In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Data-based models suffer from their own shortcomings. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. These try to anticipate the ups and downs of big economies by modelling the behaviour of individual households and firms.The empirical approach is older indeed, it was the workhorse of government forecasting in the 1940s and 1950s. Central banks and other big economic institutions use far more complicated formulas, often grouped under the bewildering label of “dynamic stochastic general equilibrium” (DSGE) models. It posits that poorer countries should generally invest more and grow faster than rich ones. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. The other is data-based, shaped by how economies have behaved in the past. One is theory-based, shaped by how economists believe economies behave. As predictions for 2016 are unveiled, it is worth assessing the soothsayers’ records.Forecasters usually rely on two different predictive approaches. Since economic output represents the aggregated activity of billions of people, influenced by forces seen and unseen, it is a wonder forecasters ever get it right. Read the passage given below and answer the questions that follow.“The only function of economic forecasting is to make astrology look respectable,” John Kenneth Galbraith, an irreverent economist, once said.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |