otieno, Rose W. Ngugi, Peter W MuriuAbstract:This study examined the stochastic properties of Inflation Rate, stock market returns and their cointegrating residuals using monthly data for the period 1993 to 2015. The Autoregressive Fractionally IntegRated Moving Average (ARFIMA)-based exact maximum likelihood estimation was employed to determine the integration orders of the individual variables as well as the cointegrating residuals. Results from the ARFIMA model indicate that the month-on-month Inflation Rate, year-on-year Inflation Rate and stock market returns have non-integer orders of integration. This is inconsistent with the stationary/nonstationary results often obtained from the conventional unit root tests and implies that any shocks to the variables are highly persistent but eventually disappear. The results also reveal that the cointegrating residuals have non-integer orders of integration, suggesting that deviations from the long run equilibrium are prolonged, contrary to the assumption held under the conventional cointegration framework. The Fractionally IntegRated Error Correction Model (FIECM) reveals that the year-on-year Inflation Rate positively granger causes stock market returns. This supports Fisher Effect and implies that stock market returns in Kenya provide shelter against Inflationary pressures. This is the first study to empirically examine fractional cointegration and ARFIMA-based Granger Causality between Inflation Rate and stock market returns in Kenya.
