Forecasting the exchange rate using nonlinear Taylor rule based models

This research utilises a non-linear Smooth Transition Regression (STR) approach to modelling and forecasting the exchange rate, based on the Taylor rule model of exchange rate determination. The separate literatures on exchange rate models and the Taylor rule have already shown that the non-linear specification can outperform the equivalent linear one. In addition the Taylor rule based exchange rate model used here has been augmented with a wealth effect to reflect the increasing importance of the asset markets in monetary policy. Using STR models, the results offer evidence of non-linearity in the variables used and that the interest rate differential is the most appropriate transition variable. We conduct the conventional out-of-sample forecasting performance test, which indicates that the non-linear models outperform their linear equivalents as well as the non-linear UIP model and random walk.     

Non-linear and non-symmetric exchange-rate adjustment: Evidence from medium- and high-inflation countries

This paper analyses a model of non-linear exchange rate adjustment that extends the literature by allowing asymmetric responses to over- and under-valuations. Applying the model to Greece and Turkey, we find that adjustment is asymmetric and that exchange rates depend on the sign as well as the magnitude of deviations, being more responsive to over-valuations than undervaluations. Our findings support and extend the argument that non-linear models of exchange rate adjustment can help to overcome anomalies in exchange rate behaviour. They also suggest that exchange rate adjustment is non-linear in economies where fundamentals models work well.     

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.     

Non-linear exchange rate relationships: An automated model selection approach with indicator saturation

This paper examines whether the explanatory power of exchange rate models can be improved by allowing for cross-country asymmetries and non-linear effects of fundamentals. Both appear to be crucial. The samples include the USD versus pound and yen from 1982:10 to 2013:10, and automated model selection is conducted with indicator saturation. Several non-linear effects are significant at 1%. Further, many of the indicators present in the linear models are eliminated once allowing for non-linearities; suggesting some of the structural breaks found in previous work were an artifact of the misspecified linear functional form. These conclusions are robust to estimation using principal components.     

基于微粒群优化算法的物流配送中心选址问题研究

结合物流配送中心选址的特点，建立数学模型．在此基础上研究了基于微粒群算法的物流多配送中心选址问题，得到一种新的多配送中心选址方法。仿真结果证明此方法比传统选址方法更适合多配送中心选址和非线性问题的优化，并且具有传统算法所不具备的灵活性，适用多样的物流配送模型。     

EMPIRICAL ESTIMATION OF NON-LINEAR INPUT–OUTPUT MODELS: AN ENTROPY ECONOMETRICS APPROACH

Despite theoretical advances, non-linear input–output models have been empirically applied only to a limited extent. This is mainly due to the fact that the number of parameters to be estimated is much higher than the number of available data points. Taking advantage of the recent proliferation of input–output databases and by applying an estimation strategy that relies on entropy econometrics, this paper suggests a way to estimate the parameters that characterize non-linear relationships between inputs and output. This non-linear modelling allows for considering time-specific input coefficients, instead of fixed ones. Several types of multipliers can be derived from this non-linear model, and the proposed generalized maximum entropy (GME) estimator allows estimating them from time series or cross-sectional datasets of input–output tables. The proposed GME technique is illustrated by means of an empirical application that estimates the parameters that characterize a non-linear input–output model for the Spanish economy over the period 1995–2011.     

Some methods for assessing the need for non-linear models in business cycle analysis

It is often suggested that non-linear models are needed to capture business cycle features. In this paper, we subject this view to some critical analysis. We examine two types of non-linear models designed to capture the bounce-back effect in US expansions. This means that these non-linear models produce an improved explanation of the shape of expansions over that provided by linear models. But this is at the expense of making expansions last much longer than they do in reality. Interestingly, the fitted models seem to be influenced by a single point in 1958 when a large negative growth rate in GDP was followed by good positive growth in the next quarter. This seems to have become embedded as a population characteristic and results in overly long and strong expansions. That feature is likely to be a problem for forecasting if another large negative growth rate was observed.     

