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Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stochastic gradient boosting, and random forests produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Stochastic gradient boosting accuracy is a few percentage points less than that of random forests for forecast horizons over 10 days. For those looking to forecast the direction of gold and silver prices, tree bagging and random forests offer an attractive combination of accuracy and ease of estimation. For each of gold and silver, a portfolio based on the random forests price direction forecasts outperformed a buy and hold portfolio.
Perry Sadorsky. Predicting Gold and Silver Price Direction Using Tree-Based Classifiers. Journal of Risk and Financial Management 2021, 14, 198 .
AMA StylePerry Sadorsky. Predicting Gold and Silver Price Direction Using Tree-Based Classifiers. Journal of Risk and Financial Management. 2021; 14 (5):198.
Chicago/Turabian StylePerry Sadorsky. 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers." Journal of Risk and Financial Management 14, no. 5: 198.
This study uses time-frequency analysis to examine directional connectedness between US sector equity ETFs, oil, gold, stock market, and uncertainty factors over the short and long terms. Frequency-based spillover index and portfolio hedging methods are applied to develop the empirical results. In analysing directional connectedness, we find that the market's 30-day forward looking expectations of US stock market volatility (VIX) has the strongest effect on the US sector equity ETFs in both the short and long runs. This is followed by the 30-day forward looking expectations of oil price volatility (OVX). Of the uncertainty factors, US economic policy uncertainty has the smallest impact on sector ETFs. Oil has a stronger effect on sector ETFs than gold in the short and long runs. Spillovers between the sector ETFs, gold, oil, and uncertainty factors, are asymmetric for both short and long runs, stronger in the short term, and noticeably increase in times of financial turmoil and economic uncertainty. Portfolio hedging results show that oil is the most effective hedge for each sector ETF in both the short term and the long term. The highest hedging effectiveness is observed for the Consumer Staples ETF.
Sanghoon Kang; Jose Arreola Hernandez; Perry Sadorsky; Ronald McIver. Frequency spillovers, connectedness, and the hedging effectiveness of oil and gold for US sector ETFs. Energy Economics 2021, 99, 105278 .
AMA StyleSanghoon Kang, Jose Arreola Hernandez, Perry Sadorsky, Ronald McIver. Frequency spillovers, connectedness, and the hedging effectiveness of oil and gold for US sector ETFs. Energy Economics. 2021; 99 ():105278.
Chicago/Turabian StyleSanghoon Kang; Jose Arreola Hernandez; Perry Sadorsky; Ronald McIver. 2021. "Frequency spillovers, connectedness, and the hedging effectiveness of oil and gold for US sector ETFs." Energy Economics 99, no. : 105278.
Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.
Perry Sadorsky. A Random Forests Approach to Predicting Clean Energy Stock Prices. Journal of Risk and Financial Management 2021, 14, 48 .
AMA StylePerry Sadorsky. A Random Forests Approach to Predicting Clean Energy Stock Prices. Journal of Risk and Financial Management. 2021; 14 (2):48.
Chicago/Turabian StylePerry Sadorsky. 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices." Journal of Risk and Financial Management 14, no. 2: 48.
Renewable energy is one of the fastest growing segments of energy consumption and wind energy is one of the most widely used sources of renewable energy. There is, however, much less known about the main drivers of wind energy consumption at the country level. This paper uses the logarithmic mean Divisia index (LMDI) method to study the driving factors in wind energy consumption for a group of 17 countries (Australia, Canada, China, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Netherlands, Portugal, Spain, Sweden, United Kingdom, and the United States) that are major consumers of wind energy. The renewable energy share component has the largest impact on wind energy consumption. Improvements in energy intensity are the largest driver of reductions in wind energy consumption. Wind energy consumption forecasts for each country in the business as usual (BAU) scenario for the years 2018–2025 show that compound annual growth rates (CAGRs) for wind energy consumption are highest for Canada, Sweden, China, and Germany. Countries that have high shares of renewable energy like Spain, Portugal, and Denmark have low forecast values of CAGRs. Wind energy forecasts are also calculated for a high growth rate scenario and a low growth rate scenario. Future increases in wind energy consumption are going to depend upon the continued increase in renewable energy share which in turn is affected by energy policy designed to promote fuel switching from fossil fuels to renewables.
Perry Sadorsky. Wind energy for sustainable development: Driving factors and future outlook. Journal of Cleaner Production 2021, 289, 125779 .
AMA StylePerry Sadorsky. Wind energy for sustainable development: Driving factors and future outlook. Journal of Cleaner Production. 2021; 289 ():125779.
