scholarly journals A Comparison of Different Short-Term Macroeconomic Forecasting Models: Evidence from Armenia

2016 ◽  
Vol 5 (2) ◽  
pp. 81-99 ◽  
Author(s):  
Karen Poghosyan
Keyword(s):  
Mean Squared Error ◽  
Small Scale ◽  
Var Models ◽  
Short Term ◽  
Macroeconomic Forecasting ◽  
Bayesian Var ◽  
Out Of Sample ◽  
Competing Models ◽  
Bayesian Factor ◽  
Short Term Forecasting

Abstract We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 – 2014Q4, we estimate parameters of four competing models. Based on the out-of-sample recursive forecast evaluations and using root mean squared error (RMSE) criterion we conclude that small scale Bayesian VAR and Bayesian Factor Augmented VAR models are more suitable for short-term forecasting than traditional unrestricted VAR model.

scholarly journals ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA

2019 ◽  
Vol 4 (1) ◽  
pp. 4-6
Author(s):  
MUSA ABUBAKAR ALKALI
Keyword(s):  
Household Income ◽  
Crude Oil Price ◽  
Short Term ◽  
Macroeconomic Factors ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Short Term Forecasting ◽  
Year 2000 ◽  
Domestic Products

This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from  the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX  forecasting models, with macroeconomic factors as exogenous variables  such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

scholarly journals Forecasting Performances of the Reduced Form VAR and Sims-Zha Bayesian VAR Models when the Multiple Time Series are Jointly Influenced by Collinearity and Autocorrelated Error

2018 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Benjamin Agboola Oyejola
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Var Models ◽  
Short Term ◽  
Series Length ◽  
Bayesian Var ◽  
Autocorrelated Error ◽  
Different Levels

The goal of VAR or BVAR is the characterization of the dynamics and endogenous relationships among time series. Also the VAR models are known for their applications to forecasting and policy analysis. This paper compare the performance of VAR and Sims-Zha Bayesian VAR models when the multiple time series are jointly influenced by different levels of collinearity and autocorrelation in the short term (T=16, 32, 64 and 128). Five levels (-0.9,-0.5, 0,+0.5,+0.9) of collinearity and autocorrelation were considered and the results from the simulation study revealed that VAR(2) model dominated for no and moderate levels of autocorrelation (-0.5, 0, +0.5) irrespective of the collinearity level except in few cases when T=16. While the BVAR models dominated for high autocorrelation levels (-0.9 and +0.9) irrespective of the collinearity level except in few cases when T=128. The performance of the models varies at different levels of the collinearity and autocorrelated error, and also varies with the short term periods. Furthermore, the values of the RMSE and MAE criteria decrease as a result of increase in the time series length. In conclusion, the performance of the forecasting models depend on the time series data structure and the time series length. It is therefore recommended that the data structure and series length should be considered in using an appropriate model for forecasting.

scholarly journals Kalman filter model, as a tool for short-term forecasting of solar potential: case of the Dakar site

2018 ◽  
Vol 57 ◽  
pp. 01004
Author(s):  
A. Mbaye ◽  
J. Ndong ◽  
M.L. NDiaye ◽  
M. Sylla ◽  
M.C. Aidara ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Mean Squared Error ◽  
Model Performance ◽  
Global Solar Radiation ◽  
Plant Production ◽  
Bias Error ◽  
Short Term ◽  
Filter Model ◽  
Short Term Forecasting

The prediction of solar potential is an important step toward the evaluation of PV plant production for the best energy planning. In this study, the discrete Kalman filter model was implemented for short-term solar resource forecasting one the Dakar site in Senegal. The model input parameters are constituted at a time t of the air temperature, the relative humidity and the global solar radiation. The expected output at time t+T is the global solar radiation. The model performance is evaluated with the square root of the normalized mean squared error (NRMSE), the absolute mean of the normalized error (NMAE), the average bias error (NMBE). The model Validation is carried out by means of the data measured within the Polytechnic Higher School of Dakar for one year. The simulation results following the 20 minute horizon show a good correlation between the prediction and the measurement with an NRMSE of 4.8%, an NMAE of 0.27% and an NMBE of 0.04%. This model could contribute to help photovoltaic based energy providers to better plan the production of solar photovoltaic plants in Sahelian environments.

