Forecasting Inflation Rate Research Articles

Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms

PurposeThe main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.Design/methodology/approachThis paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.FindingsResults suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.Originality/valueTurkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.

Does the high-frequency data is helpful for forecasting Russian 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.

ФОРМИРОВАНИЕ ПРОГНОЗА ПО УРОВНЮ ИНФЛЯЦИИ В ЮФО С УЧЕТОМ ДИНАМИКИ РАЗВИТИЯ РЕГИОНАЛЬНОЙ ЭКОНОМИКИ

Inflation is a complex multilateral process, which, in general, has a negative impact on the economy, reduces the level of economic activity of the population and leads to a decrease in the level of real income. The article studies the main constituent elements of the inflation category, reveals the parameters of the impact on the economy of the inflation process, studies the dynamics of factors affecting the forecast inflation rate in the Southern Federal District, develops a regional multifactor inflation model, and based on the approximated data, a forecast of the annual inflation rate is constructed in the Southern Federal District at the end of 2020. The most significant factors affecting the inflation rate in the Russian Federation were identified from the position of the direction of their influence: acceleration or deceleration of inflation, as well as from the point of view of their degree of influence on the inflation rate. At the same time, the influence of the coronavirus pandemic and fluctuations in the oil market on the economic parameters in the Russian Federation in 2020, on the price level of individual food and non-food products was examined. The analyzed indicator of the inflation rate is defined as an indicator of the state of the economic situation in the country, it is revealed that this variable will be dependent. Using the methods of correlation and regression analysis, a mathematical expression is found in the form of a regression model and its adequacy and statistical significance are evaluated. The coefficient of pair correlation, which characterizes the degree of statistical dependence between two variables, without taking into account the influence of other variables, was adopted as the main indicator characterizing the relationship between the analyzed variables. As a result of the calculations, a model of multiple linear regression of the inflation rate was built, the average monthly nominal accrued wages of the employees of the organizations were approximated, and approximation equations were obtained, which made it possible to build an inflation rate forecast for the Southern Federal District for 2019-2020.

Phillips Curve for the Asian Economies: A Nonlinear Perspective

ABSTRACT This paper examines the impact of exchange-rate movements on inflation in eight Asian countries. Results from an open-economy Phillips curve are; first, the Markov-switching open-economy model confirm that the two-state Phillips curve outperforms alternative models to study inflation dynamics. There is considerable heterogeneity in the pass-through estimates for Asian countries, with Singapore exhibiting the lowest exchange rate pass-through (ERPT). Regime-dependent pass-through estimates are sensitive to average inflation and it should be factored in when forecasting inflation rates. Second, the extent of pass-through is considerably lower and far from complete in a low-inflation regime, endorsing Taylor hypothesis. Third, in the majority of the countries, we find support for global disinflation in domestic inflation that has strengthened over time. The main takeaway is that globalization matters for Asian inflation dynamics.

Forecasting inflation using univariate continuous‐time stochastic models

AbstractIn this paper we investigate the applicability of several continuous‐time stochastic models to forecasting inflation rates with horizons out to 20 years. While the models are well known, new methods of parameter estimation and forecasts are supplied, leading to rigorous testing of out‐of‐sample inflation forecasting at short and long time horizons. Using US consumer price index data we find that over longer forecasting horizons—that is, those beyond 5 years—the log‐normal index model having Ornstein–Uhlenbeck drift rate provides the best forecasts.

Forecasting with High‐Dimensional Panel VARs

AbstractThis paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time‐varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation‐free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.

The Information Content of Commodity Futures Markets

textabstractWe find that commodity futures returns contain information relevant to stock market returns and macroeconomic fundamentals for a large number of countries. Commodity futures returns predict stock market returns in 59 out of 70 countries and macroeconomic fundamentals in 62 countries. This predictability is not concentrated in the Energy and Industrial Metals sectors, as it is economically and statistically significant across all sectors. Surprisingly, we find that the role of countries’ dependence on commodity trade is limited in its ability to account for this predictability. This holds true even when considering new measures that take into account indirect exposures through financial and trade linkages between countries. We find much stronger evidence of predictability being related to the ability of commodities to forecast inflation rates. Overall, our evidence is consistent with commodity markets having a truly global information discovery role in relation to financial markets and the real economy.