Chicago/Turabian StylePerry Sadorsky. 2021. "Wind energy for sustainable development: Driving factors and future outlook." Journal of Cleaner Production 289, no. : 125779.
The 2008–2009 financial crisis, often referred to as the Great Recession, presented one of the greatest challenges to economies since the Great Depression of the 1930s. Before the financial crisis, and in response to the Kyoto Protocol, many countries were making great strides in increasing energy efficiency, reducing carbon dioxide (CO2) emission intensity and reducing their emissions of CO2. During the financial crisis, CO2 emissions declined in response to a decrease in economic activity. The focus of this research is to study how energy related CO2 emissions and their driving factors after the financial crisis compare to the period before the financial crisis. The logarithmic mean Divisia index (LMDI) method is used to decompose changes in country level CO2 emissions into contributing factors representing carbon intensity, energy intensity, economic activity, and population. The analysis is conducted for a group of 19 major countries (G19) which form the core of the G20. For the G19, as a group, the increase in CO2 emissions post-financial crisis was less than the increase in CO2 emissions pre-financial crisis. China is the only BRICS (Brazil, Russia, India, China, South Africa) country to record changes in CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period that were lower than their respective values in the pre-financial crisis period. Compared to the pre-financial crisis period, Germany, France, and Italy also recorded lower CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period. Germany and Great Britain are the only two countries to record negative changes in CO2 emissions over both periods. Continued improvements in reducing CO2 emissions, carbon intensity and energy intensity are hard to come by, as only four out of nineteen countries were able to achieve this. Most countries are experiencing weak decoupling between CO2 emissions and GDP. Germany and France are the two countries that stand out as leaders among the G19.
Perry Sadorsky. Energy Related CO2 Emissions before and after the Financial Crisis. Sustainability 2020, 12, 3867 .
AMA StylePerry Sadorsky. Energy Related CO2 Emissions before and after the Financial Crisis. Sustainability. 2020; 12 (9):3867.
Chicago/Turabian StylePerry Sadorsky. 2020. "Energy Related CO2 Emissions before and after the Financial Crisis." Sustainability 12, no. 9: 3867.
This paper uses a large unique panel data set of 91 OECD and non-OECD countries and recently developed panel regression estimation techniques to answer the question by how much energy demand changes when income and energy prices display asymmetric effects. Both long run and short run impacts are studied. For the full sample, we find the short run impact of a 1% increase in GDP increases energy consumption by 0.35% while a 1% decrease in GDP decreases energy consumption by 0.68%. These values are similar across different country groupings. GDP decreases have a larger impact on energy consumption than increases in GDP by a factor of approximately 2 to 1. We do not, however, find any evidence of asymmetric long run GDP effects. The result that energy demand falls more proportionally when GDP falls then when GDP rises has implications for energy policy and energy demand forecasting. There is evidence of long run price asymmetry for the OECD countries.
Brantley Liddle; Perry Sadorsky. How much do asymmetric changes in income and energy prices affect energy demand? The Journal of Economic Asymmetries 2019, 21, e00141 .
AMA StyleBrantley Liddle, Perry Sadorsky. How much do asymmetric changes in income and energy prices affect energy demand? The Journal of Economic Asymmetries. 2019; 21 ():e00141.
Chicago/Turabian StyleBrantley Liddle; Perry Sadorsky. 2019. "How much do asymmetric changes in income and energy prices affect energy demand?" The Journal of Economic Asymmetries 21, no. : e00141.
The Australian government has recently launched a National Energy Productivity Plan that calls for a 40% increase in energy productivity (economic output divided by energy use) before 2030. Improving energy productivity would help boost economic competitiveness, reduce energy costs, and reduce carbon dioxide emissions in Australia. Understanding energy productivity dynamics at the state level is essential for the success of this program. This research analyses the convergence path of energy productivity in Australian states and territories. Club convergence analysis applied to data over the period 1990–2015 reveals two converging energy productivity clubs. Initial energy productivity, industry structure, and automobile fuel prices are important determinants of higher energy productivity. Based on Australian state energy productivity forecasts to 2030, New South Wales and Victoria will be the forerunners in maintaining higher energy productivity in 2030. Australia will not achieve a 40% increase in energy productivity before 2030 without significant changes to its fuel mix and industry structure.
Mita Bhattacharya; John N. Inekwe; Perry Sadorsky. Convergence of energy productivity in Australian states and territories: Determinants and forecasts. Energy Economics 2019, 85, 104538 .
AMA StyleMita Bhattacharya, John N. Inekwe, Perry Sadorsky. Convergence of energy productivity in Australian states and territories: Determinants and forecasts. Energy Economics. 2019; 85 ():104538.
