投资额与GDP和财政收入的回归分析及预测

文章首先运用MATLAB工具对1991-2008年我国GDP、财政收入与投资额等数据进行回归分析,得出固定资产投资与GDP及财政收入的回归模型。其次,分别建立年份对GDP与财政收入的拟合曲线,预测出2009——2010年的GDP与财政收入值,进而推出未来两年的投资额预测值。     

Purchasing Power Parity under Regime Shifts: An Application to Asian Countries*

This paper re‐examines the long‐run purchasing power parity (PPP) relationship for nine Asian countries relative to the USA and Japan during a period containing significant structural breaks. The relevance of considering structural breaks in PPP tests is demonstrated by utilizing the Johansen et al. (2000) procedure that allows for up to two pre‐determined structural breaks. Using conventional tests without considering breaks, one is able to reject the null of no cointegration for only four countries. The Johansen et al. procedure clearly demonstrates the importance of allowing for structural breaks and provides strong support for long‐run PPP for all the countries, regardless of the base country, except in the case of the Philippines vis‐à‐vis Japan. The Hansen–Johansen parameter constancy test indicates stability for all the countries except the Philippines relative to the USA and Malaysia relative to Japan.     

Unit root tests with double trend breaks and the 1990s recession in Japan

This paper applies to Japanese macroeconomic series unit root tests that allow for the possibility of up to two endogenous break points. The presence of a single structural break around the first oil price shock in 1973 turns out to be very sensitive to the amplitude of the data sample and, in particular, it disappears when one extends the sample to the observations of the 1990s. This may indicate the presence of a second structural break, the existence of which is tested with a unit root test with a two-break alternative hypothesis for which we compute finite sample critical values. Interestingly enough, the hypothesis of the absence of a second break occurring in the 1990s can be rejected. Such results seem to indicate that the deep recession of the 1990s in Japan may not be the reflection of a negative output-gap, but that of a fall in the growth trend of output as a consequence of a huge productivity shock.     

Are Turkey's tourism markets converging? Evidence from the two-step LM and three-step RALS-LM unit root tests

In this study, we aim to analyse whether Turkey's 14 major tourist source markets are converging by using monthly data over the period January 1996 to December 2012. To this aim, we use the recently developed two-step Lagrange multiplier (LM) and three-step residual augmented least squares-Lagrange multiplier (RALS-LM) unit root tests that allow for two structural breaks in data. The results indicate that 10 out of 14 markets are converging, meaning that tourism policies and strategies directed at these markets are successful. Furthermore, the break points correspond to the important political, social, natural and economic events such as crisis, earthquake, disease and terrorist attack.     

Efficient estimation of forecast uncertainty based on recent forecast errors

Multi-step-ahead forecasts of the forecast uncertainty of an individual forecaster are often based on the horizon-specific sample means of his recent squared forecast errors, where the number of past forecast errors available decreases one-to-one with the forecast horizon. In this paper, the efficiency gains from the joint estimation of forecast uncertainty for all horizons in such samples are investigated. If the forecast uncertainty is estimated by seemingly unrelated regressions, it turns out that the covariance matrix of the squared forecast errors does not have to be estimated, but simply needs to have a certain structure, which is a very useful property in small samples. Considering optimal and non-optimal forecasts, it is found that the efficiency gains can be substantial for longer horizons in small samples. The superior performance of the seemingly-unrelated-regressions approach is confirmed in several empirical applications.     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.     

Combining forecasts: some results on exchange and interest rates

The aim of this work is to investigate whether the combination of forecasts plays an important role in the improvement of forecast accuracy Particular attention is paid to: (a) the methods of forecasting (the methods compared are neural networks, fuzzy logic, GARCH models, switching regime and chaotic dynamics); (b) combining the forecasts provided by the different methods. This work has also the aim of revising a short-term econometric forecast using a longer-term forecast. The revision process usually runs the opposite way (revision is made on a longer-term forecast using a short-term one to reflect the current available information, but it is not excluded that it is possible to proceed as described above. Daily data from the financial market is used. Some empirical applications on exchange and interest rates are given.     

Excel与SPSS在线性回归预测中的比较分析

线性回归分析是经济管理与预测的一个重要的量化分析方法。分别运用Excel与SPSS对同一实际问题进行线性回归分析,得出两种软件在线性回归预测问题上各有其优势,在经济管理的实际应用中无论选择那种软件都要了解其功能和操作的特点,有助于对实际问题恰当而且正确地分析与预测。     

Comparing the accuracy of several network-based COVID-19 prediction algorithms

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.     

Diagnostics cannot have much power against general alternatives

Model diagnostics are shown to have little power unless alternative hypotheses can be narrowly defined. For example, the independence of observations cannot be tested against general forms of dependence. Thus, the basic assumptions in regression models cannot be inferred from the data. Equally, the proportionality assumption in proportional-hazards models is not testable. Specification error is a primary source of uncertainty in forecasting, and this uncertainty will be difficult to resolve without external calibration. Model-based causal inference is even more problematic.