scholarly journals Prediction of Consumption and Income in National Accounts: Simulation-Based Forecast Model Selection

2021 ◽  
Vol 5 (1) ◽  
pp. 55
Author(s):  
Adusei Jumah ◽  
Robert M. Kunst
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Sample Size ◽  
Error Correction ◽  
Forecast Model ◽  
Household Consumption ◽  
Prediction Tool ◽  
National Accounts ◽  
Simulation Based ◽  
Forecast Models

Simulation-based forecast model selection considers two candidate forecast model classes, simulates from both models fitted to data, applies both forecast models to simulated structures, and evaluates the relative benefit of each candidate prediction tool. This approach, for example, determines a sample size beyond which a candidate predicts best. In an application, aggregate household consumption and disposable income provide an example for error correction. With panel data for European countries, we explore whether and to what degree the cointegration properties benefit forecasting. It evolves that statistical evidence on cointegration is not equivalent to better forecasting properties by the implied cointegrating structure.

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

scholarly journals DAMPAK BPNT TERHADAP KONSUMSI RUMAH TANGGA DAN POLA KONSUMSI RUMAH TANGGA PENERIMA BPNT, KECAMATAN MENGWI

2021 ◽  
pp. 839
Author(s):  
Luh Ade Yumita Handriani ◽  
Sudarsana Arka
Keyword(s):  
Path Analysis ◽  
Sample Size ◽  
Direct Effect ◽  
Indirect Effect ◽  
Household Consumption ◽  
Consumption Patterns ◽  
Consumption Pattern ◽  
Large Sample Size ◽  
Analysis Techniques ◽  
The Impact

This study aims to analyze the impact of the BPNT program on household consumption and consumption patterns of BPNT recipient households in Mengwi District, Badung Regency. This research was conducted in Mengwi District, Badung Regency using a questionnaire distributed to respondents with a large sample size of 96 KPM. This study uses path analysis techniques to analyze the direct effect and Sobel test to analyze the indirect effect. Based on path analysis, the results of the study concluded that the BPNT variable had a positive and significant effect on the consumption of BPNT recipient households in Mengwi District, Badung Regency. The BPNT variable has no effect on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency. The household consumption variable has a negative and significant effect on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency. The household consumption variable did mediate the effect of the BPNT Program on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency

scholarly journals Sensitivities of the Madden–Julian oscillation forecasts to configurations of physics in the ECMWF global model

2021 ◽  
Vol 21 (6) ◽  
pp. 4759-4778
Author(s):  
Jun-Ichi Yano ◽  
Nils P. Wedi
Keyword(s):  
Forecast Model ◽  
Global Model ◽  
Madden Julian Oscillation ◽  
Forecast Performance ◽  
Theoretical Investigations ◽  
Forecast Models ◽  
Forecasting System ◽  
The Rich ◽  
The Tropics ◽  
Original Motivation

Abstract. The sensitivities of the Madden–Julian oscillation (MJO) forecasts to various different configurations of the parameterized physics are examined with the global model of ECMWF's Integrated Forecasting System (IFS). The motivation for the study was to simulate the MJO as a nonlinear free wave under active interactions with higher-latitude Rossby waves. To emulate free dynamics in the IFS, various momentum-dissipation terms (“friction”) as well as diabatic heating were selectively turned off over the tropics for the range of the latitudes from 20∘ S to 20∘ N. The reduction of friction sometimes improves the MJO forecasts, although without any systematic tendency. Contrary to the original motivation, emulating free dynamics with an operational forecast model turned out to be rather difficult, because forecast performance sensitively depends on the specific type of friction turned off. The result suggests the need for theoretical investigations that much more closely follow the actual formulations of model physics: a naive approach with a dichotomy of with or without friction simply fails to elucidate the rich behaviour of complex operational models. The paper further exposes the importance of physical processes other than convection for simulating the MJO in global forecast models.

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