Combination forecasts of tourism demand with machine learning models

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: support vector regression, Gaussian process regression and neural network models. We use an autoregressive moving average model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.     

季节平稳过程间的虚假回归

本文推导了当数据生成过程是独立的季节平稳过程情形下,OLS参数估计及检验统计量的极限分布。发现序列中的自相关性会导致虚假回归现象的发生。     

Cycles in basic innovations

Basic innovations are believed to be one of the drivers of economic growth. In this paper we examine if cycle periods found for economic data correspond with cycles in basic innovations. For an annual time series of count data on innovations covering 1764-1976, we fit a harmonic Poisson regression model. We find the presence of multiple cycles with periods 5, 13, 24, 34 and 61, and these show a remarkable resemblance with commonly found economic cycles.     

An alternative approach for testing for linear association for two independent stationary AR(1) processes

Spurious correlations occur when two independent time series are found to be correlated according to the typical statistical procedure for testing the null hypothesis of zero correlation in the population. Using a Monte Carlo analysis, this study examines the spurious correlation phenomenon for two independent stationary AR(1) processes and it finds that if an alternative testing procedure is applied, spurious behaviour is eliminated using the variance of the sample correlation coefficient of these two series, suggested by Bartlett (1935).     

我国经济增长与环境质量关系的再检验

建立了经济增长与环境质量的关系理论模型,利用1985—2008年全国时间序列数据,通过相关分析、协整分析分别建立了工业三废与经济增长之间的回归模型,对经济增长与环境质量之间的关系进行了实证分析。结果表明:我国环境质量与经济增长之间并不存在EKC"倒U"关系;经济增长、产业结构、技术要素和污染治理都是影响环境质量的重要因素。     

A novel time-series model based on empirical mode decomposition for forecasting TAIEX

Stock price prediction is regarded as a challenging task of the financial time series prediction process. Time series models have successfully solved prediction problems in many domains, including the stock market. Unfortunately, there are two major drawbacks in stock market by time-series model: (1) some models cannot be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance. For solving the above problems and promoting the forecasting performance of time-series models, this paper proposes a hybrid time-series support vector regression (SVR) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model and SVR model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE). And the more fluctuation year (2000–2001) occurs, the better accuracy of proposed model will be obtained.     

The determinants of FDI in Turkey: A Markov Regime-Switching approach

This paper considers movements of Foreign Direct Investments (FDI) in Turkey, and therefore, to understand the dynamics of FDI, runs several nonlinear FDI equations in which the basic determinants of FDI in Turkey are determined through Markov Regime-Switching Models (MSMs). The statistical properties of Markov Regime-Switching time series models are more desirable than those of conventional time series or panel regression models. Through these properties of MSMs, i) one can observe structural changes, if they exist, in FDI equations through time, ii) if, in fact, the true FDI regression equation follows a nonlinear relationship, MSMs fit data better than the linear models. This paper eventually follows maximum likelihood methodology of Markov Regime-Switching Model (MSM) to search for the possible structural changes in level and/or trends and possible changes in parameters of independent variables of FDI–MSM equations through the transition probabilities. In conclusion, this paper yields the outcome that Turkish FDI growth equation has significant structural changes in level and trend and that has significant coefficient shifts in explanatory variables. These explanatory variables are Turkish GDP Growth, Labor Cost, the Electricity Price Growth, the growth in average prices of High Sulphur Fuel Oil, Cooking Coal, Steam Coal and Natural Gas, Export Growth, Import Growth, Discount Rate and Country Risk Indexes for Turkey, US and EU, respectively, within the time interval from 1988 first quarter to 2010 second quarter.     

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non–linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)–in–mean models are employed. The conditional errors are assumed to follow the normal and Student– t distributions. The non–linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non–linearity. Under the Student density, the extent of non–linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH–in–mean regression generated the worse out–of–sample forecasts.     

Bootstrap methods for autocorrelation test with uncorrelated but not independent errors

By using bootstrap technique we investigate the properties of the Breusch [Breusch, T.S., 1978. Testing for autocorrelation in dynamic linear models. Australian Economic Papers 17, 334–355]–Godfrey [Godfrey, L.G., 1978. Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables. Econometrica 46, 1303–1310] autocorrelation tests in dynamic models with uncorrelated but not independent errors. In this paper we show that, under conditions when the errors are uncorrelated but not independent, even the best likelihood ratio test cannot achieve the asymptotic distribution under the null hypothesis of no autocorrelation. Standard bootstrap methods also fail to produce consistent results. To overcome this problem we applied several bootstrap testing methods for the same purpose and found the stationary bootstrap and Wild bootstrap with static model to perform adequately among the other bootstrap methods.     

A quantile regression approach for estimating panel data models using instrumental variables

We introduce a quantile regression approach to panel data models with endogenous variables and individual effects correlated with the independent variables. We find newly developed quantile regression methods can be easily adapted to estimate this class of models efficiently.     

