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Editorial Reviews. Review. "If you're looking to try out inspirational fiction, this wouldn't be a Janette Oke. Religion & Spirituality Kindle eBooks @ Amazon. com. Published: Ada, MI: Baker Pub., Series: Oke, Janette, Love comes softly series ; bk. 1. Subjects: Davis family (Fictitious characters: Oke) > Fiction. Read "Love Comes Softly (Love Comes Softly Book #1)" by Janette Oke available from Rakuten Kobo. Sign up today and get $5 off your first purchase. Love.

Love Comes Softly Ebook

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The Love Comes Softly Collection. Love Comes Softly (Series). Janette Oke Author (). cover image of Love Takes Wing. Love Comes Softly introduced the characters of Marty and Clark Davis, whose tragic circumstances brought them to a "marriage of convenience" on the frontier . Love Comes Softly introduced the characters of Marty and Clark Davis, whose tragic circumstances brought them to a “marriage of.

Table 1 provides a selection of contributions that investigate the fundamental drivers of food price spikes. Important gaps in the literature include the so-called disputed causes of food price spikes, namely, biofuels and speculation on food and agricultural commodity markets.

The literature on food prices and biofuels e. On one side, there are studies that use time series techniques to investigate the dynamic linkages between biofuels and food commodities by referring to historical data on commodity prices and food indexes.

Table 2 provides a selection of the results found by these studies in terms of the percentage weighting assigned to biofuels in rising food prices. All these aspects make the findings from these studies difficult to compare [10]. However, a common aspect underlined by the majority of them is the identification of expanding biofuel consumption as a driver of rising food prices [11].

A second part of the literature is based on numerical models. These project the impact of the introduction of various biofuel trade and policy scenarios on food and commodity quantities and prices in the medium term, that is, between Their findings are based on partial equilibrium PE or computable general equilibrium CGE models and, as in the case of the previous body of the literature, they generally, but not always, underline an increase in agricultural commodity prices as a consequence of expanded biofuel production.

Apart from the expected smaller price effect in CGE models than in PE models due to their incorporation of the almost complete adjustment throughout the economy to the initial stimulus, the scale of the effects varies widely across studies. The major findings are presented in Table 3. These simulation models suggest that the impact of biofuel expansion on commodity prices can be amplified or reduced according to the factors included in the analysis.

The literature on the role of speculation in food price crises can be articulated into two main bodies. On one side, there are the empirical studies that find evidence of a commodity price bubble due to excessive speculation and, on the other, there are those refuting this hypothesis. Observing the comovements between commodity indices and the total amount of financial resources involved in commodity index funds from to , Masters argues that commodity prices in were mainly driven by the rapidly increased engagement of common index traders in futures markets.

In contrast to those for physical and financial assets, hedgers and speculators coexist in commodity futures markets; as soon as speculators start to dominate markets, price bubbles may come into existence if trade is detached from fundamental movements. The majority of these are based on evidence derived from Granger causality tests Table 4. This body of the literature interprets speculation on futures markets as a factor that amplifies price spikes and volatility during food crises without measuring the intensity of the effect.

However, it should be noted that this analysis shows many limitations, as Bass [19, page 52] himself admits, particularly because it adopts an ordinary least squares regression on annual data with a low number of observations. A final major contribution is by von Braun and Tadesse [20] that, among other aspects, detects the conditions for the emergence of price bubbles. The analysis concludes that short-term price spikes can be related to excessive speculative activity on futures markets, while volatility is better explained by demand shocks since oil price spikes increase demand for agricultural commodities.

Bubble opponents argue that the view of the proponents of this hypothesis lacks explanatory power and is rather unrealistic; in particular, they suggest that it requires a better understanding of the essential mechanisms of futures markets [21].

The main argument is that additional money on futures markets does not equal more demand. Futures markets are different from physical markets, since supply on long positions in theory is unlimited and to every additional long position held by index traders there exists a short counterpart. Moreover, opponents criticize the strict differentiation between hedgers and speculators and argue that today noncommercials as well as commercials strategically invest in long and short positions.

This issue is investigated by Irwin and Sanders [22].


Introducing a new econometric approach into the relevant literature, namely, the Fama-Mac Beth regression procedure, the results confirm earlier achievements [23, 24], pointing out that, from to , there are no signals of excessive speculation and no potential positive causation between financial engagement and price volatility in futures markets. Most of the speculative positions held by index traders are offset before the expiration date.

