Forecasting inflation using the Phillips curve in inflation targeting countries

2018 ◽  
Vol 33 (5) ◽  
pp. 601-623
Author(s):  
Diana Gabrielyan
Keyword(s):  
Inflation Targeting ◽  
Phillips Curve ◽  
Forecasting Inflation

scholarly journals The Phillips Curve: A Case Study Of Theory And Practice

2020 ◽  
pp. 28-38
Author(s):  
K. Lawle ◽  
A. Moscardini ◽  
I. Pavlenko ◽  
T. Vlasova
Keyword(s):  
Systems Thinking ◽  
Inflation Targeting ◽  
Phillips Curve ◽  
Vertical Plane ◽  
Central Banking ◽  
Theory And Practice ◽  
Historical Narrative ◽  
Negative Slope ◽  
Wide Range

This paper develops a detailed case study of the Phillips Curve as it has evolved since Phillips classic work of 1958. An explicit narrative in the paper involves the evolution of the argument using economics and systems thinking, to develop underlying data generating models. These are shown to underpin the inverse relationship between inflation and unemployment in economics. The paper considers the political exigencies relating to the Great inflation of the 1970s and the Great Recession post 2008 in terms of interpretations of the Philips curve. The paper hypothesises that economic ideas have meaningful significance within the context of historical eras with concomitant political imperatives whence such notions become somnolent once crises have abated. This This historical narrative is implicit in the latest research reflections on Philips curves. A particularly useful finding is the relevance of systems thinking and systems dynamics to the interpretation of issues relating to aggregation problems in macroeconomics involving inflation and unemployment causal relationships. The paper concludes that seemingly moribund the Philips curve is alive may have been hibernating. Identifying the Phillips curve requires a wide range of variability of non-aggregative data streams. This allows the negative slope of the curve to be revealed, else the Philips curve slope is pushed towards the vertical plane. Endogenous central banking and inflation targeting intensifies this effect which is evident from a systems thinking /dynamics perspective.

scholarly journals Does the high-frequency data is helpful for forecasting Russian inflation?

2021 ◽  
Vol 37 (2) ◽  
pp. 318-343
Author(s):  
Dmitriy Tretyakov ◽  
◽  
Nikita Fokin ◽  
Keyword(s):  
Monetary Policy ◽  
Central Bank ◽  
High Frequency ◽  
Inflation Targeting ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Policy Regime ◽  
The Future ◽  
Forecasting Inflation

Due to the fact that at the end of 2014 the Central Bank made the transition to a new monetary policy regime for Russia — the inflation targeting regime, the problem of forecasting inflation rates became more relevant than ever. In the new monetary policy regime, it is important for the Bank of Russia to estimate the future inflation rate as quickly as possible in order to take measures to return inflation to the target level. In addition, for effective monetary policy, the households must trust the actions of monetary authorities and they must be aware of the future dynamics of inflation. Thus, to manage inflationary expectations of economic agents, the Central Bank should actively use the information channel, publish accurate forecasts of consumer price growth. The aim of this work is to build a model for nowcasting, as well as short-term forecasting of the rate of Russian inflation using high-frequency data. Using this type of data in models for forecasting is very promising, since this approach allows to use more information about the dynamics of macroeconomic indicators. The paper shows that using MIDAS model with weekly frequency series (RUB/USD exchange rate, the interbank rate MIACR, oil prices) has more accurate forecast of monthly inflation compared to several basic models, which only use low-frequency data.

scholarly journals Forecasting Inflation In China

2013 ◽  
Vol 29 (6) ◽  
pp. 1825 ◽  
Author(s):  
Lillian Kamal
Keyword(s):  
Phillips Curve ◽  
Extended Period ◽  
Chinese Economy ◽  
Output Gap ◽  
Export Competitiveness ◽  
Out Of Sample ◽  
International Investors ◽  
Forecasting Inflation ◽  
Gap Models

China is the worlds second largest economy, has sustained strong growth rates for an extended period of time, and is a prime destination for international investors. It is therefore important for researchers to correctly forecast key macroeconomic variables, such as economic growth and inflation within the Chinese economy. Forecasts of inflation, in particular, are important for domestic production, export competitiveness, business planning and for international investors. A section of the literature has assessed models to explain and forecast inflation within this literature, the role of deviations of output from equilibrium output (Phillips Curve models specified as output gap models) and the role of monetary policy in explaining inflation in China have been of great interest. This paper assesses atheoretic and structural models to explain Chinese inflation and tests the forecasting ability of these models. The findings show that money is not as important in explaining inflation as the output gap. The output gap approach also provides the best forecasts of inflation for the out-of-sample period 2003-2012. Models based on past information alone cannot beat the output gap models.

Is forecasting inflation easier under inflation targeting?

2014 ◽  
Vol 48 (2) ◽  
pp. 609-626 ◽  
Author(s):  
Harun Özkan ◽  
M. Ege Yazgan
Keyword(s):  
Inflation Targeting ◽  
Forecasting Inflation
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