_{1}

^{*}

This article reviews the seminal econometric^{1} models published by maritime economists between 1934 and 2012, indicating the main mistakes committed during this period, so that future research can avoid them. The errors were spurious regression, identification and the false assumption that maritime markets are sufficiently **efficient, **if co-integrated. Three further mistakes are noted: the belief that a shipping firm is its vessel; the assumption that a random walk is appropriate for modeling tanker markets, and the assumption that shipping markets are random and linear. Koopmans was correct (in 1939) in stating that the discrepancy **(surplus/deficit)** between supply and demand for ship space determines freight rates, something that passed unnoticed until recently. The papers reviewed cast in four centers -on the basis of the academic domicile of their authors: 1) the pioneering Dutch Center, 1934-1939, 2) the Zannetos Model, 1966, 3) the Norwegian Center, 1976-2012, and 4) the Beenstock-Vergottis Model, 1985-1993 and English Center, 1987- 2002. Unfortunately, each new shipping model rejected almost all previous ones and consequently the research did not build a clear picture. Moreover, maritime markets are not **perfectly competitive, and **most maritime economic concepts need a re-definition, including: **shortrun**, shipowners **expectations** and **marginal cost**.

Models are important because they attempt to represent reality. A model is a simplified representation of actual phenomena, and has three specific purposes: to explain, to predict and to control reality. In econometric language, this triptych entails three targets: 1) a structural^{2} analysis, 2) a forecasting and 3) policy evaluation.

There are four main types of model (

The first type was used by Adam Smith (1776) [

Historically, freight markets were a popular subject of shipping economics and gained priority in shipping econometrics (e.g. Koopmans, (1939) [

Two economists, who started shipping economics, became Nobel laureates in economics: Jan Tinbergen in 1969 and Tjalling Koopmans in 1975. Three early shipping economics books influenced theory: Koopmans’ [

Shipping is an international industry, and as such attracted the close attention of international regulatory bodies: the United Nations (UN) and its specialized agencies: the International Maritime Organization (IMO), the International labor office (ILO); also the Organization of economic cooperation and development and European Union (EU) among others. It is an over-regulated industry. These bodies focused on legal, socio-managerial as well technical issues: safety, security, sea pollution, climate change, and piracy. In Talley (2012) [

A flood of papers appeared eventually on port economics as time passed-by. Port economics gained an equal place in published papers alongside shipping economics and shipbuilding^{3}. A popular new subject also emerged: Maritime Logistics^{4}―ML.

Shipping econometricians were slow to adopt available econometric methods. Most applications emerged from doctoral dissertations (Zannetos, (1966) [

Econometrics, like economics, first dealt with economic epidemics, or “business-cycles”. The most painful cycles were the “Great Depression” of 1929 to 1937 and the Global Financial Crisis followed by the end of 2008 to 2016. There have been three major depressions in shipping since the Great Depression: 1) in 1973, with a crisis in tankers, as a result of the first oil crisis, and in 1979, after the second energy crisis, 2) in 1981 to 1987, a dry cargo depression, and 3) from 2008 to 2016 due to the banking crisis in USA, affecting both dry and liquid cargoes.

Shipping, with its frequent cycles, (Stopford, (2009) [

The paper surveys of the econometric models applied to shipping economics, between 1934 and 2012, without repeating material from our two other papers which covered the period 1996 to 2016 [

The paper is organized in seven parts. Part 2, describes the evolution of econometrics; Part 3 presents the models of the Dutch Center, 1934-1965. Part 4, presents Zannetos’ Model, 1966. Part 5, presents the models of the Norwegian Center, 1976-1985. Part 6, analyses the Beenstock and Vergottis Model, 1985-1995, and the models of the English Center, 1986-1995; 2002. Part 7, provides a critique of the models presented, and Part 8, suggests further research.

The most popular econometric tool is “linear regression”, invented by Francis Galton in 1885 [

In rigorous terms, a linear regression is denoted as:

y t = α + β x t + u t (1)

where the error term u_{t}, is assumed to be identically and independently distributed round its mean, with 0 mean and variance equal to σ^{2}. The exact form of any regression, (i.e. estimating the parameters α and β) is determined mathematically by the method of least squares, devised by both Legendre in 1805 and Gauss in 1809^{5}.

