Developing policies and programmes aimed at mitigating the impacts of climate change on crop yield requires methods that correctly quantify the dependence of crop yield on weather. This article utilizes recent advances in econometric methodology and proposes an interactive fixed effects model for modelling crop yield. An attractive feature of the proposed model is that it allows for the varying degrees to which different farmers adapt to global events, such as, an increase in the global fertilizer price and an introduction of a new technology. Thus, the proposed method is an improvement over the standard method based on the fixed effects model, which is widely used in the literature for modelling crop yield. We apply the proposed approach to model wheat yields across 24 statistical areas that cover the wheat belt of the state of Victoria, Australia. Our forecasts point to a potential 14% loss in yields in the state of Victoria and a 27% loss in North-West Victoria, under the Hotter & Drier climate scenario, which the IPCC predicts to be more likely for Victoria in the medium term. Should this eventuate, it would have significant negative impacts on the wheat industry, food security and the economy.

We develop a method for constructing prediction intervals for a nonstationary variable, such as GDP. The method uses a factor augmented regression [FAR] model. The predictors in the model includes a small number of factors generated to extract most of the information in a set of panel data on a large number of macroeconomic variables considered to be potential predictors. The novelty of this paper is that it provides a method and justification for a mixture of stationary and nonstationary factors as predictors in the FAR model; we refer to this as mixture-FAR method. This method is important because typically such a large set of panel data, for example the FRED-MD, is likely to contain a mixture of stationary and nonstationary variables. In our simulation study, we observed that the proposed mixture-FAR method performed better than its competitor that requires all the predictors to be nonstationary; the MSE of prediction was at least 33% lower for mixture-FAR. Using the data in FRED-QD for the US, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production. We observed that the mixture-FAR method performed better than its competitors.

This study decomposes the bilateral trade flows using a three-dimensional panel data model. Under the scenario that all three dimensions diverge to infinity, we propose an estimation approach to identify the number of global shocks and countryspecific shocks sequentially, and establish the asymptotic theories accordingly. From the practical point of view, being able to separate the pervasive and nonpervasive shocks in a multi-dimensional panel data is crucial for a range of applications, such as, international financial linkages, migration flows, etc. In the numerical studies, we first conduct intensive simulations to examine the theoretical findings, and then use the proposed approach to investigate the international trade flows from two major trading groups (APEC and EU) over 1982-2019, and quantify the network of bilateral trade.

This is the first paper to propose a flexible local logit regression for defaulted loan recoveries that lie in [0,1]. Via a simulation study, we demonstrate that the proposed model is robust to nonlinearity, and non-normality of errors. Applied to Moody's dataset, the local logit model uncovers the intrinsic nonlinear relationship between loan recoveries and covariates, which include loan/borrower characteristics and economic conditions. We exploit the empirical features of the local logit model to improve the specification of the standard regression for the fractional response variable (RFRV) model, which we refer to as the calibrated-RFRV model. The estimation of the calibrated-RFRV model is more straightforward and faster than the local logit model. The overall out-of-sample predictive performance of the calibrated-RFRV is superior to the local logit, RFRV, neural network (NN), regression tree (RT) and Inverse Gaussian (IG) models. The local logit model outperforms others in quantile forecasting, showing the attractiveness of this model for estimating tail risks, the accurate estimation of which is beneficial to risk managers. (c) 2021 Elsevier B.V. All rights reserved.

This paper introduces a statistical model to estimate and evaluate the predictability of the response of wheat yield to extreme temperature exposures and rainfall during the three phases of wheat grain production (vegetative, reproductive and grain filling) in northwestern (NW) Victoria, Australia. Unlike crop models which rely on functions developed from field experiments, we use observed data on annual wheat yields from 44 farms in the region over a period of 26 years (1993-2018). We find that the one-way fixed effects panel data model tends to outperform competing models in the out-of-sample prediction of future yields. We detect as positive drivers of NW Victorian wheat yield growth, exposure to moderate temperatures in all the three phases of the wheat production and total rainfall in the first two phases of the growing season. Providing adequate soil moisture, January-March rainfall also was found to be a positive driver of yields. Conversely, exposure to freezing temperatures during the vegetative and reproductive phases as well as to extreme high temperatures in all three phases of wheat production constitute negative drivers of NW Victorian wheat yields. The reproductive phase appears to be the most sensitive to climate variability, with adverse extreme heat and frost having sizeable negative impacts on yields. These negative effects are partially offset by increased rainfall in the same phase of wheat production. Moreover, we compare yield predictions by our statistical model to yield potentials calculated by APSIM. The gaps can be used to make recommendations on some adaptation opportunities available to farmers in the NW Victoria region.