Forecasting Panel Data with Structural Breaks and Regime-Specific Grouped Heterogeneity

Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Existing methods for forecasting time series under breaks reduce parameter estimation error by pooling estimates across pre- and post-break data necessarily inducing bias. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. We develop a new Bayesian methodology to estimate panel regression models in the presence of breaks and regime-specific grouped heterogeneity. Parameters are pooled within, but differ across, regime-specific groups. We develop a formal test of regime-specific patterns of grouped heterogeneity that integrates over all parameters and penalises model complexity. In an empirical application to forecasting inflation rates across 20 U.S. industries we find evidence of regime-specific grouped heterogeneity. Exploiting this information produces significantly improved forecasts relative to a range of popular methods.

A predictive Autoregressive Integrated Moving Average (ARIMA) Model for forecasting inflation rates

This paper considers the modelling and forecasting of monthly All-items (12 months average change) inflation rates in Nigeria using the Box-Jenkins (ARIMA) model. Time series data used in the study was collected from the Central Bank of Nigeria statistical web database. The data was differenced twice to achieve stationarity in the series as required. Based on the evaluation and diagnostic criteria, the most accurate model is selected. The order of the best ARIMA model was found to be ARIMA (1, 2, 1). The diagnostic analysis of the model residuals showed that they are normally distributed uncorrelated random shocks. The findings in this study showed that the selected ARIMA model captured the dynamics in the series and produced forecasted values which had minimal forecast errors when compared with the actual inflation values in the validation period.

Multi-Step Inflation Prediction with Functional Coefficient Autoregressive Model

Forecasting inflation rate is one of the most important topics in finance and economics. In recent years, China has stepped into a “New Normal” stage of economic development, with a different state from the fast growth period during the past few decades. Hence, forecasting the inflation rate of China with a time-varying model may give high accuracy. In this paper, we investigate the problem of forecasting the inflation rate with a functional coefficient autoregressive (FAR) model, which allows the coefficient to change over time. We compare the FAR model based on the B-splines estimation method with the autoregressive moving average (ARMA) model by extensive simulation studies. In addition, with the monthly CPI data of China, we conduct both in-sample analysis and out-of-sample forecasting. The forecasting result shows that the FAR model based on the B-splines estimation method has a better performance than the ARMA model.

Fourier Transformation on Model Fitting for Pakistan Inflation Rate

Inflation is one of the serious economic indicators in Pakistan. Inflation can be crawling, walking, running, hyper and stagflation according to nature. To model monthly inflation rate in Pakistan periodogram analysis and frequency domain analysis which is also known as Fourier analysis or spectral analysis is used. After analyzing the data, inflation cycle length is observed and appropriate Fourier series models are fitted to the data. Monthly inflation rate is also analyzed by Auto Regressive Integrated Moving average (ARIMA). Further, models are compared and it is found that Fourier series models are more suitable to forecast inflation rate of Pakistan.

Forecasting Inflation Rate: Professional Against Academic, Which One is More Accurate

This paper evaluates the professional forecasts, those made by financial and non-financial forecasters and the aggregate between them, by comparing their results to academic forecasts. The US quarterly inflation rate and the professional forecasts are considered for the period of 1981 third quarter to 2012 final quarter. This paper examines whether academic forecasts outperforms the professional forecasts. For short term inflation forecasting the professional forecasters (non-financial, financial and the aggregate) proved to be the most accurate, however for long term inflation forecasting academic forecasts showed to be most accurate. The results also indicate that the long term aggregate forecasts related to information from the aggregate short term forecasts and current inflation rate. Furthermore, financial forecasters use the short term non-financial forecasts in their expectations and the non-financial forecasters use the short term financial forecasts in their long term expectations. In addition, the results confirm causality between the short and long term forecasts of the non-financial forecasters. For the financial inflation forecasts, there is no causality between the short and long term financial forecasts.

AKURANSI ARIMA DALAM PERAMALAN INFLASI KOTA BANDUNG

Inflation is as one of economic development indicator has an important role to public’s economic prosperity in each country. Inflation rate controlling should be running in order to economic stability .This research aims to forecast inflation of Bandung City using ARIMA Method is suitable for time series data such as inflation rate. Researcher taken data as much as 10 years of inflation rate of Bandung City from January 2006 to December 2015 to forecast inflation rate on next 12 months. The tool using Minitab version 15.0.Result of research shows that sum of data did not influence on accuracy of forecast. It seen on Mean Absolut Percentage Error (MAPE) number tend to not patterned. It is showed on the smallest MAPE value (0,0082) in Forecasting number IV (using 84 data). Moreover, ARIMA shows that longer forecast period is linear to more deviated forecast.