Chicago/Turabian StyleMita Bhattacharya; John N. Inekwe; Perry Sadorsky. 2019. "Convergence of energy productivity in Australian states and territories: Determinants and forecasts." Energy Economics 85, no. : 104538.
Nonlinear, symmetric, and asymmetric dependence characteristics in energy equity sectors matter to portfolio investors and risk managers because of the risks and diversification opportunities they entail. Specifically, nonlinear dependence dynamics between assets are harder to predict, monitor, and manage, and can make investment positions go wrong unexpectedly. In this paper, we investigate whether the dependence dynamics of US and Canadian large-capitalized energy equity portfolios are nonlinear, symmetric, or asymmetric. We draw our results by implementing a robust copula approach based on time-varying parameter copulas and vine copula methods. Both time varying parameter and vine-copula methods indicate that the Canadian energy sector portfolio is driven by nonlinear negative tail asymmetric dependence during the global financial crisis and when the full sample period is employed. On the other hand, it displays nonlinear symmetric dependence during the oil price crisis, implying the need for close monitoring and rebalancing and a more continuous assessment of long investment positions. The US energy sector portfolio is driven by positive tail asymmetric dependence, and by symmetric dependence dynamics during crisis and non-crisis periods.
Waqas Hanif; Jose Arreola Hernandez; Perry Sadorsky; Seong-Min Yoon. Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric? The North American Journal of Economics and Finance 2019, 51, 101065 .
AMA StyleWaqas Hanif, Jose Arreola Hernandez, Perry Sadorsky, Seong-Min Yoon. Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric? The North American Journal of Economics and Finance. 2019; 51 ():101065.
Chicago/Turabian StyleWaqas Hanif; Jose Arreola Hernandez; Perry Sadorsky; Seong-Min Yoon. 2019. "Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric?" The North American Journal of Economics and Finance 51, no. : 101065.
Irene Henriques; Perry Sadorsky. Investor implications of divesting from fossil fuels. Global Finance Journal 2018, 38, 30 -44.
AMA StyleIrene Henriques, Perry Sadorsky. Investor implications of divesting from fossil fuels. Global Finance Journal. 2018; 38 ():30-44.
Chicago/Turabian StyleIrene Henriques; Perry Sadorsky. 2018. "Investor implications of divesting from fossil fuels." Global Finance Journal 38, no. : 30-44.
Syed Abul Basher; Alfred A. Haug; Perry Sadorsky. The impact of oil-market shocks on stock returns in major oil-exporting countries. Journal of International Money and Finance 2018, 86, 264 -280.
AMA StyleSyed Abul Basher, Alfred A. Haug, Perry Sadorsky. The impact of oil-market shocks on stock returns in major oil-exporting countries. Journal of International Money and Finance. 2018; 86 ():264-280.
Chicago/Turabian StyleSyed Abul Basher; Alfred A. Haug; Perry Sadorsky. 2018. "The impact of oil-market shocks on stock returns in major oil-exporting countries." Journal of International Money and Finance 86, no. : 264-280.
Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An analysis of the economic value shows that risk-averse investors will be willing to pay a high performance fee to switch from a portfolio with gold to a portfolio with bitcoin. These results are robust to the inclusion of trading costs.
Irene Henriques; Perry Sadorsky. Can Bitcoin Replace Gold in an Investment Portfolio? Journal of Risk and Financial Management 2018, 11, 48 .
AMA StyleIrene Henriques, Perry Sadorsky. Can Bitcoin Replace Gold in an Investment Portfolio? Journal of Risk and Financial Management. 2018; 11 (3):48.
Chicago/Turabian StyleIrene Henriques; Perry Sadorsky. 2018. "Can Bitcoin Replace Gold in an Investment Portfolio?" Journal of Risk and Financial Management 11, no. 3: 48.
The Indian government has a number of ambitious economic and energy related initiatives including increasing access to electricity (“24X7 Power for All”), greater economic activity from manufacturing (“Make in India”), and reducing carbon dioxide emissions. Energy productivity is an important factor in helping to achieve these objectives. In this paper, we test the hypothesis of energy productivity convergence in a panel of contiguous states and territories (S&Ts) in India. In measuring energy productivity at the S&T level, we use a unique firm-level dataset maintained by the Centre for Monitoring Indian Economy (CMIE) for the period 1988 to 2016. We identify convergence clubs across Indian S&Ts; i.e. we identify groups of states that converge to different equilibria. The findings from the club merging analysis show that energy productivity across the S&Ts converges into two clubs with one divergent club. Higher initial energy productivity makes it more likely for states to be in the high energy productivity club. Industry structure is also an important determinant. The club convergence of the S&Ts has implications for Indian energy policy.
