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Given the absence of a formal forecasting model of property insurance surtax revenue for the state of Kentucky, this paper presents the insample and out-of-sample forecasts of four models: Holt linear trend algorithm, autoregressive model, linear trend/autoregressive model, and economic activity model based on annual fiscal year data from 1984 to 2001. The Holt linear trend algorithm and the linear trend/autoregressive model were reasonably close in their respective forecasting performance for both the in-sample and out-ofsample forecast horizons. However, the linear trend/autoregressive model exhibited some evidence of instability for the period 1992 to 1994. With respect to the out-of-sample forecasts, the Holt linear trend algorithm provided a better fit to the actual surtax data. Moreover, as time passes and additional data on the surtax becomes available, the models presented can easily be updated and reevaluated.

Homeownership provides shelter and is a vital component of wealth, and house purchase signifies a lifetime achievement for many households. For South Africa confronted with social and structural challenges, homeownership by the low and lower middle-income household is pivotal for its structural transformation process. In spite of these potential benefits, research on the affordable housing market in the context of South Africa is limited. This study aims to contribute to this knowledge gap by answering the question “do changes in household income per capita have a symmetric or asymmetric effect on affordable house prices?”

A survey of the international literature on house prices and income revealed that linear modelling that assumes symmetric reaction of macroeconomic variables dominates the empirical strategy. This linearity assumption is restrictive and fails to capture possible asymmetric dynamics inherent in the housing market. The authors address this empirical limitation by using asymmetric non-linear autoregressive distributed lag models that can test and detect the existence of asymmetry in both the long and short run using data from 1985Q1 to 2016Q3.

The results revealed the presence of an asymmetric long-run relationship between affordable house prices and household income per capita. The estimated asymmetric long-run coefficients of logIncome[+] and logIncome[−] are 1.080 and −4.354, respectively, implying that a 1% increase/decrease in household income per capita induces a 1.08% rise/4.35% decline in affordable house prices everything being equal. The positive increase in affordable house prices creates wealth, helps low and middle-income household climb the property ladder and can reduce inequality, which provides support for the country’s structural transformation process. Conversely, a decline in affordable house prices tends to reduce wealth and widen inequality.

This paper recommends both supply- and demand-side policies to support affordable housing development. Supply-side stimulants should include incentives to attract developers to affordable markets such as municipal serviced land and tax credit. Demand-side policy should focus on asset-based welfare policy; for example, the current Finance Linked Income Subsidy Programme (FLISP). Efficient management and coordination of the FLISP are essential to enhance the affordability of first-time buyers. Given the enormous size of the affordable property market, the practice of mortgage securitization by financial institutions should be monitored, as a persistent decline in income can trigger a systemic risk to the economy.

The study results illustrate the importance of homeownership by low- and middle-income households and that the development of the affordable market segment can boost wealth creation and reduce residential segregation. This, in turn, provides support to the country’s structural transformation process.

The affordable housing market in South Africa is of strategic importance to the economy, accounting for 71.4% of all residential properties. Homeownership by low and lower middle-income households creates wealth, reduces wealth inequality and improves revenue collection for local governments. This paper contributes to the empirical literature by modelling the asymmetric behaviour of affordable house prices to changes in household income per capita and other macroeconomic fundamentals. Based on available evidence, this is the first attempt to examine the dynamic asymmetry between affordable house prices and household income per capita in South Africa.

Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

This paper aims to examine association of money demand with key macroeconomic variables in Pakistan. The paper also investigates the asymmetric effect of real effective exchange rate (REER) on money demand.

The study employs both linear autoregressive distributed lag (ARDL) and non-linear autoregressive distributed lag (NARDL) model. Annual data from 1970 to 2018 is used which is subjected to non-linearity through partial sum concept. Empirical analysis is conducted to prove if money demand is influenced by currency appreciation or depreciation, for long and short run.

Cointegration test indicates existence of a long-run relationship between money demand and its determinants. Results from NARDL model suggest negative relation between money demand and inflation in long and short run. Real income shows positive but a very minimal and insignificant effect on money demand in long and short run. Impact of call money rates is statistically significant and negative on M1 and M2. Wald tests and differing coefficient sign confirm presence of asymmetric relation of REER in long run with M2, whereas in short run we observe a linear, symmetrical relation of REER with M1 and M2. Stability diagnostic tests (CUSUM and CUSUMSQ) verify stability of M2 demand model in Pakistan.