Frisch (1938) [

The work done by the Cowles Commission was an intellectual success, (but an) empirical failure (Heckman, (2000) [

In 1958, econometrics abandoned structural models, i.e.

y = α + β x (2)

with exogenous^{6} x and explanatory variable(s) fixed over repeated samples (Brooks, (2014) [^{7} variable, then Equation (2) can be written:

y t = α + β x t (3)

where t = 1 , ⋯ , n (i.e. a linear static function applicable to time series).

More specifically, in “structural equation modelling” (SEM), each endogenous variable has its own equation. Econometricians considered Equation (3) insufficient, and added a (stochastic) error term^{8}, u_{t}, turning it into a statistical model.

A causal relationship was first considered as coming from x' variables to y. But, causality^{9} can also be from y to x'. The variables may thus be interrelated in a system of linear simultaneous equations. An endogenous variable can also appear on the right hand side of an equation creating the simultaneity problem. This means that if a (stochastic) price and quantity appear in two equations, it is impossible to estimate them validly using ordinary least squares, and maintaining that x and u are independent, and errors uncorrelated with explanatory variables [

SEM, however, was popular in published papers, and books, until 1970, accounting for 20% in 1951, rising to 48% in 1959-1966 and falling back a little to 33% in 1968-1970 [

There is also a feedback mechanism operating between many economic variables. Economic data should then be described as a system of (simultaneous) relations. A SEM is then properly used to describe relationships among random economic variables.

The property of randomness in a variable, or time series, should be tested (e.g. by the Jarque-Bera test), and when tested the results should be taken into account. A diagrammatic test of randomness is presented in ^{11}”. It shows the first logarithmic differences between a random variable (e.g. Baltic Panamax Index-BPI) (red line) and its actual representation (blue line).

As shown, the supposedly random curve of daily BPI for 1999-2012 (blue line) departs from random (red line).

Haavelmo (1943) [

SEM was then transformed into a reduced form, which is more useful for forecasting. The main method adopted was the maximum likelihood, which became the limited information maximum likelihood model, with associated and appropriate computing methods (Epstein, (1987), [

Three movements occurred during late 1970s in econometrics: 1) the “rational expectations” move in macroeconomics; 2) VAR^{12} (Sims, (1980) [

Rational expectations are based on the principle that people make, on average, correct guesses about future, using all available and relevant information. In order to have a perfect foresight, information must be complete, and the uncertainty zero… Models that are related to VAR are: ARMA^{13}, VECM^{14} and VMA^{15}.

Dynamic models include lagged or differenced terms of the dependent or independent variables. A new methods to estimate these models was the two stages least squares developed by Theil (1953) [^{16}. IV are correlated with the variables they replace, but not with error term. SUR applied to shipping by Kavussanos M. SUR is suitable for models with movements of several highly related dependent variables, using a time series regression, and at the same time, allowing correlation in error terms.

Shipping economists used non-stationary variables in their models. Using such methods one can easily fit regressions using levels of co-trending, but unrelated variables, and find reasonable goodness of fit, and heavy serial correlation, even though this correlation is spurious (Phillips, 1986) [

A regression involving two or more independent non-stationary variables, where the estimates of the slopes appear highly significant in standard statistical tests, can have highly significant t-ratios, even though in reality there is no relationship. This restriction to the method was first considered in the 1970s, and appeared in shipping models 10 years later. Because of this phenomenon, the conclusions of any paper published before 1990 must be considered invalid. The time series involved did not have constant means, and included a variance and an auto-covariance.

After 1986-1987, econometricians used methods to analyze time series with many co-integrated variables/equations, (i.e. variables showing a fixed relationship in the long-run), with error corrected (i.e. variables that were stationary/1st differenced―which were combined with a term capturing movements back to their long-run equilibrium) (Engle & Granger, (1987), [

Maritime economists adopted these innovative approaches roughly six years after their first appearance. Many maritime economists incorporated assumptions into their models, including the idea that markets were efficient and that people (traders) have rational expectations. In combination, these suggest that asset prices, (or their natural logarithms), follow a random walk (with or without drift) (with unpredictable differences).

Kavussanos and Alizadeh-M (2002) [^{17}, condition for an efficient market. They used a generalized conditional heteroscedastic model (GARCH-M), previously developed by Campbell and Shiller, (1987) [

They assumed that time charters (of 12 months) should reflect the weighted average of the expected monthly (spot) freight contracts over the next 12 months, if they were efficient. They applied the expectations hypothesis of term structure (EHTS). Their results did not support the hypothesis because returns were excessively volatile. This implies that shipowners prefer to be in less volatile markets, but between 2003 and 2008 shipowners preferred to be in the spot market with very high profits, extreme volatility, and more risk.