Mita Bhattacharya; John Nkwoma Inekwe; Perry Sadorsky; Anjan Saha. Convergence of energy productivity across Indian states and territories. Energy Economics 2018, 74, 427 -440.
AMA StyleMita Bhattacharya, John Nkwoma Inekwe, Perry Sadorsky, Anjan Saha. Convergence of energy productivity across Indian states and territories. Energy Economics. 2018; 74 ():427-440.
Chicago/Turabian StyleMita Bhattacharya; John Nkwoma Inekwe; Perry Sadorsky; Anjan Saha. 2018. "Convergence of energy productivity across Indian states and territories." Energy Economics 74, no. : 427-440.
Clean energy equities represent a relatively new class of assets to invest in, and these assets can be very volatile. An understanding of how investors in clean energy stocks can hedge their investment is essential for risk management. In this study, we use daily data covering the period March 03, 2008 to October 31, 2017, to examine how crude oil, US-bonds, gold, VIX, OVX and European carbon prices can be used to hedge an investment in clean energy equities. We apply three variants of multivariate GARCH models (DCC, ADCC and GO-GARCH) to estimate time-varying optimal hedge ratios. The results suggest that VIX is the best asset to hedge clean energy equities followed by crude oil and OVX. This is a new result relative to the existing literature on clean energy stock prices and one that is of interest to current and future investors in clean energy stocks.
Wasim Ahmad; Perry Sadorsky; Amit Sharma. Optimal hedge ratios for clean energy equities. Economic Modelling 2018, 72, 278 -295.
AMA StyleWasim Ahmad, Perry Sadorsky, Amit Sharma. Optimal hedge ratios for clean energy equities. Economic Modelling. 2018; 72 ():278-295.
Chicago/Turabian StyleWasim Ahmad; Perry Sadorsky; Amit Sharma. 2018. "Optimal hedge ratios for clean energy equities." Economic Modelling 72, no. : 278-295.
Muhammad Shahbaz; Syed Jawad Hussain Shahzad; Mantu Kumar Mahalik; Perry Sadorsky. How strong is the causal relationship between globalization and energy consumption in developed economies? A country-specific time-series and panel analysis. Applied Economics 2017, 50, 1479 -1494.
AMA StyleMuhammad Shahbaz, Syed Jawad Hussain Shahzad, Mantu Kumar Mahalik, Perry Sadorsky. How strong is the causal relationship between globalization and energy consumption in developed economies? A country-specific time-series and panel analysis. Applied Economics. 2017; 50 (13):1479-1494.
Chicago/Turabian StyleMuhammad Shahbaz; Syed Jawad Hussain Shahzad; Mantu Kumar Mahalik; Perry Sadorsky. 2017. "How strong is the causal relationship between globalization and energy consumption in developed economies? A country-specific time-series and panel analysis." Applied Economics 50, no. 13: 1479-1494.
Brantley Liddle; Perry Sadorsky. How much does increasing non-fossil fuels in electricity generation reduce carbon dioxide emissions? Applied Energy 2017, 197, 212 -221.
AMA StyleBrantley Liddle, Perry Sadorsky. How much does increasing non-fossil fuels in electricity generation reduce carbon dioxide emissions? Applied Energy. 2017; 197 ():212-221.
Chicago/Turabian StyleBrantley Liddle; Perry Sadorsky. 2017. "How much does increasing non-fossil fuels in electricity generation reduce carbon dioxide emissions?" Applied Energy 197, no. : 212-221.
Cleantech venture capital investment differs from the typical venture capital investment in that it tends to be very capital intensive and faces greater technology risks associated with the functioning of the technology, scalability and exit requirements than the typical venture capital investment. Moreover, unlike the typical venture capital investment, the benefits arising from cleantech cannot be totally captured by the venture capitalist as many of its benefits accrue to society via reduced environmental degradation and better health and quality of life outcomes. The public goods literature posits that such externalities reduce investment in cleantech below the socially optimal level. We seek to determine whether there are countervailing factors which may incite greater cleantech investment. We argue that oil prices, increased stakeholder attention, as well as the impact of various formal and informal institutions are such factors. This paper provides a cross-country analysis of the determinants of cleantech venture capital investment with a unique worldwide dataset of 31 countries spanning 1996–2010. The data show consistent evidence of a pronounced role for oil prices in driving cleantech venture capital deals, which is more important than other economic, legal or institutional variables. Cleantech media coverage is likewise a statistically significant determinant of cleantech venture capital investment and as economically significant as other country level legal, governance, and cultural variables. Uncertainty avoidance has a negative impact on cleantech venture capital investment, as well as a moderating effect on other variables.