They are detached from the delivery process and do not affect physical supply and demand. In his view, transmission only works by means of arbitrage and the physical hoarding of commodities.

If detached high prices were transmitted from futures to spot markets, this would be indicated by means of an accelerated accumulation of global stocks.

Bubble opponents also critically discuss the degree of reliance of the used Commodity Futures Trading Commission data, stating that they only provide a fair approximation of the realized position changes of commodity index traders [25].

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The close examination of the econometric analyses conducted in different studies finds that some studies have not clearly differentiated between different market levels and their relations. In many of them, the link between speculative activities, futures markets, and spot prices remains undefined. An attempt to consider these aspects is the study by Sassi and Werner [26] that focuses on the speculative activity of different typologies of speculators on the futures market of the Hard Red Winter wheat from to , introducing a methodology that distinguishes between the realized effects of futures and spot prices.

Their evidence supports the hypothesis by Robles et al. This section investigates the long-run and short-run fluctuations in commodity food prices by adopting a structural time series model, that is, the unobservable component time series model.

This approach consists of useful components, such as trends, cycles, seasonal, and irregular, for analyzing the time series under consideration. Each component can be modeled as a stochastic process that depends on normally distributed disturbances.

The unobservable component model used in the present paper has the additional advantage that it can also be used to generate effective forecasts, since more weights are given to the most recent observations. Furthermore, the study by Cashin and Mc Dermott [29] provides a literature review of the empirical work on the behavior of real commodity prices.

Previous empirical work employing structural time series models to analyze commodity prices includes that by Labys et al. The aforementioned work indicates that the most common model used to analyze commodity price movements is the unit root model or the stationary autoregressive model.

The present paper, however, employs the unobserved component model, which has not been extensively used in the literature, to analyze and forecast movements in commodity food prices. To obtain a better understanding of the evolution of commodity food prices, the structural trend methodology of Koopman et al. Let the logarithm of the commodity food price index be presented as indicates the long-run permanent component of the series and shows the direction towards which the series is moving.

In particular, the trend component captures permanent demand and supply changes as well as changes in any unobserved factor that are considered to be permanent.

In the present analysis, the empirical results support the fact that the trend component should be better specified as a fixed term and given by captures the fact that agricultural commodities are influenced by the weather. Furthermore, most primary crops wheat, soybeans, and corn are harvested once per year, which causes seasonal fluctuations in prices.

In the present model specification, the seasonal component has a trigonometric deterministic seasonal form, which is given by. Agricultural commodity prices are known for exhibiting price cycles beyond that explained by seasonality. In particular, livestock prices, such as hog and cattle prices, exhibit cyclical behavior. According to Sterns and Petry [30], hog cycles last for about four years, while based on Lawrence [31], cattle price cycles last for about 10 years.

In this model, the cycle component indicates the lagged values of the dependent variable. In the present model, four lagged values, that is, 1, 3, 4, and 5, are included because these are found to be statistically significant. Two exogenous variables , such as crude oil petroleum and the US real effective exchange rate, are included in structural model 1.

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The crude oil petroleum price is considered because when it increases farmers face higher prices for fuel and fertilizer and thus livestock and crop production costs increase. Furthermore, high oil prices make biofuel production more profitable and this causes increases in the prices of grain, sugar, and vegetable oils, which are used not only in food production but also in biofuel production.

The US real effective exchange rate is used because the US dollar is the main currency for the global trade of most commodities and other goods. Commodity food prices measured in dollars increase when the dollar depreciates against other currencies and decrease when the dollar appreciates.

The inverse relationship between the exchange rate and commodity food prices can also be attributed to inflation. More specifically, when the dollar depreciates, investors and speculators concerned about higher inflation rates invest in commodities futures such as grains, thereby driving up commodity food prices. Intervention variables are dummy or indicator variables that are used to capture structural breaks or outlying irregular observations.

A structural break is modeled by a step intervention variable that takes the value of zero before the event and one after. The structural break dummy variable shifts the level of the series up or down and can be attributed to permanent events, such as changes in economic policies and structural reforms.

An outlier irregular is modeled by an impulse intervention variable that takes the value of one at the time of the outlier and zero otherwise. An outlier can be considered to be a temporally large value of the irregular disturbance at a given time and can be attributed to temporary events such as oil price or weather shocks.