Results signify that role of money demand is imperative as a monetary policy tool and it can be utilized to achieve objective of price stability. Additionally, exchange rate movements should be critically examined by monetary authorities to avoid inflationary pressures resulting from an increase in demand for broad monetary aggregate.

The paper contributes to scarce monetary literature on asymmetrical effects of exchange rate in Pakistan. Impact of variables has been studied through linear approach, but this paper is unique since it attempts to explore non-linear relationships.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

This paper aims to explain real exchange rate fluctuations by means of a model including both standard fundamentals and two alternative measures of inflation expectations for five inflation targeting countries (the UK, Canada, Australia, New Zealand and Sweden) over the period January 1993–July 2019.

Both a benchmark linear autoregressive distributed lag (ARDL) model and a nonlinear autoregressive distributed lag (NARDL) specification are considered.

The results suggest that the nonlinear framework is more appropriate to capture the behaviour of real exchange rates given the presence of asymmetries both in the long and short run. In particular, the speed of adjustment towards the purchasing power parity (PPP) implied long-run equilibrium is three times faster in a nonlinear framework, which provides much stronger evidence in support of PPP. Moreover, inflation expectations play an important role, with survey-based ones having a more sizable effect than market-based ones.

The focus on linearities and the estimation of a NARDL model, which is shown to outperform the linear ARDL model both within sample and out of sample, is an important contribution to the existing literature which has rarely applied this type of framework; the choice of an appropriate econometric method also makes the policy implications of the analysis more reliable; in particular, monetary authorities should aim to achieve a high degree of credibility to manage them and thus currency fluctuations effectively; the inflation targeting framework might be especially appropriate for this purpose.

This paper aims to investigate the determinants of bilateral exports of India to the USA by taking the non-linearity issue in export demand equations which is neglected so far in the empirical work. The study tries to know the reaction of change in exports to exchange rate changes in a non-liner fashion. For this purpose, non-linear autoregressive distributed lag (NARDL) bounds testing approach of Shin et al. (2011) has been used. This approach allows testing for non-linearities both in the short and long run, which might give indications of strategic pricing and non-linearities in exchange rate. The empirical analysis is carried out for bilateral export demand relationships of India with the USA for the period from January 1993 until December 2013. The overall results show that exports are determined in the long run by foreign demand, exchange rates and relative prices. The assumed linearity in export demand functions might be too restrictive. Thereby, the one threshold model that distinguishes exchange rate effects between appreciations and depreciations delivers plausible results. If exchange rate non-linearities are detected, it would seem that exports respond stronger to appreciations than to depreciations. A reason for this might be that firms perform strategic pricing in international trade to gain or maintain market shares.

The paper uses the newly developed non-linear ARDL framework of Shin et al. (2011) to investigate whether there are non-linearities with respect to the exchange rate for India’s exports to the USA. One of the important features of this framework is that it is free from unit root pre-testing and can be applied regardless of whether variables are I(0) or I(1). In addition, ARDL and NARDL technique efficiently determines the cointegrating relation in small sample. The short-run and long-run parameters with appropriate asymptotic inferences can be obtained by applying OLS to NARDL with an appropriate lag length. Following is the NARDL representation of equation 4(a) and 4(b). For brevity, this is illustrated for 4(a) only, where is the first difference operator, P is the drift component and it is the white noise residual, the coefficients ?_1 to ?_4 represent the long-run relationship, whereas remaining expressions with summation sign represent the short-term dynamics of the model. This equation nests the linear ARDL model presented in Pesarean et al. (2001) for the case of d_k^+=d_k^-and ?_2=?_3for all k. Thus, equation is less restrictive than a linear model. For this test, as its distribution is non-standard, Pesarean et al. (2001) tabulate the critical values. The bound test is used to examine the existence of the long-run relationship among the variables in the system. This test is based on Wald/F-statistic and follows a non-standard distribution. To check whether a cointegrating relationship exists, one has to test the null hypothesis ?_1=?_2=?_3=?_4 = 0 in the equation. Pesarean et al. (2001) provide two sets of critical values in which lower critical bound assumes that all the variables in the ARDL are I(0) and upper critical bound assumes I(1). The null hypothesis of cointegration is rejected if the calculated F-statistics is greater than the upper bound critical values. If the F-statistics is below than the lower critical bound, then null hypothesis cannot be rejected; this indicates no cointegration among the variables. If it lies within the lower and upper bounds, the result is inconclusive. After examining the cointegration, long-run coefficients are calculated by estimating the model with the appropriate lag orders based on the Schwarz Information Criteria (SIC). Further, the short-run dynamics of the model is also analyzed by using unrestricted error correction model based on the assumption made by Pesarean et al. (2001). Thus, the error correction version of the NARDL model pertaining to the central export equation can be expressed as: 10; 10, where ? is the speed of adjustment parameter, and EC is the residuals that are obtained from the estimated cointegration model of equation 4(a). The EC term is expressed as 10; 10, where are the OLS estimators obtained from the equation (5a). The coefficients of the lagged variables provide the short-run dynamics of the model covering the equilibrium path. The error correction coefficient ( ) is expected to be less than zero, and its significant value implies the cointegration relation among the variables. Finally, various tests such as serial correlation, functional form, normality and heteroskedasticity have been conducted to check the performance of the model.