Douglas Cumming; Irene Henriques; Perry Sadorsky. ‘Cleantech’ venture capital around the world. International Review of Financial Analysis 2016, 44, 86 -97.
AMA StyleDouglas Cumming, Irene Henriques, Perry Sadorsky. ‘Cleantech’ venture capital around the world. International Review of Financial Analysis. 2016; 44 ():86-97.
Chicago/Turabian StyleDouglas Cumming; Irene Henriques; Perry Sadorsky. 2016. "‘Cleantech’ venture capital around the world." International Review of Financial Analysis 44, no. : 86-97.
Using annual data for the period 1971–2012, this study explores the relationship between globalization and energy consumption for India by endogenizing economic growth, financial development and urbanization. The cointegration test proposed by Bayer–Hanck (2013) is applied to estimate the long-run and short-run relationships among the variables. After confirming the existence of cointegration, the overall results from the estimation of an ARDL energy demand function reveal that in the long run, the acceleration of globalization (measured in three dimensions — economic, social and overall globalization) leads to a decline in energy demand in India. Furthermore, while financial development is negatively related to energy consumption, economic growth and urbanization are the key factors leading to increased energy demand in the long run. These results have policy implications for the sustainable development of India. In particular, globalization and financial development provide a win–win situation for India to increase its economic growth in the long run and become more environmentally sustainable.
Muhammad Shahbaz; Hrushikesh Mallick; Mantu Kumar Mahalik; Perry Sadorsky. The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development. Energy Economics 2016, 55, 52 -68.
AMA StyleMuhammad Shahbaz, Hrushikesh Mallick, Mantu Kumar Mahalik, Perry Sadorsky. The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development. Energy Economics. 2016; 55 ():52-68.
Chicago/Turabian StyleMuhammad Shahbaz; Hrushikesh Mallick; Mantu Kumar Mahalik; Perry Sadorsky. 2016. "The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development." Energy Economics 55, no. : 52-68.
Syed Abul Basher; Perry Sadorsky. Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics 2016, 54, 235 -247.
AMA StyleSyed Abul Basher, Perry Sadorsky. Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics. 2016; 54 ():235-247.
Chicago/Turabian StyleSyed Abul Basher; Perry Sadorsky. 2016. "Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH." Energy Economics 54, no. : 235-247.
This paper uses Markov-switching models to investigate the impact of oil shocks on real exchange rates for a sample of oil exporting and oil importing countries. This is an important topic to study because an oil shock can affect a country’s terms of trade which can affect its competitiveness. We detect significant exchange rate appreciation pressures in oil exporting economies after oil demand shocks. We find limited evidence that oil supply shocks affect exchange rates. Global economic demand shocks affect exchange rates in both oil exporting and importing countries, though there is no systematic pattern of appreciating and depreciating real exchange rates. The results lend support to the presence of regime switching for the effects of oil shocks on real exchange rates.
Syed Abul Basher; Alfred A. Haug; Perry Sadorsky. The impact of oil shocks on exchange rates: A Markov-switching approach. Energy Economics 2016, 54, 11 -23.
AMA StyleSyed Abul Basher, Alfred A. Haug, Perry Sadorsky. The impact of oil shocks on exchange rates: A Markov-switching approach. Energy Economics. 2016; 54 ():11-23.
Chicago/Turabian StyleSyed Abul Basher; Alfred A. Haug; Perry Sadorsky. 2016. "The impact of oil shocks on exchange rates: A Markov-switching approach." Energy Economics 54, no. : 11-23.
Mortgage rates are one of the important drivers of the housing market. While there is a literature looking at the pass-through effect from Central Bank rates to mortgage rates, there is less known about how useful Central Bank rates are for forecasting mortgage rates. This article uses a selection of models (ARIMA, ARIMAX, BATS, state space error, trend seasonal (ETS), Holt Winter, random walk, simple exponential smoothing (SES), OLS and VAR) to forecast Canadian 5-year conventional mortgage rates. Based on RMSE, regression-based approaches like ARIMAX or OLS that use Central Bank rates to forecast mortgage rates are preferred when it comes to forecasting Canadian mortgage rates 6 or 12 months into the future, respectively.
Perry Sadorsky. Forecasting Canadian mortgage rates. Applied Economics Letters 2015, 23, 1 -4.
AMA StylePerry Sadorsky. Forecasting Canadian mortgage rates. Applied Economics Letters. 2015; 23 (11):1-4.
Chicago/Turabian StylePerry Sadorsky. 2015. "Forecasting Canadian mortgage rates." Applied Economics Letters 23, no. 11: 1-4.