Kavussanos and Alizadeh-M [

Certain models presuppose that investors should be compensated for taking more risk by getting a higher return. We argue that in shipping when seeking profit risk is ignored. Engle et al. (1987) [^{18} was more popular, and replaced

ARCH-M = Y t = m + d V t − 1 + u t (4)

If d > 0 (then a risk premium exists), and if it is statistically significant, then increased risk leads to a rise in the mean return.

Goulielmos (2009) [

In econometrics, the sequence of the events was: 1) the “autoregressive model” (AR^{19}), which replaced the multivariate and inappropriate structural model. 2) A class of time series models were introduced: the “AR integrated moving average” (=ARIMA^{20}), associated with Box and Jenkins (1976) [

The earliest shipping models produced by Dutch Center were due to Tinbergen^{21} and Koopmans^{22}, who also played an important role in the development of Econometrics (Qin, (2013) [

Tinbergen (1934; 1959) [

S = K a P − b F g (5)

where P = the price of fuel (coal) -other costs assumed constant- and K = the tonnage level. The equilibrium condition is that demand equals supply.

Solving (5) at equilibrium for freight rate gives a linear regression:

F = e 1 D − e 2 K + e 3 P (6)

where e_{1} = 1/g, e_{2} = a/g and e_{3} = b/g. This states that the freight rate is determined, at equilibrium, by demand, D for tonnage, where K = level of existing tonnage (i.e. supply) and P = cost of fuel.

Equation (6) is a rudimentary timeless (no lags) model that omits various factors. The expression of supply and demand in ton-miles indicates that Tinbergen understood the importance of distance and speed. Dynamic models, i.e. with lags, appeared a few decades later. This is a shortrun model depending on laid up tonnage, which is a shortrun variable, reacting on changes in demand in one to three months. Demand for extra tonnage reacts first, and supply reacts in a second cycle by new constructions (in the long run). New construction is restricted by construction time (one to two years).

Economists generally use a catch all phrase for omitted factors, customarily assuming that do not change―others things being equal, the ceteris paribus assumption. Fuel cost may have exerted a direct influence on supply, meaning that it was included twice. The elasticity of supply and demand curves, at equilibrium, is essential in setting the freight rate, and should be included. Tinbergen argued that the freight rate reverts^{23} to its average.

The concept of equilibrium (D = S) used by shipping economists, first emerged in Physics in the work of Canard in 1801 (Schweitzer (ed.), (2002), [

Koopmans (1939) [

D = K ( F / P ) g (7)

where P = costs (all costs). Supply is proportional to fleet size (K) and to the ratio of freight rate to costs. Coefficient g was found to be 0.15, indicating a strong influence of laid-up tonnage on elasticity of supply. Koopmans also showed the effect of laid-up tonnage on freight rates accounting for short-run ups and downs. He also, related ship values to freight rates for tankers and linked the freight market with shipbuilding and scrapping. Koopmans' model is presented diagrammatically in

_{1-3}. The supply curve, (expressed in ton miles), is initially (on left) perfectly elastic (part sf), because the available tonnage is not hired and some ships are laid-up. If the demand curve intersects this part of the supply curve, the impact on equilibrium freight rates will be negligible.

If demand increases, as shown by the shift of demand curves to the right, and intersects the right hand part of the supply curve, the fleet is fully employed. Increases in demand gradually draw into use all sea-worthy tonnage. The full impact of increased demand, in the shortrun, passes entirely to freight rates, no more tonnage is available (not even from shipbuilding). Certain factors in the ceteris paribus cupboard, like: average speed of ships, ships lost, ships scrapped and ships converted, are assumed to have exerted all their influence on supply and to remain constant at equilibrium.

The construction of additional ships requires time, which, by definition, is beyond the shortrun. Equilibrium is delayed and demand outstrips supply until supply responds. The time taken to reach equilibrium varies. There are waiting and construction times for new buildings, also variable, depending on whether a boom or a depression is on the way. The time span may be from a few months to a few years.

The various factors mentioned above show that a simple two-variable model―like those of Koopmans and Tinbergen―left out a number of factors. Tinbergen and Koopmans indirectly incorporated sea distances, scrapping, and laid-up tonnage. Sea distances and lay-up tonnage play an important role, but the impact the speed of ships in low steaming is completely ignored.

