Econometrics Model

International tourism is considered an effective means of economic development. However, the effects of tourism are not evenly distributed between rural and urban households in China. In the wake of significant socioeconomic events, the uneven distribution of the economic effects has huge welfare implications for Chinese households. This study is the first attempt to evaluate the distributional effect of two large, recent, sequential events on China's rural and urban households. It adopts an innovative approach that combines an econometric model and a two-household computable general equilibrium model. The results show that in terms of welfare, urban households were more adversely affected by the events than rural households. To mitigate the loss of welfare, measures should be taken to continually promote China as a destination and attract tourists after such events occur. Meanwhile, training and education should be made more accessible to rural households to increase their job opportunities.

How can we explain our consumption of cocoa? Is it the caffeine in this hot, stimulating drink that keeps us buying? But then again wouldn’t we substitute it for other hot, stimulating caffeine containing drinks like tea or coffee? Could sugar be a complementary product for cocoa? These are some of the interesting questions that are investigated and are attempted to be answered in this economic report.

This paper aims to provide the most up-to-date survey of tourism economics research and to summarise the key trends in its recent development. Particular attention is paid to the research progress made over the last decade in respect of approaches, methodological innovations, emerging topics, research gaps, and directions for future research. Remarkable but unbalanced developments have been observed across different sub-research areas in tourism economics. While neoclassical economics has contributed the most to the development of tourism economics, alternative schools of thought in economics have also emerged in advancing our understanding of tourism from different perspectives. As tourism studies are multi- and inter-disciplinary, integrating economics with other social science disciplines will further contribute to knowledge creation in tourism studies.

This is a time-series analysis aimed primarily to test different macroeconomic models of consumption, or more specifically to test the consumers’ non-durable expenditure in the United Kingdom. It aims to examine the extent to which household consumption (the exogenous variable) is directly related to present income (the endogenous variable) but also to investigate the influence of past income levels.

The study involves Ordinary Least Squares (OLS) regression of aggregate United Kingdom consumption data to econometric functions, although focus is on the Keynesian and DHSY models. The period that is covered in this dissertation is between 1936, when Keynes introduced marginal propensity to consume, and 2004. Therefore, the literature review includes a mixture of both classical and recent works.

There are many potential issues in modelling the aggregate UK consumptions using time-series data. One example is the existence of uncontrolled - i.e. beyond the scope of what the model can capture based on theory - economic factors that affect annual consumption of non-durable goods leading to autocorrelation, heteroskedasticity, model misspecification and so forth. Thus, the economic reason for pursuing this study is to investigate how the components of the model relate to each other and to analyse the causes of the consequently biased models.

: The purpose of this study is to develop models to predict the level of innovative development of countries, as
well as to identify the most significant factors influencing innovative development.
The scientific novelty consists in applying a systematic, integrated approach to the selection of statistically significant
factors that are drivers of innovative development, with the subsequent construction of econometric models and their
testing. When developing models, both resources (“input parameters”) and results (“output parameters”) were taken into
account, which also allows evaluating the effectiveness of innovative development and developing scenario forecasts
taking into account the existing possibilities and limitations, optimizing innovative development strategies.
The main methods of research and approaches were used: statistical summary and grouping of information, trend
analysis, regression and correlation analysis, testing of statistical hypotheses, factor analysis. The procedure for
detecting multicollinearity was performed using the VIF test (Variance Inflation Factor, incremental regression method).
In determining the set of explanatory variables (the choice of "short" or "long" regression), the following criteria were
used: Akaike criterion and Bayesian Schwarz information criterion. To estimate the parameters of econometric models,
the Least Squares Method was used with a preliminary check of the fulfillment of all conditions of the Gauss-Markov
theorem. In addition, various tests for checking the constructed models and their parameters for significance, adequacy
were applied: Durbin-Watson test, Sved-Eisenhart series method and Breush-Godfrey test, Helvig agreement test,
Shapiro-Wilk test, Goldfeld-Quandt test and Spearman's rank correlation test. To determine the influence of explanatory
factors on the explained factor, the average elasticity coefficients were calculated on the basis of linear regression as the
best model based on the results of all tests.
Data and Empirical Analysis: The main components included in the calculation of the Global Innovation Index (GII) were
selected for the study. Statistical data on them are published annually, which allows us to estimate the country’s place in
international innovation development. The study identified four multiple econometric models: one linear and three nonlinear. The value of the Global Innovation Index was chosen as an explained factor, and the indicators for the main
groups in accordance with the GII structure were chosen as explanatory factors.
To achieve this goal, the following work was carried out, as reflected in this article: 1) an econometric analysis was
performed based on a sample of 30 countries based on the 2018 Global Innovation Index report; 2) multiple regression
models were built - linear, polynomial, hyperbolic and power; 3) with the use of special tests, a check for
heteroscedasticity and autocorrelation of random residues was implemented; 4) the parameters and the obtained
regressions were estimated for statistical significance and adequacy.
According to the results of the study, the model that best approximates the initial data was chosen. Using this regression,
one can form scenario forecasts of the country's innovative development, for example, by predicting the values of
individual factors using various modern methods of macroeconomic planning and forecasting. The principle is the
expediency of the most optimal combination of resources for innovative development in order to ensure the maximum
effect on the "output".

Accurate forecast of inbound tourism demand is vital for the tourism industry as well as government economic policy and decision making. This article sought to identify the factors which influence the demand for China’s tourism with the aid of econometric models and to generate forecasts of international tourist arrivals to China from five major long-haul source markets. Using the general-to-specific modelling approach, the demand for tourism in China by the residents of Australia, Canada, Germany, the United Kingdom and the United States of America is modelled and forecasted. The empirical results indicate that the ‘word of mouth effect’, income levels in the origin country, the costs of tourism in both China and competing destinations are the crucial factors that determine the demand for China’s tourism by residents of the five origin countries. The forecasts show sluggish growth in tourist arrivals for most of the Western source markets. Findings hold implications for policy formulations.

Ray Fair is one of my favorite econometricians. He has an excellent website, fairmodel.econ.yale.edu, where he freely shares his models. He writes clearly and his methods are reported in detail. He is concerned with the proper use of econometric methods. For example, he has long been opposed to the common practice of making ex post subjective adjustments to forecasts from econometric models. A review of empirical evidence supports his position (Armstrong and Collopy 1998).