Revisiting dividend yield dynamics and returns predictability: Evidence from a time-varying ESTR model

Since the bubble of the late 1990s the dividend yield appears non-stationary indicating the breakdown of the equilibrium relationship between prices and dividends. Two lines of research have developed in order to explain this apparent breakdown. First, that the dividend yield is better characterised as a non-linear process and second, that it is subject to mean level shifts. This paper jointly models both of these characteristics by allowing non-linear reversion to a changing mean level. Results support stationarity of this model for eight international dividend yield series. This model is than applied to the forecast of monthly stock returns. Evidence supports our time-varying non-linear model over linear alternatives, particularly so on the basis of an out-of-sample R-squared measure and a trading rule exercise. More detailed examination of the trading rule measure suggests that investors could obtain positive returns, as the model forecasts do not imply excessive trading such that costs would not outweigh returns. Finally, the superior performance of the non-linear model largely arises from its ability to forecast negative returns, whereas linear models are unable to do.     

The Rank-Size rule in the absence of growth

This paper makes a discovery in comparing Steindl's model of a growing system of cities to Champernowne's model of a stationary one: While the so-called Pareto coefficient (a measure of size concentration) of the city size distribution for a growing system is determined by the ratio of the average rate of growth in the sizes of cities to the rate of growth in the number of cities, and is thus independent of the variance in growth rates across cities, and also, to a large degree, independent of their behavior over time, the coefficient is directly proportional to this variance in the case of stationarity. This has interesting policy implications: As long as the urban system is growing as a whole, efforts to reduce rates of growth in high-growth areas and to raise them in low-growth areas, i.e., to reduce the dispersion in growth rates across cities, will have no effect on the shape of the size distribution of cities. However, if the system were to cease to grow, these same efforts would have a potentially great effect on this distribution. This suggests that the customary pessimism expressed by students of urban phenomena in the efficacy of legislation to alter the form of the size distribution of cities, a pessimism induced by their observation of the persistence of the current distribution over time in many countries, is primarily due to the circumstances in which these systems are observed, i.e., in periods of growth and expansion.     

A semiparametric cointegrating regression: Investigating the effects of age distributions on consumption and saving

We consider a semiparametric cointegrating regression model, for which the disequilibrium error is further explained nonparametrically by a functional of distributions changing over time. The paper develops the statistical theories of the model. We propose an efficient econometric estimator and obtain its asymptotic distribution. A specification test for the model is also investigated. The model and methodology are applied to analyze how an aging population in the US influences the consumption level and the savings rate. We find that the impact of age distribution on the consumption level and the savings rate is consistent with the life-cycle hypothesis.     

Separating Technical Change from Time-Varying Technical Inefficiency in the Absence of Distributional Assumptions

This paper proposes an alternative model for separating technical change from time-varying technical inefficiency. The proposed formulation uses the general index, developed by Baltagi and Griffin (1988), to model technical change in the production frontier function and a quadratic function of time, as in Cornwell, Schmidt and Sickles (1990), to capture the temporal pattern of technical inefficiency. In such a setting, all parameters associated with the rate of technical change and the temporal pattern of technical inefficiency are identified separately. Moreover, the proposed formulation is independent of any distributional assumption concerning the one-sided error term associated with technical inefficiency, and it can be estimated in a single stage with non-linear FGLS. Empirical results based on a translog production frontier, and estimates of technical inefficiency and technical change are presented for the UK dairy sector over the period 1982–1992.     

Regional environmental and economic impact evaluation: An input-output approach

This article develops a regional input-output model which deals with both the environmental sector and the traditional treatment of the economy. The model differs from previous work in that environmental interactions are treated non-linearly and explicit account is taken of environmental feedback to the economic sector. Estimation of non-linear environmental feedback makes it possible to more accurately assess the sensitivity of the regional economic-environmental structure to shifts in final demand over time.     

Value and risk dynamics over the innovation cycle

This paper studies investment in intellectual capital and corresponding value and risk dynamics over the innovation cycle. We assume that the innovation cycle consists of three phases, R&D, trial, and market introduction phases. We use a real option investment model to characterize firm value and risk dynamics over the innovation cycle and find that firm value is the sum of the value of assets in place and non-linear option values related to breakthrough, exit, and market introduction options. Firm risk over the innovation cycle is highly non-linear and quite distinct in different phases. During the R&D phase risk is high as the firm faces high operating leverage originating from R&D fixed costs together with technological uncertainty. During the trial phase risk is significantly lower and dominated by option risk to launch the product in the market while after the introduction of the product in the market risk is equivalent to the asset risk of the company. Our model is consistent with the view that positive excess returns of R&D intensive firms are a compensation for risk. Based on this insight we derive several testable predictions.     