In this article, we propose MFCAPM panel models with fixed effects and test theories associated with risk exposures and anomalies postulated by Fama and French, and we assess their out-of-sample predictive performances. Based on the portfolios formed by French, we construct 10 panel models, each consisting of 10 portfolios grouped by size deciles, and another 10 panels by value deciles. In the presence of cross-section dependence, the MFCAPM panel model is estimated by the feasible generalized least squares (FGLS) method for the sample period 1963(1)-2018(9). The results show that the market, firm-size and value risk exposures are significant and robust across three-, five- and six-factor panel models. Significant time-fixed effects indicate that there are several portfolios resilient to dot.com bubble peak in 2000, while some others resilient to GFC in 2007. We estimate the models for the in-sample period 1963(1)-1999(12) and generate the out-of-sample portfolio returns for the period 2000(1)-2018(9). We find that portfolio returns forecasts generated by the six-factor panel model are superior to other MFCAPM panel models, mostly due to the momentum factor (investor behaviour) explaining large return variations and volatility exposures. The findings have implications for investors, security traders and portfolio risk managers.

There is a scientific consensus that global climate change is already impacting on yields of major staple food crops such as rice, wheat, and soybeans, and this situation will progressively worsen posing severe dangers to global food security. The impact on crop yields of climate change may be varying over time and across states, which is not well addressed in the current literature. This paper proposes a three dimensional (3-D) panel model for rice yields with time-varying coefficients of climatic and non-climatic variables. Moreover, the yield trend is allowed to be an unknown function of time and it varies across the states and districts within the state. The trend and coefficient functions are estimated by the nonparametric local linear method. We apply this approach to two Indian states, one is a coastal state Andhra Pradesh with 11 districts, while the other is an inland state Telangana with 9 districts, for the period 1966-2015. We find that the rainfall effect has been mostly insignificant and then negative since 2005 for the coastal state, while it has been positive and increasing overall for the inland state. The average temperature, on the other hand, has been marginally positive for the coastal districts, whereas it has been mostly negative for inland districts. Both minimum and maximum temperatures have had deeper negative effects on inland districts than the coastal counterparts. The results indicate that farmers coastal state have a better adaptation plan for climate change than those in the inland state and that overlooking heterogeneity in the coefficients can vastly underestimate the climate change impact on the crop yields and food security.

This paper introduces a new specification for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coeffcients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for estimating this TVC-HAR model as well as a bootstrap method for constructing confidence intervals for the time varying coefficient functions. In addition, the estimated nonparametric TVC-HAR was calibrated by fitting parametric polynomial functions by minimising the L2-type criterion. The calibrated TVC-HAR and the simple HAR models were tested separately against the nonparametric TVC-HAR model. The test statistics constructed based on the generalised likelihood ratio method augmented with bootstrap method provide evidence in favour of calibrated TVC-HAR model. More importantly, the results of conditional predictive ability test developed by Giacomini and White (2006) indicate that the non-parametric TVC-HAR model consistently outperforms its calibrated counterpart as well as the simple HAR and the HAR-GARCH models in out-of-sample forecasting.

This paper investigates whether or not there are significant changes in the dependence between the Thai equity market and six Asian markets - namely, Singaporean, Malaysian, Hong Kong, Korean, Indonesian and Taiwanese markets - due to 1997-July financial crisis. If so, this may be an indication that the underlying bivariate joint distributions capturing the dependence between the Thai market and these six markets have changed. We employ the chi-plot proposed by Fisher and Switzer (2001) and the Kendall plot proposed by Genest and Boies (2003) to examine the dependence in these six markets for the pre- and post-1997 financial crisis periods. We find that marginal distributions of all seven markets have notably changed due to this financial crisis, and that the functional forms of the underlying joint distributions generating the dependence in the Korean, Indonesian and Taiwan markets have also changed for the post-crisis period. It appears that the same parametric copula can capture the dependence in the Singapore, Malaysia and Hong Kong markets for both pre- and post-crisis periods, and that only the tail indices of bivariate distributions between the Thai and these three markets have changed. It is interesting to observe that the same conclusions can be drawn using both chi- and Kendall plots.