The commodity food price index includes the price indices for cereals, vegetable oils, meat, seafood, sugar, bananas, and oranges, and it is obtained from the International Monetary Fund IMF.

Two exogenous variables are used in the present model: the logarithm of crude oil monthly prices lcoil and the logarithm of the US monthly real effective exchange rate lreer. The crude oil petroleum price is measured in US dollars per barrel and is a simple average of three spot prices, that is, Dated Brent, West Texas Intermediate, and the Dubai Fateh.

Thus, the US real effective exchange rate measures the overall value of the dollar against a basket of 27 currencies. The descriptive statistics means and standard deviations of the data set used in the present paper are reported in Table 5. Figure 3 presents the evolution of the IMF commodity food price index from 11 to Commodity food prices increased dramatically between late and mid Prices then fell drastically in the final months of , after peaking at their highest level in 30 years in the second quarter of They reached at a level slightly higher than that of in the first months of and then rose in the first half of and during They decreased in the second half of but increased again during and by the third quarter of reached the price level.

The resurgence of high commodity food prices in , early , and by the third quarter of prompted concerns of a repeat of the — food crisis, threatening increasing food insecurity, food price inflation, and civil unrest. Note that the causes of the — food crisis have been discussed in detail in previous sections of the present paper.

Table 6 presents some diagnostics and goodness-of-fit statistics, such as the Log Figure 4 presents additional information about the estimated model, namely, graphs of the standardized residuals, residual correlogram, spectral density, and density.

The residuals are the standardized one-step-ahead perdition errors or innovations, as defined in Koopman et al. Thus, the aforementioned statistics Table 6 and graphs presented in Figure 4 are the means of checking the validity of the model.

In particular, the correlogram and spectral density graphs presented in Figure 4 indicate that the residuals are not autocorrelated. Note that the theoretical spectrum is a horizontal straight line for white-noise residuals. These results indicate that even though both cycles show a high degree of persistence, they are stationary, since their damping factors are less than one. Thus, in the long run the cyclical components dissipate, and the forecast of the commodity food price series converges towards its trend value.

The estimated periodicity of cycle 2 is about two years, which could be considered to be the result of the averaging process in which the cyclical values of individual commodity food price series constituting the aggregate commodity food price index interact.

More specifically, the cyclical activity in agricultural food prices is often the result obtained with a one-year cycle for annual crops, up to two-year cycles for livestock production, and up to six-year cycles for perennial crops.

The empirical results of the present paper related to the cyclical activity of commodity food prices are comparable with those proposed by Labys et al. An inspection of the graph corresponding to the long cycle cycle 2 in Figure 5 indicates that the more pronounced cycle activity occurred after This finding is consistent with the literature which indicates that factors causing higher and more volatile commodity food prices came into effect in and eventually these factors caused the — food crisis as well as the subsequent food price variability.

The results also indicate that the amplitude of cycle 1 as a percent of the level is 3. The importance of seasonal effects is statistically significant since the -statistic presented in Table 9 is significant. In particular, seven out of 12 seasonal effects are statistically significant at conventional levels of significance. These effects indicate that from August to December commodity food prices drop below the trend level; from February to May they are above the trend level; and for the months of January, June, and July they are at the trend level.

Thus, the commodity food price index is, on average, lower than the trend level by 0. However, it is, on average, higher than the trend level by 1.

The impact of seasonality might be mainly attributed to crop production such as corn, soybeans, and wheat. Most of these crops are harvested once per year, in fall, and thus the price level drops below the trend line in the fall, as indicated by the empirical results presented in Table 9. By contrast, the price level is above the trend line earlier in the year, that is, from February to May, since food manufacturers buy high quantities of crops to protect themselves against possible tight crop supplies later in the year, that is, after the harvest.

Note that this drives up commodity food prices earlier in the year, but as harvest time approaches, that is, June and July, commodity food prices approach the trend price level. The empirical results in Table 9 show that the effects of the four lagged values on the commodity food price index, that is, , are statistically significant, indicating that past prices affect the current price level.

In the same manner, the effects of the two explanatory variables, that is, crude oil lcoil and the US real effective exchange rate lreer , are statistically significant and they have the expected signs.

Further, the diagnostic tests on the auxiliary residuals are presented in Table 10 and these indicate that they are generally well behaved. Auxiliary residuals are smoothed estimates of irregular and level disturbances [44].