How do zero-coupon inflation swaps predict inflation rates in the euro area? Evidence of efficiency and accuracy on 1-year contracts

This paper examines the risk-neutral efficient market hypothesis for inflation swap markets in the euro area from 2005.10 to 2014.07. Overall, we conclude that 1-year zero-coupon inflation swap rates are unbiased predictors of inflation rates. Further, there is no empirical evidence of an inflation risk premium and the assumption of rationality seems to hold. Definitely, these inferences encourage the reading of inflation expectations embedded in short-term inflation swaps. Additionally, we compare the predictive ability of inflation swaps with other measures of inflation expectations. The in-sample results show that, in contrast with surveys, market-based measures are able to accurately forecast inflation rates. In turn, based on an out-of-sample analysis, a straightforward econometric model dominates other sources. Therefore, a combined analysis that uses different sources contributes to a more robust view of future inflation rates.

Forecasting Inflation Rate of India using Neural Networks

Forecasting Inflation Rate of India using Neural Networks

Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing

In this paper, two models of forecasting are used the Box-Jenkins procedure employing the SARIMA and the Holt-Winters triple exponential smoothing. Published Consumer Price Index Data from Kenya National Bureau of Statistics (KNBS) for the period November 2011 to October 2016 was used. This paper we equate the forecasted values of both the models and we choose the best model based on the least mean Absolute square error (MASE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The three step model building for Box-Jenkins was first employed, followed by the Hold-Winters triple exponential smoothing. The study found the SARIMA Model was a better model than the Holt-winters triple exponential smoothing as per the obtained results using MASE, MAE and MAPE.

Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates

Typically, central banks use a variety of individual models (or a combination of models) when forecasting inflation rates. Most of these require excessive amounts of data, time, and computational power, all of which are scarce when monetary authorities meet to decide over policy interventions. In this paper we use a rolling Bayesian combination technique that considers inflation estimates by the staff of the Central Bank of Colombia during 2002–2011 as prior information. Our results show that: (1) the accuracy of individual models is improved by using a Bayesian shrinkage methodology, and (2) priors consisting of staff estimates outperform all other priors that comprise equal or zero vector weights. Consequently, our model provides readily available forecasts that exceed all individual models in terms of forecasting accuracy at every evaluated horizon.

Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

Modeling inflation rates and exchange rates in Ghana: application of multivariate GARCH models

This paper was aimed at investigating the volatility and conditional relationship among inflation rates, exchange rates and interest rates as well as to construct a model using multivariate GARCH DCC and BEKK models using Ghana data from January 1990 to December 2013. The study revealed that the cumulative depreciation of the cedi to the US dollar from 1990 to 2013 is 7,010.2% and the yearly weighted depreciation of the cedi to the US dollar for the period is 20.4%. There was evidence that, the fact that inflation rate was stable, does not mean that exchange rates and interest rates are expected to be stable. Rather, when the cedi performs well on the forex, inflation rates and interest rates react positively and become stable in the long run. The BEKK model is robust to modelling and forecasting volatility of inflation rates, exchange rates and interest rates. The DCC model is robust to model the conditional and unconditional correlation among inflation rates, exchange rates and interest rates. The BEKK model, which forecasted high exchange rate volatility for the year 2014, is very robust for modelling the exchange rates in Ghana. The mean equation of the DCC model is also robust to forecast inflation rates in Ghana.

Forecasting Inflation Rate in Kenya Using SARIMA Model

It is the desire of the policy makers in a country is to have access to reliable forecast of inflation rate. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast Kenya's inflation rate using quarterly data for the period 1981 to 2013 obtained from KNBS. SARIMA (0,1,0) (0,0,1)4 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. Diagnostic checks using Jarque-Bera Normality Test indicated that they were normally distributed. ACF and PACF plots for the residuals and squared residuals revealed that they followed a white noise process and were homoskedastic respectively. The predictive ability tests RMSE=0.2871, MAPE=3.9456 and MAE= 0.2369 showed that the model was appropriate for forecasting the inflation rate in Kenya.