Many empirical studies have estimated the elasticities of different final export demand components with respect to the exports because of their importance in trade policy formulation. But all the work has accounted only linearity in the exchange rate in export demand equation. Hence, in this paper, we tried to estimate non-linearities in export demand equation. The study fills the gap in the literature by improving on existing literature with the incorporation of the newly developed NARDL approach of Shin et al. (2011). This approach allows testing for non-linearities both in the short- and in the long run which might give indications of strategic pricing and non-linearities in exchange rate. The empirical analysis is carried out for bilateral export demand relationships of India with the USA for the period from January 1993 until December 2013. The bound test shows that there exists cointegration among the variables. Results show that exports are determined in the long run by foreign demand, exchange rates and relative prices. The long-run coefficients have got the expected sign and are of reasonable magnitude and statistically significant. Regarding non-linearities, the results show that assuming linearity in export demand functions might be too restrictive. Thereby, the one threshold model that distinguishes exchange rate effects between appreciations and depreciations deliver plausible results. If exchange rate non-linearities are detected, it seems that exports respond stronger to appreciations than to depreciations. A reason for this might be that firms perform strategic pricing in international trade to gain or maintain market shares.

The originality of this paper lies in the fact that it applies NARDL approach to Indian trade data (export demand) and analyzes the asymmetrical and non-linear impact of exchange rate changes on Indian exports.

The purpose of this paper is to examine whether there is significant evidence that hedge fund managers engage in deceptive manipulation of their reported performance results.

A model of hedge fund performance has been developed using standard regression analysis incorporating dependent lagged variables and an autoregressive process. In addition, the extreme bounds analysis technique has been used to examine the robustness and sensitivity of the explanatory variables. Finally, the conditional influence of the global stock market’s returns on hedge fund performance and the conditional return behavior of the Hedge Fund Index’s performance have been explored.

This paper begins by identifying a model of hedge fund performance using passive index funds that is well specified and robust. Next, the lag structure associated with hedge fund returns has been examined and it has been determined that it seems to take the hedge fund managers two months to integrate the global stock market’s returns into their reported performance; however, the lagged variables were reduced from the final model. The paper continues to explore the smoothing behavior by conditioning the dependent lagged variables on positive and negative returns and find that managers are conservative in their estimates of positive performance events, but, when experiencing a negative result, they seem to attempt to rapidly integrate that effect into the return series. The strength of their integration increases as the magnitude of the negative performance increases. Finally, the performance of returns for both the Hedge Fund Index and the passive indices were examined and no significant differences between the conditional returns were found.

The results of this analysis illustrate that hedge fund performance is not all that different from the performance of passive indices included in this paper, although it does offer investors access to a unique return distribution. From a management perspective, we are reminded that we need to be cautious about hastily arriving at conclusions about something that looks different or feels different from everything else, because, at times, our preconceived notions will cause us to avoid participating in something that may add value to our organizations. From an investment perspective, sometimes having something that looks and behaves differently from everything else, improves our investment experience.

This paper provides a well-specified and robust model of hedge fund performance and uses extreme bounds analysis to test the robustness of this model. This paper also investigates the smoothing behavior of hedge fund performance by segmenting the returns into two cohorts, and it finds that the smoothing behavior is only significant after the hedge funds produce positive performance results, the strength of the relationship between the global stock market and hedge fund performance is more economically significant if the market has generated a negative performance result in the previous period, and that as the previous period’s performance becomes increasingly negative, the strength of the relationship between the Hedge Fund Index and the global stock market increases.