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Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer? for CAT 2024 is part of CAT preparation. The Question and answers have been prepared according to the CAT exam syllabus. Information about Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer? covers all topics & solutions for CAT 2024 Exam. Find important definitions, questions, meanings, examples, exercises and tests below for Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer?.

Solutions for Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer? in English & in Hindi are available as part of our courses for CAT. Download more important topics, notes, lectures and mock test series for CAT Exam by signing up for free.

Here you can find the meaning of Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer? defined & explained in the simplest way possible. Besides giving the explanation of Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer?, a detailed solution for Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer? has been provided alongside types of Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer? theory, EduRev gives you an ample number of questions to practice Group QuestionRead 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. 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. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers records.Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. 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. 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. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessaryto make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMFs forecasts by the organisations Independent Evaluation Office concluded that their accuracy was comparable to that of private-sector forecasts. However, the accuracy of forecasts are always under speculation.Q. Which of the following is a shortcoming of a data-based model?a)Insurmountable statistical issuesb)Lack of theoretical disciplinec)Complex structure of cause and effectd)Adverse influences on growthCorrect answer is option 'A'. Can you explain this answer? tests, examples and also practice CAT tests.