This paper explores the hypothesis that international capital flows are driven in part by the level of economic activity. It investigates the existence of the income mobility of capital, as distinct from the interest mobility of capital, by using Hsiao's approach to testing for Granger causality to model the relationship between Australian investment abroad and the Australian real GDP. We find some evidence that income causes capital flows but no evidence to support reverse causation from capital flows to income. This result suggests that the analytical construct of the income mobility of capital has empirical content.

This paper introduces an innovative nonparametric panel data approach to model the long-run relationship between the monthly oil price index and stock market price indices of ten large net oil importing countries; namely, the United States, Japan, China, South Korea, India, Germany, France, Singapore, Italy and Spain. In the proposed model, we allow the coefficient on the oil price index to be a time-varying function which evolves over time in a way that is assumed to be unknown. We also allow the common trend function to evolve over time, as well as extending the model further to incorporate country-specific trend functions. We employ a data-driven local linear method to estimate these time-varying trend and coefficient functions. The results show that, despite being largely positive, there are several downward trends, reflecting the aftermath of the Iraq war and the recent unprecedented drop in the oil price. Overall, we find that the nonparametric panel data model better captures the way in which the underlying stock-oil price relationship has evolved over time in comparison to the point estimates of the parametric counterpart. Moreover, we find that stock market fundamentals play a significant role in determining the oil-stock price relationship. Our findings have important implication for policymakers and financial speculators.

A semiparametric method is developed for estimating the dependence parameter and the joint distribution of the error term in the multivariate linear regression model. The nonpara- metric part of the method treats the marginal distributions of the error term as unknown, and estimates them by suitable empirical distribution functions. Then a pseudolikelihood is maximized to estimate the dependence parameter. It is shown that this estimator is as- ymptotically normal, and a consistent estimator of its large sample variance is given. A simulation study shows that the proposed semiparametric estimator is better than the para- metric methods available when the error distribution is unknown, which is almost always the case in practice. It turns out that there is no loss of asymptotic e±ciency due to the estimation of the regression parameters. An empirical example on portfolio management is used to illustrate the method. This is an extension of earlier work by Oakes (1994) and Genest et al. (1995) for the case when the observations are independent and identically distributed, and Oakes and Ritz (2000) for the multivariate regression model.

This paper introduces nonparametric methods for estimating 99.9% operational value-at-risk (OpVaR) and its confidence interval (CI), and demonstrates their applications to US business losses. An attractive feature of these new methods is that there is no need to estimate either the entire heavy-tailed loss distribution or the tail region of the distribution. Furthermore, we provide algorithms that facilitate applied researchers and practitioners in risk management area to implement the sophisticated empirical likelihood ratio (ELR) based methodologies to construct the CI of the true underlying 99.9% OpVaR. In a simulation study, we find that the weighted ELR (WELR) CI estimator is more reliable than the ELR CI estimator. The empirical results show that the nonparametric OpVaR estimates are consistently larger than those of other comparable methods, which provide adequate regulatory capitals, particularly during crises. The findings have implications for regulators, and effective and efficient risk financing.

This paper proposes a nonparametric quantile regression (NP-QR) and a partially linear additive QR (PLA-QR) for modelling recovery rates (RR). Using Moody’s Recovery Database, we uncover two novelties of the NP-QR model. First, the local constant estimation of NP-QR model captures the key empirical feature of RR data being bounded in [0,1] interval. Second, the heterogeneity in the impact of borrower characteristics with clarity can be estimated. For example, we show that the way in which the impact of debt cushion on RR depends on the other characteristics, such as collateralisation and the degree of instrument rank of loans, during various economic conditions. By contrast such heterogeneity is reduced to a single dimension in the parametric regression model. Furthermore, accommodating bimodality and heteroscedastic errors, the NP-QR also provides the heterogeneity of these impacts across the various quantiles of the conditional RR distribution. On the other hand, the idiosyncratic marginal effects of borrower characteristics are estimated using the PLA-QR model over the various quantiles of the conditional RR distribution. For example, for loans with very low risk, we find that the RR is less sensitive to the change in debt cushion at the lower quantiles than that at the upper quantile, particularly when the debt cushion is less than 30%, during economic downturns. The findings of this study have implication for lenders designing an optimum treatment rule for borrowers.