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.     

Non-linear,hybrid exchange rate modeling and trading profitability in the foreign exchange market

A neuro-fuzzy decision-making technology is designed and implemented to obtain the optimal daily currency trading rule. It is found that a non-linear, artificial neural network exchange rate microstructure (hybrid) model combined with a fuzzy logic controller generates a set of trading strategies that earn a higher rate of return compared to the simple buy-and-hold strategy. After accounting for realistic transaction costs, the gains from utilizing a dynamic, neuro-fuzzy model are still present.     

Exploring Nonlinearities in Path Models

In causal analysis, path models are an appropriate tool for studying relationships between social phenomena. However, they assume linear linkages between variables, and hence they are not always suitable for describing the complexity and richness of relationships in social phenomena. The aim of this work is to propose an exploratory graphical method to evaluate if the phenomena under analysis are actually characterized by non-linear linkages. In particular, the method is well suited to discovering interactions between the observed variables in path models. The proposed approach, which does not depend on any hypothesis on the error distribution, is based on a series of plots that can be easily interpreted and drawn using standard statistical software. As an additional feature, the plots – which we call joint effect plots – support qualitative interpretation of the non-linear linkages after the path model has been specified. Finally, the proposed method is applied within a case study. Non-linearities are explored in a casual model aiming to find the determinants of remittances of a group of Tunisian migrants in Italy.     

Monte Carlo evidence on the choice between sample selection and two-part models

This Monte Carlo study examines the relative performance of sample selection and two-part models for data with a cluster at zero. The data are drawn from a bivariate normal distribution with a positive correlation. The alternative estimators are examined in terms of means squared error, mean bias and pointwise bias. The sample selection estimators include LIML and FIML. The two-part estimators include a naive (the true specification, omitting the correlation coefficient) and a data-analytic (testimator) variant.In the absence of exclusion restrictions, the two-part models are no worse, and often appreciably better than selection models in terms of mean behavior, but can behave poorly for extreme values of the independent variable. LIML had the worst performance of all four models. Empirically, selection effects are difficult to distinguish from a non-linear (e.g., quadratic) response. With exclusion restrictions, simple selection models were significantly better behaved than a naive two-part model over subranges of the data, but were negligibly better than the data-analytic version.     

基于BP神经网络的西安市宏观经济预测

宏观经济系统是一个复杂的非线性系统,对宏观经济进行预测应采用非线性的工具进行建模。采用BP神经网络对西安市宏观经济指标进行预测,此预测模型只需少量训练样本就可以确定网络的权值和阈值。实验表明模型预测精度高,能够对西安市宏观经济系统中的非线性关系进行描述,使建立的非线性模型与实际系统更加接近。     

A two-level electricity demand model : Evaluation of the connecticut time-of-day pricing test

A two-stage budgeting model is developed for electricity demand where comsumption in each period is treated as a different commodity. A relative household demand model is first estimated, a consistent price index for electricity is constructed, and then a total electricity consumption model is estimated. Economic procedures are derived which permit application of the model to both time-of-day price situations and also declining vlock price situatiions which result in non-linear budget sets. The model is applied to both types of situations- the data from the Connecticut time-of-day pricing test as well as data from the declining block rate situation of the prevoius year. The model is also tested in a forecasting application to time-of-day customers.     

A class of partially adaptive one-step m-estimators for the non-linear regression model with dependent observations

In this paper we consider a class of partially adaptive one-step M-estimators for the non-linear regression model with dependent observations. Those estimators adapt themselves with respect to a measure of the tailthickness of the disturbance distribution (as well as to a measure of the scale). The large-sample behavior of those estimators is examined theoretically for general disturbance distributions and numerically for various specific ones. The estimators considered are motivated by the Student-t maximum-likelihood estimator. Given appropriate specifications of the adaptation parameter the estimators are asymptotically efficient on the family of Student-t distributions including the normal distribution.