In _{1}F_{2} < F_{2}F_{3}. This explains why there are large fluctuations in freight rates (Evans (1994) [

A valid hypothesis made by Koopmans [^{24} for the surplus of supply over demand. The correlation coefficient was high (=0.84) between surpluses in demand and supply, and time charters (1976-1988).

Zannetos’ model is presented in

Mobility of capital is possible for Zannetos, because there are organized second hand and scrap markets. These markets stimulate competition and clear the freight market so that supply can equal demand. Successful shipping companies buy tonnage owned by bankrupt companies. But the capital loss that the first owner suffers―during a depression―is substantial. This was the case for the “very large and ultra large” tankers (VLCC; ULCC) involved in the two energy crises of 1973 and 1975, and for the more recent Hadjin bankrupt-case.

Zannetos was dissatisfied with the traditional static economic analysis, which, as Hicks noted, ignored time, and was unable to explain how tanker freight rates are formed. He was puzzled because freight rates and demand for tanker services were cyclical, while demand for oil was not...

Zannetos found his theoretical framework in Hicks’ theory of expectations. His model is a product of his PhD thesis (1956-1959) at Massachusetts Institute of Technology. His book, based on it, was a continuation of the work of Koopmans (Zannetos, (1987) [

Zannetos argued that a substantial rate movement away from equilibrium creates expectations. More recent theory provides not one, but three main theories for expectations: adaptive, rational and behavioral. Because people anticipate the future and act on those assumptions, future freight rates will be proportionally greater or less than the current level. If this assumption is valid―which should be tested econometrically, using data provided by shipowners―then almost all Zannetos’ theory follows. This could explain the cyclical behavior of markets, as expectations work as a stop-go mechanism. The elasticity of expectations is unity, and it is this elasticity that creates market instability. Moreover, new-building prices also move the system into market instability (Zannetos [

Georgantzi (2005) [

Zannetos also recognized the term structure of freight rates in the long-run (Veenstra and De La Fosse, (2006) [

_{1}, R_{2}, R_{3}, R_{4}, R_{5} and R_{s}, between short and long run rates, whenever supply equals demand and when slopes are right for stability. Partial equilibria are also possible (Marshall, (1920) [_{2} (unstable) and R_{3} (stable from below). R_{4} is unstable, and R_{5} is stable from above. Zannetos studied situations away^{25} from equilibrium, where there is a dynamic process moving towards equilibrium.

Zannetos argued that freight rates create a non-symmetrical behavior between buyers and sellers of ship space that is regular, non-uniform, and creates

alternating expectations. Buyers and sellers have no memory. Sellers react either immediately, or with a delay.

Q = f ( F ) (8)

where F stands for spot rates. Freight rates have long troughs and brief sudden peaks that can be simulated by an adapted cobweb model (Tvedt, (2003) [

However, the above statement cannot be taken as always true, as for example, the 2003 to 2008 boom was not at all short. Zannetos assumed that expectations alternate between elastic and inelastic, if the market stays long enough at equilibrium. He made the peculiar statement that the memory of the market operators may not be long enough to recall how the market came to rest…

Zannetos discussed the pattern of ownership of the world tanker fleet meaning how much tonnage is (or should be) owned by the oil majors. He gave no consistent explanation of why oil majors kept a certain percentage, which was about 35% of the world fleet (1950-1975), rising to about 40% (1976-1985), and then falling to about 20% (1986-2000) (Veenstra and De La Fosse, (2006) [

We believe that the oil majors needed a specific percentage of ownership of the tanker fleet to control the market in an oligo-psonistic manner (1950-1985). Also, oil-majors induced independent owners to build new ships by negotiating with them long term (15 to 20 years) contracts at freight rates lower than the cost of their own ships (Goulielmos, (2013) [

This policy, however, to increase existing supply (and removing a potential demand from the market), and in the end to lowering the freight rates to be paid by the oil majors, was brilliant. Oil majors have bureaucratic boards of directors. Some argue that the spot freight rate was the yardstick in negotiations. We believe that the oil majors’ operating costs were the yardstick. Independents could not easily refuse a long term contract provided by a first class charterer that could be used in a bank to obtain finance for between 60% and 80% to build the ship involved. Independents relied on profit from their lower operating costs and economies of scale building larger ships than had previously in the market (e.g. Onassis, Niarchos).

Strandenes (2012) [

Glen and Martin (1998) [^{26} charter as a proxy.