The empirical results on the intervention effects reported in Table 9 show that 18 effects are related to structural breaks while nine are related to outliers. Most of the structural breaks, that is, 12 out of 18, have a positive effect on the price level, while most of the outliers, that is, seven out of nine, have a negative effect. It is worth noting that the — food crisis is captured by the structural break interventions, that is, 10 , 6 , 2 , and 1 , while the price decrease due to the dollar appreciation is captured by the 7 break intervention.

Furthermore, the 20 price increases are captured by the corresponding break dummies, 12 and 7 , respectively. It should also be stated that the remaining structural break dummies take into account the trend of the price level series quite well. For example, the 9 , 12 , and 2 structural break interventions take into account the downward trend in the commodity food price level from to , while the 6 and 4 ones capture the upward trend in price level. Finally, the outliers capture some temporal effects, that is, short-lived spikes, well.

Figure 7 shows the prediction graphics created by estimating the model from 11 to 11 and reserving 12 to 10 for the out-of-sample forecast.

Predictions are made using the information at the end of and are updated with the arrival of each new observation. The predicted values of the commodity food price index lcfpi and residuals are inside the prediction intervals, set at two root mean square errors RMSEs. The CUSUM plots testing parameter stability and forecast accuracy indicate that the model specified in the present paper is a good one and that its forecasting performance is generally accurate.

Figure 8 provides forecasts of the commodity food price index lcfpi from 11 to The forecasted values are given within a forecasting interval of one RMSE on either side.


The forecasts indicate that commodity food prices are expected to remain above their historical trend levels at least until late A considerable number of empirical studies identify and analyze the possible causes of recent food and agricultural commodity spikes.

Several of these factors are commonly identified as being responsible for the price shift, while other potential explanations are controversially discussed or even negated. Moreover, the literature argues about the econometric approaches adopted for the investigation of how these factors affect food and agricultural commodity price formation.

These different positions are often difficult to reconcile and perhaps should be interpreted in order to understand common global trends in a series of food crises that are not the result of a single casual event but rather the consequence of a momentous combination of distinct but strongly interrelated factors.

This paper uses the structural time series approach to analyze movements in the monthly commodity food price index for the period 11 — The price series is decomposed into components such as trend, seasonal, cycle, interventions dummies , and irregular.

Then, forecasts are obtained for each month of the period The empirical results indicate that the price series is best described by a fixed level; fixed seasonal; two stochastic cycles; two explanatory variables, that is, crude oil and the US real effective exchange rate; and several intervention dummies, that is, structural breaks and outliers.

Both cycles show a high degree of persistence but they dissipate in the long run. The longer cycle shows a periodicity of two years, and the more intense cyclical activity takes place after , which is consistent with the literature and indicates that the consequences of that factors affecting commodity food prices began to appear after The effect of the explanatory variables on commodity food prices is the expected one and this finding is supported by the literature.

In particular, crude oil has a positive effect, while the US real effective exchange rate has a negative effect. The structural break dummies capture the trend component of the price series adequately, while the outliers capture some temporal effects, that is, short-lived spikes, well. The structural break dummies generated by the estimation process also coincide with the years that are most extensively discussed by the literature analyzing movements in commodity food prices.

Finally, the model presented in the present paper provides monthly forecasted values of the commodity food price series from 11 to The forecast shows high and volatile commodity food prices for the medium term, that is, the next two years. Some short- and long-term strategies for coping with food price volatility in ACP counties are stabilizing food prices, setting up emerging food stocks at a regional level, facilitating the exchange of agricultural data, protecting the most vulnerable populations, and raising smallholder productivity [45].

Andrew Elliott, N. James Knackstedt, M. Joseph Morgan, M. Pamela Wible, M. Steven Baker In this article, an extensive review of the rapidly growing biofuel-related time-series literature is carried out. The data used, the modeling techniques and the main findings of this literature are discussed.

Providing a review of this flourishing research area is relevant as a guidepost for future research. This literature concludes that energy prices drive long-run agricultural price levels and that instability in energy markets is transferred to food markets. We complement the literature review with a detailed analysis. We offer courses in fiction, poetry, creative nonfiction, and playwriting to students at both the undergraduate and graduate levels.

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The Love Comes Softly Collection: Eight Novels in One by Janette Oke.

Love Comes Softly

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