The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting

2018 ◽  
Vol 22 (4) ◽  
Author(s):  
Andrea Giusto ◽  
Talan B. İşcan
Keyword(s):  
Prediction Error ◽  
Asymptotic Properties ◽  
Var Model ◽  
Var Models ◽  
Macroeconomic Forecasting ◽  
Bayesian Var ◽  
Mixed Frequency

Abstract This paper introduces the rescaled representation of VAR models (R-VARs) and demonstrates its application in forecasting mixed-frequency macroeconomic data. We develop the model, illustrate how to implement it, and derive the asymptotic properties of the estimates. We show that R-VARs provide reliable estimates of the prediction error bands while maintaining the precision of the point forecasts. We illustrate these features by comparing it to a mixed-frequency Bayesian VAR model, the leading alternative in the existing literature.

scholarly journals Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

2020 ◽  
Vol 11 (1) ◽  
pp. 73-88
Author(s):  
Paweł Baranowski ◽  
Karol Korczak ◽  
Jarosław Zając
Keyword(s):  
Regression Models ◽  
Linear Regression Models ◽  
Short Term ◽  
Research Attention ◽  
Forecasting Models ◽  
Box Office ◽  
Out Of Sample ◽  
Short Term Forecasting ◽  
The Individual ◽  
Weekly Frequency

AbstractBackground: Cinema programmes are set in advance (usually with a weekly frequency), which motivates us to investigate the short-term forecasting of attendance. In the literature on the cinema industry, the issue of attendance forecasting has gained less research attention compared to modelling the aggregate performance of movies. Furthermore, unlike most existing studies, we use data on attendance at the individual show level (179,103 shows) rather than aggregate box office sales.Objectives: In the paper, we evaluate short-term forecasting models of cinema attendance. The main purpose of the study is to find the factors that are useful in forecasting cinema attendance at the individual show level (i.e., the number of tickets sold for a particular movie, time and cinema).Methods/Approach: We apply several linear regression models, estimated for each recursive sample, to produce one-week ahead forecasts of the attendance. We then rank the models based on the out-of-sample fit.Results: The results show that the best performing models are those that include cinema- and region-specific variables, in addition to movie parameters (e.g., genre, age classification) or title popularity.Conclusions: Regression models using a wide set of variables (cinema- and region-specific variables, movie features, title popularity) may be successfully applied for predicting individual cinema shows attendance in Poland.

scholarly journals Forecasting models for the Chinese macroeconomy: the simpler the better?

2019 ◽  
Vol 58 (1) ◽  
pp. 139-167
Author(s):  
Chris Heaton ◽  
Natalia Ponomareva ◽  
Qin Zhang
Keyword(s):  
Price Index ◽  
Candidate Model ◽  
Var Models ◽  
Real Activity ◽  
Price Inflation ◽  
Macroeconomic Forecasting ◽  
Producer Price ◽  
Bayesian Var ◽  
Forecasting Models ◽  
Forecasting Error

Abstract We consider the problem of macroeconomic forecasting for China. Our objective is to determine whether well-established forecasting models that are commonly used to compute forecasts for Western macroeconomies are also useful for China. Our study includes 19 different forecasting models, ranging from simple approaches such as the naive forecast to more sophisticated techniques such as ARMA, Bayesian VAR, and factor models. We use these models to forecast two different measures of price inflation and two different measures of real activity, with forecast horizons ranging from 1 to 12 months, over a period that stretches from March 2005 to December 2018. We test null hypotheses of equal mean squared forecasting error between each candidate model and a simple benchmark. We find evidence that AR, ARMA, VAR, and Bayesian VAR models provide superior 1-month-ahead forecasts of the producer price index when compared to simple benchmarks, but find no evidence of superiority over simple benchmarks at longer horizons, or for any of our other variables.