Eriksen and Norman (1976) [

Wergeland (1981) [

supply ( inton-miles ) as = T d F − e (9)

where T is world’s bulk trade, and e is assumed positive; d is roughly unity. Wergeland used the methods: two stages least squares, full information maximum likelihood and a log-linear approach. He proved that the existence of laid-up tonnage determines elasticity of supply, something argued also by Koopmans [

Strandenes (1981) [

Strandenes (1984) [

T C = r ( t ) [ a p + b p * ] (10)

where TC = time charter, valid today with duration t; p = current time charter equivalent in the short-term, p^{*} = expected long term time charter equivalent and r(t) = risk premium in the short and long run. She worked on bulk carriers, medium sized tankers and large tankers, including expectations. If r(t) < 1, owners accept a lower p, if a time charter is safer than a (riskier) spot market. A drop in fuel cost, significantly and permanently, reduces freight rates by increasing distances, and S, given the existence of “combined carriers”^{27}.

Profitability depends on elasticity of demand and fuel costs. Inperfectly competitive markets, where demand equals supply, then

T = P ( r + d ) − O C (11)

where r = the return on capital, T = long term TCE (time charter equivalent), OC = long term operating costs; and P = the new-building price; OLS are used. She estimated a set of parameters for the term structure of freight rates. Beenstock (1985) [

Strandenes (1986) [

This means that in order for a market to be efficient, all relevant information has to have been included in the price. But the hypothesis that the market is efficient has been disputed for some time, especially after 1987 (Black Monday). Strandenes (1990) [

This period is characterized by the work of Beenstock and Vergottis (1989 a, b) [^{28}.

Maritime economists at this time were heavily preoccupied with the cross effects of spot freight rates between tankers and dry bulks, due to the appearance of combination carriers. These ships, initially called OBO (oil-bulk-ore), of a total of 49m dwt globally, had handsome profits between 1967 and 1970 and in 1973. Stopford (2009) [

Charemza and Gronicki (1981) [^{29}. They created a linear bulk market model with freight rates in disequilibrium, supply and demand taken at their minima, and freight rates changed proportionally to excess demand. Their econometric models were separate for dry bulk and tankers, where ship values made it possible to react to changes in freight rates. Beenstock (1985) [

Hale and Vanags (1989) [^{30}.

Hale and Vanags (1992) [

Campbell and Shiller (1991) [

He found no clear grounds on which to reject them. So, ship-owners may well employ both rational and non-rational expectations, depending on market conditions. He said that the key factor is the level of confidence in rational expectations. He examined the nature of expectations in the market for secondhand ships, but there were no obvious grounds for distinguishing between them.

Beenstock and Vergottis (1993) [

R = F ∗ s (12)

where R is the freight revenue per unit of time period, F in $ per-ton mile, and s = speed in milesper unit time period.

b = s α (13)

where b = bunker (fuel) consumption in tons and α is a technology constant coefficient, assumed to be greater than unity (=decreasing returns to scale). A more interesting case that has been totally neglected by economists is where there are increasing returns to scale (Goulielmos, (2018), [

They argued [

Profits = Π = F s − P b − O C (14)

where P is the price of fuel, OC fixed costs (operating). Now

T C E = Π + O C = F s − P b (15)

and given that b is a function of speed (15) becomes

Π = F s − P s α − O C (16)

Profit maximization of the vessel requires:

d Π / d s = F − α P s α − 1 = 0 (17)

s = ( F / α P ) 1 / ( α − 1 ) = optimum speed (18)

To maximize profits, speed must vary with the ratio of freight rate to fuel price.

Equation (17) implies that maximum profit is related to freight rates and fuel prices. Following certain further steps―omitted here―they can determine total market supply. Next, demand is assumed exogenous and completely inelastic. For market equilibrium,

D = K ( F / α P ) 1 / ( α − 1 ) (19)

where K = carrying capacity in dwt of all ships. No data could be found on profits or expected profits.

They used 2SLS (equivalent to 3SLS), OLS, instrumental variables in place of 2SLS (where the full economic structural system of equations could be used), simulation, with an emphasis on rational expectations, 3SLS (better is the generalized method of moments-GMM, 1982, also in 3 stages), using optimization (maximizing profits), and perfect competition (in a structural model).

Expectations are formed in the most effective way through an information mechanism so that forecasting error is random and unforeseen. Rational expectations are the result of rational maximizing behavior in acquiring and processing information to form a view about the future. They also modeled the interdependencies between dry cargo and tanker markets and the market for ships and rational expectations.

Papadopoulos and Tamvakis (1994) [^{2} method, however, was found to be more appropriate.

