Several authors have tried to measure the "persistence" in U.S state unemployment rates
by running the following regression: $$u r _ { i , t } = \beta _ { 0 } + \beta _ { 1 } \times u r _ { i , t - k } + z _ { i , t }$$ where $u r$ is the state unemployment rate, $i$ is the index for the $i$-th state, $t$ indicates a time period, and typically $k \geq 10$. (a)Explain why finding a slope estimate of one and an intercept of zero is typically
interpreted as evidence of "persistence."

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The Answer of Several authors have tried to measure the "persistence" in U.S state unemployment rates
by running the following regression: $$u r _ { i , t } = \beta _ { 0 } + \beta _ { 1 } \times u r _ { i , t - k } + z _ { i , t }$$ where $u r$ is the state unemployment rate, $i$ is the index for the $i$-th state, $t$ indicates a time period, and typically $k \geq 10$. (a)Explain why finding a slope estimate of one and an intercept of zero is typically
interpreted as evidence of "persistence."

Several Authors Have Tried to Measure the "Persistence" in U uri,t=β0+β1×uri,t−k+zi,tu r _ { i , t } = \beta _ { 0 } + \beta _ { 1 } \times u r _ { i , t - k } + z _ { i , t }uri,t​=β0​+β1​×uri,t−k​+zi,t​

Several Authors Have Tried to Measure the "Persistence" in U $u r _ { i , t } = \beta _ { 0 } + \beta _ { 1 } \times u r _ { i , t - k } + z _ { i , t }$