scholarly journals ARMA model for short-term forecasting of solar potential: application to a horizontal surface of Dakar site

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
A. Mbaye ◽  
M.L. Ndiaye ◽  
D.M. Ndione ◽  
M. Sylla ◽  
M.C. Aidara ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Horizontal Surface ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
P Value ◽  
Short Term ◽  
Arma Process ◽  
Short Term Forecasting ◽  
Sahelian Zone

This paper presents a model for short-term forecasting of solar potential on a horizontal surface. This study is carried out in to the context of valuing of energy production from photovoltaic solar sources in the Sahelian zone. In this study, Autoregressive Moving Average (ARMA) process is applied to predict global solar potential upon 24 hours ahead. The ARMA (p, q) is based on finding optimum parameters p and q to better fit considered variable (sunshine). Data used for the model calibrating are measured at the station of Ecole Supérieure Polytechnique of Dakar. Records are hourly and range from October 2016 to September 2017. The choice of this model is justified by its robustness and its applicability on several scales through the world. Simulation is done using the RStudio software. The Akaike information criterion shows that ARMA (29, 0) gives the best representation of the data. We then applied a white noise test to validate the process. It confirms that the noise is of white type with zero mean, variance of 1.252 and P-value of about 26% for a significant level of 5%.Verification of the model is doneby analyzing some statistical performance criteria such the RMSE =0.629 (root mean squared error), the R² = 0.963 (Coefficient of determination), the MAE=0.528 (Mean Absolut Error) and the MBE=0.012 (Mean BiasError). Statistics criteria show that the ARMA (29,0) is reliable; then, can help to improve planning of photovoltaic solar power plants production in the Sahelian zone.

scholarly journals Modeling the possibilities of economic adaptation of trade enterprises and hospitality industry in the context of epidemiological zoning

2020 ◽  
Vol 18 (4) ◽  
pp. 191-202
Author(s):  
Iryna Melnyk ◽  
Yuriy Turyanskyy ◽  
Ihor Mishchuk ◽  
Nataliіa Mitsenko ◽  
Roksolana Godunko
Keyword(s):  
Negative Impact ◽  
Forecast Model ◽  
Economic Losses ◽  
Clear Correlation ◽  
Regression Equations ◽  
Software Modeling ◽  
Short Term ◽  
Macroeconomic Forecasting ◽  
Effective Activity ◽  
Short Term Forecasting

The article identifies the negative impact of the coronavirus crisis on the expected efficiency of retail, hotel, restaurant and tourism businesses. The aim of the paper is to develop a methodological algorithm for short-term forecasting of opportunities to restore the effective activity of enterprises under quarantine restrictions.Seasonal component adjustments were performed in the Demetra+ software. Modeling the recovery of effective activity included an assessment of the influence of macroparameters on the dynamics of an enterprise’s sales volumes under pre-quarantine conditions, defining the size of economic losses, determining coefficients of macroindicators’ dynamic influence under conditions of differentiation of quarantine restrictions, constructing a matrix of multiple regression equations, which clearly demonstrates the forecast prospects for restoring the effective activity of enterprises, depending on the quarantine zoning. A situational model of the possible scenario dynamics of enterprises’ trade turnover was built taking into account the quarantine zoning and the logical transformational algorithms of influence on variable system parameters caused by it. The thermometer principle was used as a fuzzy logic tool to consider the specifics of the dynamics of various linguistic variables and bring the forecast model as close as possible to the epidemiological zoning logic. Approbation of the methodology revealed a clear correlation between the severity of quarantine restrictions and the expected growth of enterprise activity amounts. In a more advanced form, the method should be used for short-term macroeconomic forecasting when determining quarantine restrictions and epidemiological zoning.

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