Evans (1994) [

3 p k s 2 = M C (20)

where F = freight rate, MC = marginal cost, p = price of fuel with a constant k, s = optimum speed/miles per day (the time spent in ports is ignored), plus profit maximization. F = MC; MC = Marginal Revenue = Average Revenue. He argued that in the long-run supply does not equal demand. MC = the cost of producing for an additional ship-mile.

M C per ship mile = 3 p k s o 2 + 2 p k s o 3 t / d (21)

where s is the optimum speed and s * o is the voyage speed, k is a constant, d is distance, p is the price of fuel, and t is voyage time. If t = 0, revenue per ship mile = 3pks^{2}.

Glen (1991) [

Most of maritime papers reviewed above reached contradictory findings and ultimately were inconclusive. All shipping firms in the market assumed theoretically, at equilibrium, that their ships were ready to supply services in a process of profit maximization. But in practice a shipping company tries to negotiate, using in-house, or appointing brokers, with the brokers of charterers, for a freight rate using the market for previous similar and recent charters as a benchmark. Firms try to achieve this pre-determined freight rate in a process of constrained maximization. This conforms to a shortrun purely competitive market.

Moreover, shipping markets are assumed to clear instantaneously, which is not true. A shipping market may need years to clear, (e.g. the tanker market in the 1970s). McConville (1998; editorial) [

In Tinbergen’s model [^{2} with no intercept). No error term appeared; his equation was exact. Koopmans (1939) [

Zannetos (1966) [

Importantly, Zannetos argued that a random walk was not present in his empirical findings, but if it was, it would negate the theory of price elastic expectations... Zannetos argued that the probability that one positive change would be followed by another positive change is high, i.e. 61% (Lyridis and Zacharioudakis, (2012) [

Eriksen (1977) [

Owners order ships if trade increases, freight rates rise, and prices (of new buildings) fall. Also, the price of steel can be inserted into the regression of freight rates (both affecting new building prices). Orders affect deliveries (positively). Scrapping depends on expected future profits of old ships and on the size of the fleet (supply). Freight rates and demand affect (negatively) scrapping (if high). The fleet size is the supply, and the fleet grows when there are new deliveries and shrinks when vessels are scrapped.

Wergeland (1981) [

Hale and Vanags (1992) [

Three essential claims about market behavior derive from the above assumptions, and these need to be carefully evaluated: 1) the best estimate of tomorrow’s freight rate is today’s; 2) tomorrow’s freight rate is independent of past freight rates; 3) changes in freight rate vary in accordance with a normal distribution. The assumption of efficient markets implies that asset prices will rapidly reflect all relevant and available information though the exact speed required has not been specified.

A mistake committed by Thorburn (1960; p. 11) [^{31} shipping, the vessel forms the economic unit of analysis (i.e. the shipping company), while the shipping company per se plays a subordinate role. This approach, though wrong especially after 1960, was also adopted by Zannetos [

Metaxas (1971) [

Professor Heaver (1993) [

It is important to know whether elastic expectations really apply to shipping, and whether perfect competition occurs in the bulk and tanker industries (Goulielmos, (2013) [

Moreover, free-exit from the market is not as free as certain maritime economists believe (the Hajin case). It is also worth re-examining the concept of the shortrun in shipping. A shipping company almost always makes decisions in the shortrun as most decisions of a shipping firm to change the level of the firm’s capital, or number of ships need no more than one to two months.

Depressions had the magic to force all kinds of models, including econometric models to fail (Weatherall, (2013) [

The most crucial trigger for econometrics was the widespread failure of macro-econometric models to predict global recession after the 1973 oil crisis. Macro-econometric models also failed to predict the depression at the end of-2008 (Lucas and Sargent, (1979) [

A new feature has now arisen with the appearance of futures markets. It needs to be investigated whether the parties involved in a chartering market for dry cargos and tankers are substantially affected by futures forecasts. Above all it is important to understand the influence of future freight rates agreements (FFAs).

The concept of the shortrun in shipping needs to be re-examined, as mentioned. For the industry, shortrun is when new ships cannot be built. This time can be from one to four years. For the firm, shortrun is when additional ships cannot be bought. This time is from one to two months.

The author declares no conflicts of interest regarding the publication of this paper.

Goulielmos, A.M. (2019) A Brief History of Maritime Econometrics, 1934-2012. Modern Economy, 10, 730-756. https://doi.org/10.4236/me.2019.103050