مدل تعادل عمومی پویا با عدم قطعیت: عدم قطعیت در مورد مسیر آینده اقتصاد
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|28935||2013||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Economic Modelling, Volume 32, May 2013, Pages 429–439
This paper develops a new method for incorporating uncertainty within a computable general equilibrium (CGE) model. The method involves incorporating uncertainty into the model by formulating different states of the world or paths that the economy may take. The risk then is that on one or more of the paths, there may be an external demand shock, for example, an exogenous shock in tourism demand. The multi-sector forward-looking CGE model with risk shows the impact of uncertainty on the economy and how households and industry respond to the presence of uncertainty. The results show that, where there is an asymmetric shock, the possibility of a future tourism demand shock creates a welfare loss. The welfare gains along the non-shocked path are a result of household's risk aversion and their substituting resources away from the shocked path. The difference in the monetary values of the welfare on the different paths can be interpreted as the ‘price’ of the risk. It is the price households would pay to remove the possibility of the tourism shock. Therefore, this research was able to quantify the monetary value of the risk. This method can be used in scenario modelling for other adverse contingent events, such as the uncertainty of climate change impacts, and agriculture production risks.
The concept of risk has been examined from many different disciplines: from an economic perspective (Anscombe and Aumann, 1963, Arrow, 1965, Kahneman and Tversky, 1979, Pratt, 1964, Rothschild and Stiglitz, 1970, Rothschild and Stiglitz, 1971 and von Neumann and Morgenstern, 1944), from a sociological perspective (Finucane and Holup, 2005, Slovic, 1986, Slovic, 1987 and Slovic et al., 1985), from a financial perspective (Bluhm et al., 2002) and from a technical perspective (Kammen & Hassenzahl, 1962). Risk is a complex construct. Risk has been defined in many different ways. One frequently cited definition of risk is that of Knight (1921). He defines risk as “measurable uncertainty”. Denenberg et al. (1974) simply define risk as “uncertainty of loss”. There can be many types of losses as well. Denenberg et al. take a very narrow view of risk defining loss as the loss of wealth or profit. Loss could be a loss of satisfaction/happiness or utility as in the economic meaning of utility. Thus, a loss of utility could involve a financial loss or may involve dissatisfaction or simply just the loss of happiness. This can be measured as a loss in economic welfare. The CGE class of models is empirically estimated by Arrow and Debreu (1954) using general equilibrium models with empirical data. CGE models were developed in the early 1960s to solve for both market prices and quantities simultaneously, thus simulating the working of a competitive market economy. A CGE model attempts to model the whole economy and the relationships between the economic agents in it. The model solves for a set of prices, including production prices, factor prices, and exchange rate and levels of production that clear all markets. The result is that, following the neoclassical assumption, producers maximise profits, which are the difference between revenue earned and the cost of factors and intermediate inputs. Commodity market demands depend on all prices and satisfy Walras's law. That is, at any set of prices, the total value of consumer expenditures equals consumer incomes. Technology is described by constant returns to scale production functions. Producers maximise profits. The zero homogeneity of demand functions and the linear homogeneity of profits in prices (i.e. doubling all prices double money profits) imply that only relative prices are of any significance in such a model. The absolute price level has no impact on the equilibrium outcome (Rutherford & Paltsev, 1999). In conventional forward-looking dynamic CGE models, economic agents are endowed with perfect foresight, so both consumers and firms anticipate any exogenous shocks and adjust their maximising behaviour from the first time period onwards. Perfect foresight then would appear to negate any uncertain response to a shock. Taking a simple model with Ramsey economic growth dynamics, this paper illustrates a frame work that incorporates uncertainty by allowing alternative future time paths resulting in uncertainty in the model. When an adverse shock occurs on one of the paths, this uncertainty is realised as a risk. The next section outlines the way risk is treated in standard CGE model, whether they are comparative static modes, or dynamic model (both dynamic recursive or forward-looking). Section 3 assesses previous research that has attempted to incorporate risk and uncertainty into CGE models. Section 4 in this paper conceptually describes the explicit treatment of risk in a CGE model involving the creation of multiple future paths for the model, where agents are able to predict each path and make decisions, given an element of risk aversion, in the presence of this uncertainty. Section 5 takes the conceptualisation of the uncertainty explained in Section 4 and applies it to a stylised benchmark economy to show the impact of the uncertainty on the economy. The implications the uncertainty has for the behaviour of the different economic agents (households, tourists, government, and industry) are highlighted. Section 6 concludes and suggests areas for further research using this uncertainty framework.
نتیجه گیری انگلیسی
CGE models have traditionally tended to be deterministic in nature. In this paper, a way to model risk and uncertainty in a CGE model has been demonstrated. The risk is incorporated into the model through the introduction of uncertainty regarding the future path of the economy. The probability and timing of a shock to the model economy is the underlying source of uncertainty. Various scenarios have been simulated. For example, one what-if scenario was to model a 50% probability of 10% negative tourism demand shock from time period t = 9 until the end of the model horizon along with a 50% probability that the economy would continue along its usual growth path. In this scenario, the expected value of welfare decreases by $US 1537.7 million or 0.48%. If the risk is realised and shock occurs, welfare decreases by $US 3713.5 million or 1.26%. If the economy were to follow the non-shocked path, households would receive $US 372.4 million in welfare or 0.13%, above the baseline. The welfare gains along the non-shocked path are a result of household's risk aversion and their substituting resources away from the shocked path. The difference in the monetary values of the welfare on either path can be interpreted as the ‘price’ of the risk. It is the price households would be willing to pay to guarantee no tourism shock. Another scenario was to model a 50% probability of 10% negative tourism demand shock from the time period t = 9 until the end of the model horizon in conjunction with a 50% probability that the economy would experience a positive tourism demand shock from the same point in time. The expected value of welfare was marginally positive meaning the welfare gain from the tourism boom is greater than the welfare loss from the tourism bust. This value can also be interpreted as the cost of imperfect information. There are more avenues that could be explored using this type of analysis. The analysis of tourism demand is only one area where an exogenous shock can be modelled with risk due to external factors such as global political and health situations. The area of agricultural economics lends itself to this type of analysis. For example, the introduction of cash crops in an economy reduces poverty and is generally seen as welfare enhancing but, due to the vagaries of climate and weather, agriculture can be a riskier activity than other sectors. Noting the inherent uncertainty in the weather and the implications it has for the agricultural sector, leads questions of how this type of modelling might be used to model climate change, where uncertainties about future impacts of climate change can be included in a model to show the effects of this uncertainty. Another interesting branch of research could be to investigate what other policy actions the government might to do, if anything, to decrease the amount of uncertainty in order to increase long term growth in the economy. This paper makes original contributions in the literature both methodologically and notes policy implications as a result of the inclusion of risk in the model. The research investigates the economic impact of uncertain tourism demand. The method used in this paper evaluates the influence of unanticipated shocks through the uncertainty of the future path of the economy. This imperfect information results in a market distortion. As such, there may be a suboptimal level of tourism production and a welfare loss. One policy implication for government as a result of this imperfect information could be the use of tourism taxes or credits to offset the loss of income due to the uncertainty of future tourism demand. The imposition of an additionally tourism tax would generate tax revenues, which would eventually be distributed back to residential households by government. Hence, the revenue generated by the additional taxes would need to be as great as the income lost as a result of the tourism bust — an example of the theory of second best.