Testing for unit roots: a simple alternative to Dickey—Fuller

This ariticle proposes a very simple test for a unit root in a time series which is based on the residual autocorrelation function from a regression of the observed series on a linear trend. The test statistic is proportional to the maximum lag length for which the residual autocorrelations at all lower lag lengths are stickly positive. Critical values for the test are calculated using Monte Carlo simultation assuming a random walk DGP. Its robustness properties to different I(1) DGPs are examined along with its power characteristics. These are found to compare very favourably to those of Dickey–Fuller-type tests.     

Fiscal dependence on trade taxes and the level of economic development:: a comment

We test for fractional dynamics in US monetary series, their various formulations and components, and velocity series. Using the spectral regression method, we find evidence of a fractional exponent in the differencing process of the monetary series (both simple-sum and Divisia indices), in their components (with the exception of demand deposits, savings deposits, overnight repurchase agreements, and term repurchase agreements), and the monetary base and money multipliers. No evidence of fractional behaviour is found in the velocity series. Granger's (Journal of Econometrics, 25, 1980) aggregation hypothesis is evaluated and implications of the presence of fractional monetary dynamics are drawn.     

Errors in variables in panel data with a binary dependent variable

In a conditional logistic regression model for panel data when independent variables are subject to measurement error, the usual estimator, obtained by a regression on the observed independent variables, is asymptotically biased. We introduce a bias-adjusted estimator, which is examined asymptotically under conditions that are appropriate when the measurement error is small but non-negligible.     

Volatility forecasting of exchange rate by quantile regression

Exchange rates are known to have irregular return patterns; not only their return volatilities but the distribution functions themselves vary with time. Quantile regression allows one to predict the volatility of time series without assuming an explicit form for the underlying distribution. This study presents an approach to exchange rate volatility forecasting by quantile regression utilizing a uniformly spaced series of estimated quantiles. Based on empirical evidence of nine exchange rate series, using 19 years of daily data, the adopted approach generally produces more reliable volatility forecasts than other key methods.     

中国的货币在长期是中性的吗?——基于Fisher-Seater定义的研究

Fisher-Seater对长期货币中性给出了在实证上具有可操作性的定义。基于此定义,本文从货币量冲击的分类、"长期关系"和"长期影响"的区别两方面,更进一步地界定长期货币中性的含义。在此基础上,本文利用1994年以来的季度宏观数据,采用时序回归法和向量自回归法,对中国的长期货币中性是否成立进行检验,实证结果不足以否定长期货币中性。这对用于中国经验的宏观理论模型的构建以及央行货币政策的制定都具有一定的参考价值。     

Optimal Income Taxation With Quasi-Linear Preferences Revisited

Properties of the optimal income tax for quasi-linear in leisure preferences are studied. With utilitarian or maxi-min objectives, closed-form solutions are obtained. Bunching occurs over intervals where the second-order incentive condition is binding. Whether this occurs depends solely on the skill distribution. The patterns of consumption and tax rates in the nonbunched range are independent of whether the second-order incentive constraints are binding. Bunching at the bottom can also occur if a non-negative constraint on incomes is binding for some households. The pattern of marginal tax rates depends on the skill distribution and whether it is truncated.     

Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series

Commonly used Mean Absolute Percentage Errors (MAPE) and the authors’ revised Mean Absolute Percentage Errors (RMAPE) are applied to measure the forecasting accuracy from different Moving Average Methods for independent time series. Simulation results show that both MAPE and RMAPE can only provide sensitive forecasting accuracy measurements on Moving Average Methods when coefficients of variation (c.v.) are smaller than 0.4 or is much greater than 4.0 for those independent time series. For independent time series with moderate c.v.’s, the complexity from the ratios of MAPE and RMAPE will mislead researchers on distinguishing the forecasting accuracies from different Moving Average Methods. The complexity from the ratios will be released only when the c.v. is very small, or when the c.v. is very large. Therefore, when data are from independent time series, the Mean Absolute Deviation (MAD) reveals valid the forecasting accuracies from various Moving Average Methods, but not from MAPE or RMAPE.     

线性回归模型结构稳定性的Eviews诊断

线性回归模型是计量经济学中最基本的模型,当模型涉及时间序列数据时就可能出现模型结构稳定性的问题,即模型参数可能随着时间的变化而变化。本文根据Chow检验的方法步骤。介绍线性回归模型结构稳定性的Eviews诊断方法。     

Ownership structure,size, and interest income substitution by banks: An exploratory study in the Indian context

This study explored how emerging economy banks are rebalancing their interest income and non-interest income to ensure stability. We set our study in India during the period 2005–2017. Interestingly, we observe that time–series correlation of interest income growth and non-interest income growth for public sector banks as well as for private banks is on the negative side. We applied panel vector auto regression and generalised method of moments methodology. This study found that when bank interest income falls, they try to increase their non-interest income to offset their losses to a certain extent, and the trend is increasing. Public sector banks are overall substituting non-interest income for a reduction in the margin, and there is an increasing trend for this substitution. Based on the size of banks, it has been found that change in non-interest income in the subsequent year due to change in interest income in the previous year is there for large banks, whereas no such significant change has been found in case of small banks.     

Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. Probit models are then estimated using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.