On the Markov switching welfare cost of inflation

Dai, Wei; Serletis, Apostolos

doi:10.1016/j.jedc.2019.103748

Cited by 18 publications

(5 citation statements)

References 36 publications

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“…Regarding the research of hotspot acquisition, foreign countries have proposed topic discovery methods based on language models. Dai and Serletis [20] proposed a method for calculating word weight based on the log-likelihood test and proposed a method for identifying subtopics by adding named entities, etc., and proposed a method for identifying hot topics with free parameters. e research on forecasting originated from the autoregressive model established in 2011 to predict the law of market changes.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

2021

Complexity

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Public policy and public opinion directly affect the image of the government, but due to the lack of appropriate monitoring and early warning tools, the government’s handling of credit changes is seriously lagging behind. In response to this problem, this paper integrates the internet, public information, market credit information, and other data, uses hidden Markov models and natural language processing technology, and establishes a modern government public policy and public opinion monitoring and early warning model to evaluate government credit in real time; the government can formulate relevant policies based on the evaluation results to improve the government’s governance capabilities. Empirical analysis shows that, based on the dynamic scoring framework and Markov model, the government credit monitoring and early warning models established, respectively, have 90% of the reference value, and the analysis results have the same reference. This method can effectively predict the trend of hot online public opinion. The subsequent establishment of an online public opinion early warning system and an online public opinion guidance mechanism provided theoretical support.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

2021

Complexity

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“…Over the years, Barnett ( 1978 , 1980 ), Belongia ( 1996 ), Hendrickson ( 2014 ), Serletis and Gogas ( 2014 ), Belongia and Ireland ( 2014 , 2015 , 2016 ), Ellington ( 2018 ), Dai and Serletis ( 2019 ), Serletis and Xu ( 2020 , 2021 ), and Xu and Serletis ( 2022 ) have demonstrated the superiority of Divisia monetary aggregates over simple sum aggregates. In fact, these authors revealed that Divisia aggregates are superior to the simple sum aggregates currently used by central banks.…”

Section: Divisia Monetary Aggregatesmentioning

confidence: 99%

Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis

Andreadis¹,

Fragkou

Karakasidis

et al. 2023

Financ Innov

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We construct recurrence plots (RPs) and conduct recurrence quantification analysis (RQA) to investigate the dynamic properties of the new Center for Financial Stability (CFS) Divisia monetary aggregates for the United States. In this study, we use the latest vintage of Divisia aggregates, maintained within CFS. We use monthly data, from January 1967 to December 2020, which is a sample period that includes the extreme economic events of the 2007–2009 global financial crisis. We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin. The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008. Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates, we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times.

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“…Lucas (2000) estimates that, in the US and for the period 1900-1994, the gain from reducing the inflation rate from 10 percent to zero is equivalent to an increase in real income of slightly less than one percent. Dai and Serletis (2019) use the Markov-switching approach to account for instabilities in the long-run money demand function and compute the welfare cost of inflation in the United States; they find that the welfare cost of inflation based on statistically significant estimates of the money demand function declined significantly (by close to 50%) after the 1980s. Penha Penha Cysne and Turchick (2010) analyze the bias in estimating welfare costs of inflation when interest-bearing deposits are disregarded.…”

Section: Literature Reviewmentioning

confidence: 99%

A Markov-Switching Model of Inflation in Bolivia

Bojanic

2021

Economies

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The Bolivian inflation process is analyzed utilizing a time-varying univariate and multivariate Markov-switching model (TMS). With monthly data and, beginning in the late 1930s, inflation is accurately described by a univariate TMS. The intercept for the high-inflation regime is significantly higher than for the low-inflation regime and the actual inflation rate mirrors the smoothing probabilities of the Markov process. Additionally, the predicted duration of each regime closely fits the periods when the country experienced low and inordinate high inflation rates. From a long-run perspective and utilizing a multivariate TMS, the results generally fall in line with what the quantity theory of money predicts. In the high-inflation regime, money growth increases inflation (almost) one-for-one, as classical economics contends. From a short-run perspective and in the high-inflation regime, inflation is almost exclusively explained by a negative output gap. In the low-inflation regime, lagged inflation is the most important determinant of inflation, in line with price stickiness expectations. Partitioning the sources of inflation demonstrate that, from a long-run perspective and in the high inflation regime, differences in inflation are mostly explained by GDP growth; in the low-inflation regime, money growth and velocity growth are the principal factors explaining the variance of inflation. From a short-run perspective, the output gap explains almost all regression variance in the high-inflation regime, and past inflation does the same during times of low inflation, though in both cases the R2 is low which precludes making definite statements about the sources of variability in inflation.

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On the Markov switching welfare cost of inflation

Cited by 18 publications

References 36 publications

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis

A Markov-Switching Model of Inflation in Bolivia

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