This paper investigates the contagion effects in the daily bond yield spreads (relative to Germany) of five peripheral EU countries including Portugal, Italy, Ireland, Greece and Spain, as a consequence of the recent euro-debt crisis, by employing a robust semiparametric copula method. Furthermore, we model both the means and volatilities of daily bond yield spreads in terms of potential determinants. By doing so, we obtain “other effects” free sovereign bond spreads, which together with the robust copula method would correctly uncover the core contagion, when present. The empirical results indicate that the German stock index return, the Euro Interbank Offered Rate, stock index returns of these countries, S&P 500 returns, VIX and sovereign debt ratings have had significant impacts on the bond yield spreads and/or volatilities, particularly in the post-crisis period. We find overwhelming evidence of financial contagion effects among the peripheral countries. The two large countries Spain and Italy appear to be operating independent of each other, whereas Ireland, Greece and Portugal are found to be the exporters of contagion. In globally interconnected financial markets, central bankers and policy makers are concerned about contagion, through which the crisis proliferates around the region and beyond, triggering instability in the financial markets. Thus, our findings have implications for international policy debate, debt crisis risk management and financial market participants.

This paper builds a structural VARMA (SVARMA) model for investigating Canadian monetary policy. Using the scalar component methodology proposed by Athanasopoulos and Vahid (2008a), we first identify a VARMA model and then construct a SVARMA for Canadian monetary policy. Relative to the responses by a structural VAR, the responses generated by the SVARMA are consistent with those supported by various theoretical models and solve economic puzzles commonly found in the empirical literature on monetary policy. The superior out-of-sample forecasting performance of the reduced form VARMA compared to VAR alternatives further advocates the suitability of this framework for small open economies.

This paper investigates nonparametric estimation of density on [0, 1]. The kernel estimator of density on [0, 1] has been found to be sensitive to both bandwidth and kernel. This paper proposes a unified Bayesian framework for choosing both the bandwidth and kernel function. In a simulation study, the Bayesian bandwidth estimator performed better than others, and kernel estimators were sensitive to the choice of the kernel and the shapes of the population densities on [0, 1]. The simulation and empirical results demonstrate that the methods proposed in this paper can improve the way the probability densities on [0, 1] are presently estimated.

In this paper, we study the kernel estimation of the copula density on unit square [0,1]X[0,1], and demonstrate the implementation of this methodology to equity and bond markets. There are two crucial problems associated with this estimator. First, the kernel estimator is biased at the boundaries. Second, the kernel estimator is sensitive to both kernel and bandwidth. To correct the boundary effects, we propose a Gaussian copula (GC) kernel and a logit transformation (LT) kernel estimators, and derive their asymptotic properties. Moreover, we introduce a Bayesian approach to bandwidth and kernel selection, and an L2-type goodness-of-fit test. We conduct a simulation study to assess the finite sample performance of the GC and LT kernels, and two comparable kernels based on Gaussian transformation (GT) and mirror-reflection (MR), with the Bayesian and likelihood cross-validation (LCV) bandwidths. The results show that the performances of the Bayesian and LCV bandwidths are more or less the same. The performance of the kernel functions depends largely on the shapes of the underlying copula densities. The GC kernel density estimate fits the copula density of All Ords and S&P 500 returns well. The t-copula fits the copula density of sovereign bond yield spreads of Greece and Spain well.

This paper investigates stock–bond portfolios' tail risks such as value-at-risk (VaR) and expected shortfall (ES), and the way in which these measures have been affected by the global financial crisis. The semiparametric t-copulas adequately model stock–bond returns joint distributions of G7 countries and Australia. Empirical results show that the (negative) weak stock–bond returns dependence has increased significantly for seven countries after the crisis, except for Italy. However, both VaR and ES have increased for all eight countries. Before the crisis, the minimum portfolio VaR and ES were achieved at an interior solution only for the US, the UK, Australia, Canada and Italy. After the crisis, the corner solution was found for all eight countries. Evidence of "flight to quality" and "safety first" investor behaviour was strong, after the global financial crisis. The semiparametric t-copula adequately forecasts the outer-sample VaR. These findings have implications for global financial regulators and the Basel Committee, whose central focus is currently on increasing the capital requirements as a consequence of the recent global financial crisis.